Bio-Mimic Optimization Strategies in Wireless Sensor Networks: A Survey
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paper_title: An Intelligent Energy Efficient Clustering in Wireless Sensor Networks

paper_content:

 Abstract— One of the main challenges of wireless sensor network is how to improve its life time. The limited energy of nodes is the main obstacle. We may overcome this problem by optimizing the nodes' power consumption. A solution is clustering, but optimum clustering of wireless sensor network is an NP-Hard problem. This paper proposes a hybrid algorithm based on Genetic Algorithm and Particle Swarm Optimization to overcome this clustering problem by finding the number of clusters, the cluster heads and the clusters members. Simulation results reveal that this algorithm outperforms LEACH and Genetic Algorithm based clustering scheme.
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paper_title: Prolonging lifetime of wireless sensor networks with mobile base station using particle swarm optimization

paper_content:

Wireless sensor networks are a family of networks in wireless communication system and have the potential to become significant subsystem of engineering applications. In view of the fact that the sensor nodes in wireless sensor networks are typically irreplaceable, this type of network should operate with minimum possible energy in order to improve overall energy efficiency. Therefore, the protocols and algorithms developed for sensor networks must incorporate energy consumption as the highest priority optimization goal. Since the base station in sensor networks is usually a node with high processing power, high storage capacity and the battery used can be rechargeable, the base station can be utilized to collect data from each sensor node in the sensing area by moving closer to the transmitting node. In this paper, we proposed an energy-efficient protocol for the movement of mobile base station using particle swarm optimization (PSO) method in wireless sensor networks. Simulation results demonstrate that the proposed protocol can improve the network lifetime, data delivery and energy consumption compared to existing energy-efficient protocols developed for this network.
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paper_title: Particle Swarm Optimization in Wireless-Sensor Networks: A Brief Survey

paper_content:

Wireless-sensor networks (WSNs) are networks of autonomous nodes used for monitoring an environment. Developers of WSNs face challenges that arise from communication link failures, memory and computational constraints, and limited energy. Many issues in WSNs are formulated as multidimensional optimization problems, and approached through bioinspired techniques. Particle swarm optimization (PSO) is a simple, effective, and computationally efficient optimization algorithm. It has been applied to address WSN issues such as optimal deployment, node localization, clustering, and data aggregation. This paper outlines issues in WSNs, introduces PSO, and discusses its suitability for WSN applications. It also presents a brief survey of how PSO is tailored to address these issues.
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paper_title: Energy-Aware Clustering for Wireless Sensor Networks using Particle Swarm Optimization

paper_content:

Wireless sensor networks (WSNs) are mainly characterized by their limited and non-replenishable energy supply. Hence, the need for energy efficient infrastructure is becoming increasingly more important since it impacts upon the network operational lifetime. Sensor node clustering is one of the techniques that can expand the lifespan of the whole network through data aggregation at the cluster head. In this paper, we present an energy-aware clustering for wireless sensor networks using particle swarm optimization (PSO) algorithm which is implemented at the base station. We define a new cost function, with the objective of simultaneously minimizing the intra-cluster distance and optimizing the energy consumption of the network. The performance of our protocol is compared with the well known cluster-based protocol developed for WSNs, LEACH (low-energy adaptive clustering hierarchy) and LEACH-C, the later being an improved version of LEACH. Simulation results demonstrate that our proposed protocol can achieve better network lifetime and data delivery at the base station over its comparatives.
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paper_title: Computational Intelligence in Wireless Sensor Networks: A Survey

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Wireless sensor networks (WSNs) are networks of distributed autonomous devices that can sense or monitor physical or environmental conditions cooperatively. WSNs face many challenges, mainly caused by communication failures, storage and computational constraints and limited power supply. Paradigms of computational intelligence (CI) have been successfully used in recent years to address various challenges such as data aggregation and fusion, energy aware routing, task scheduling, security, optimal deployment and localization. CI provides adaptive mechanisms that exhibit intelligent behavior in complex and dynamic environments like WSNs. CI brings about flexibility, autonomous behavior, and robustness against topology changes, communication failures and scenario changes. However, WSN developers are usually not or not completely aware of the potential CI algorithms offer. On the other side, CI researchers are not familiar with all real problems and subtle requirements of WSNs. This mismatch makes collaboration and development difficult. This paper intends to close this gap and foster collaboration by offering a detailed introduction to WSNs and their properties. An extensive survey of CI applications to various problems in WSNs from various research areas and publication venues is presented in the paper. Besides, a discussion on advantages and disadvantages of CI algorithms over traditional WSN solutions is offered. In addition, a general evaluation of CI algorithms is presented, which will serve as a guide for using CI algorithms for WSNs.
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paper_title: Intelligent Optimization of Wireless Sensor Networks through Bio-Inspired Computing: Survey and Future Directions

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This survey article is a comprehensive discussion on Intelligent Optimization of Wireless Sensor Networks through Bio-Inspired Computing. The marvelous perfection of biological systems and its different aspects for optimized solutions for non-biological problems is presented here in detail. In the current research inclination, hiring of biological solutions to solve and optimize different aspects of artificial systems’ problems has been shaped into an important field with the name of bio-inspired computing. We have tabulated the exploitation of key constituents of biological system for developing bio-inspired systems to represent its importance and emergence in problem solving trends. We have presented how the metaphoric relationship is developed between the two biological and non-biological systems by quoting an example of relationship between prevailing wireless system and the natural system. Interdisciplinary research is playing a splendid contribution for various problems’ solving. The process of combining the individuals’ output to form a single problem solving solution is depicted in three-stage ensemble design. Also the hybrid solutions from computational intelligence-based optimization are elongated to demonstrate the emergent involvement of these inspired systems with rich references for the interested readers. It is concluded that these perfect creations have remedies for most of the problems in non-biological system.
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paper_title: Cluster-head identification in ad hoc sensor networks using particle swarm optimization

paper_content:

We propose a new application of the optimization technique known as particle swarm optimization (PSO) to the problem of clustering nodes. The PSO approach is an evolutionary programming technique where a 'swarm' of test solutions, analogous to a natural swarm of bees, ants or termites, is allowed to interact and cooperate to find the best solution to the given problem. In a typical optimization, some function or fitness is used as a criterion for the optimization. Here we use application specific criteria, where we are equalizing the number of nodes, and candidate cluster-heads in each cluster, with the objective of minimizing the energy expended by the nodes while maximizing the total data gathered. The objective criteria fit with the implementation of a wireless, ad hoc, sensor network with cluster-head routing and data aggregation.
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paper_title: An ant colony optimization approach for reputation and quality-of-service-based security in wireless sensor networks

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In wireless sensor networks (WSN), message security is an important concern. The protection of integrity and confidentiality of information and the protection from unauthorized access are important issues. However, due to factors such as resource limitations, absence of centralized access points, open wireless medium and small size of the sensor nodes, the implementation of security in WSN is a challenging task. ::: ::: ::: ::: In this paper, we propose a protocol, quality-based distance vector routing (QDV), for securing WSN using concepts based on Ant colony optimization ACO [1]. Two fundamental parameters—quality-of-service (QoS) and reputation [2]—are used. The high value of reputation of a node signifies that the node is trusted and is more reliable for data communication purposes. As a node shows signs of misbehavior, its reputation decreases, which, in turn, affects its quality-of-security QSec [2], thereby disabling the malicious nodes from gaining access to the network. By incorporating these two factors, we are able to distinguish the nodes present in the network. We, then, present a method to achieve “equilibrium” where the node is able to guarantee that its neighbors are secure. Copyright © 2008 John Wiley & Sons, Ltd.
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paper_title: A clustering algorithm of wireless sensor networks based on PSO

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The routing protocol of WSN based on clustering hierarchy has been one of the important research topics. An improved strategy based on LEACH (Low Energy Adaptive Clustering Hierarchy) is proposed in this paper, which uses the PSO algorithm to optimize clustering process by considering the energy, the communication costs, the load balance and other factors to determine the cluster-head node. The simulation results show that the improved algorithm can solve the problems in LEACH, such as uneven clustering, uneven load of the cluster-head node and other disadvantages, and it can prolong the WSN lifetime.
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paper_title: Computational Optimization: An Overview

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Computational optimization is ubiquitous in many applications in engineering and industry. In this chapter, we briefly introduce computational optimization, the optimization algorithms commonly used in practice, and the choice of an algorithm for a given problem. We introduce and analyze the main components of a typical optimization process, and discuss the challenges we may have to overcome in order to obtain optimal solutions correctly and efficiently. We also highlight some of the state-of-the-art developments in optimization and its diverse applications.
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paper_title: Introduction to Optimum Design

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Introduction to Optimum Design, Fourth Edition, carries on the tradition of the most widely used textbook in engineering optimization and optimum design courses. It is intended for use in a first course on engineering design and optimization at the undergraduate or graduate level in engineering departments of all disciplines, with a primary focus on mechanical, aerospace, and civil engineering courses. Through a basic and organized approach, the text describes engineering design optimization in a rigorous, yet simplified manner, illustrates various concepts and procedures with simple examples, and demonstrates their applicability to engineering design problems. Formulation of a design problem as an optimization problem is emphasized and illustrated throughout the text using Excel and MATLAB as learning and teaching aids. This fourth edition has been reorganized, rewritten in parts, and enhanced with new material, making the book even more appealing to instructors regardless of course level. * Includes basic concepts of optimality conditions and numerical methods that are described with simple and practical examples, making the material highly teachable and learnable* Presents applications of optimization methods for structural, mechanical, aerospace, and industrial engineering problems* Provides practical design examples that introduce students to the use of optimization methods early in the book* Contains chapter on several advanced optimum design topics that serve the needs of instructors who teach more advanced courses
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paper_title: Engineering Optimization: An Introduction with Metaheuristic Applications

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An accessible introduction to metaheuristics and optimization, featuring powerful and modern algorithms for application across engineering and the sciences From engineering and computer science to economics and management science, optimization is a core component for problem solving. Highlighting the latest developments that have evolved in recent years, Engineering Optimization: An Introduction with Metaheuristic Applications outlines popular metaheuristic algorithms and equips readers with the skills needed to apply these techniques to their own optimization problems. With insightful examples from various fields of study, the author highlights key concepts and techniques for the successful application of commonly-used metaheuristc algorithms, including simulated annealing, particle swarm optimization, harmony search, and genetic algorithms. The author introduces all major metaheuristic algorithms and their applications in optimization through a presentation that is organized into three succinct parts: Foundations of Optimization and Algorithms provides a brief introduction to the underlying nature of optimization and the common approaches to optimization problems, random number generation, the Monte Carlo method, and the Markov chain Monte Carlo method Metaheuristic Algorithms presents common metaheuristic algorithms in detail, including genetic algorithms, simulated annealing, ant algorithms, bee algorithms, particle swarm optimization, firefly algorithms, and harmony search Applications outlines a wide range of applications that use metaheuristic algorithms to solve challenging optimization problems with detailed implementation while also introducing various modifications used for multi-objective optimization Throughout the book, the author presents worked-out examples and real-world applications that illustrate the modern relevance of the topic. A detailed appendix features important and popular algorithms using MATLAB and Octave software packages, and a related FTP site houses MATLAB code and programs for easy implementation of the discussed techniques. In addition, references to the current literature enable readers to investigate individual algorithms and methods in greater detail. Engineering Optimization: An Introduction with Metaheuristic Applications is an excellent book for courses on optimization and computer simulation at the upper-undergraduate and graduate levels. It is also a valuable reference for researchers and practitioners working in the fields of mathematics, engineering, computer science, operations research, and management science who use metaheuristic algorithms to solve problems in their everyday work.
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paper_title: A conceptual comparison of the Cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms

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In this paper, the algorithmic concepts of the Cuckoo-search (CK), Particle swarm optimization (PSO), Differential evolution (DE) and Artificial bee colony (ABC) algorithms have been analyzed. The numerical optimization problem solving successes of the mentioned algorithms have also been compared statistically by testing over 50 different benchmark functions. Empirical results reveal that the problem solving success of the CK algorithm is very close to the DE algorithm. The run-time complexity and the required function-evaluation number for acquiring global minimizer by the DE algorithm is generally smaller than the comparison algorithms. The performances of the CK and PSO algorithms are statistically closer to the performance of the DE algorithm than the ABC algorithm. The CK and DE algorithms supply more robust and precise results than the PSO and ABC algorithms.
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paper_title: Computational optimization, modelling and simulation–a paradigm shift

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Abstract Computational optimization forms an integrated part of modern computational science. Any good design should intend to achieve certain optimality, though optimal solutions are often difficult to find in practice since uncertainty and nonlinearity always present in almost all real-world problems. As resources, time and money are always limited, optimization becomes even more important in practice. This workshop on Computational Optimization, Modelling and Simulation (COMS 2010) at ICCS 2010 will summarize the latest developments of optimization and modelling and their applications in science, engineering and industry
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paper_title: Hessian approximation algorithms for hybrid optimization methods

paper_content:

This article introduces Hessian approximation algorithms to estimate the search direction of the quasi-Newton methods for solving optimization problems of continuous parameters. The proposed algorithms are quite different from other well-known quasi-Newton methods, such as symmetric rank-one, Davidon–Fletcher–Powell, and Broyden–Fletcher–Goldfarb–Shanno, in that the Hessian matrix is not calculated from the gradient information, rather directly from the function values. The proposed algorithms are designed for a class of hybrid algorithms that combine evolutionary search with the gradient-based methods of quasi-Newton type. The function values calculated for the evolutionary search are used for estimation of the Hessian matrix (or its inverse) as well as the gradient vector. Since the estimation process of the Hessian matrix is independent of that of the gradient vector, more reliable Hessian estimation with a small population is possible compared with the previous methods based upon the classical quasi-N...
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paper_title: Comparison between genetic and gradient-based optimization algorithms for solving electromagnetics problems

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This paper compares the application of genetic algorithms and traditional gradient-based algorithms to various optimization problems in electromagnetics. Gradient algorithms work well for a small number of continuous parameters. Genetic algorithms are best for a large number of quantized parameters. Both antenna array and scattering optimization examples are shown. >
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paper_title: Sensitivity analysis, optimisation, and sampling methods applied to continuous models.

paper_content:

This report describes the initial stages of work for the Software Support for Metrology programme project Tools for Continuous Modelling and Simulation. The aims of the project are to improve the understanding and use of continuous modelling packages and to build the confidence of model users in the quality of their results. We introduce the study of sensitivity analysis, optimisation, and sampling methods applied to nite element models. A key problem affecting the application of such techniques to continuous models and black box software is how to gain the most accurate information on the performance and sensitivity of the modelled system using the least amount of computational effort. ::: Three small finite element models were developed that cover a range of physical problems, complexities and interactions between model parameters. The models are introduced and we explain the mathematical background to the methods that were applied to analyse the problems, and summarise our main conclusions and recommendations.
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paper_title: A simplex method for function minimization

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A method is described for the minimization of a function of n variables, which depends on the comparison of function values at the (n 41) vertices of a general simplex, followed by the replacement of the vertex with the highest value by another point. The simplex adapts itself to the local landscape, and contracts on to the final minimum. The method is shown to be effective and computationally compact. A procedure is given for the estimation of the Hessian matrix in the neighbourhood of the minimum, needed in statistical estimation problems.
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paper_title: Particle swarm optimization

paper_content:

A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed. The relationships between particle swarm optimization and both artificial life and genetic algorithms are described.
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paper_title: The Ant System: Optimization by a colony of cooperating agents

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An analogy with the way ant colonies function has suggested the definition of a new computational paradigm, which we call ant system (AS). We propose it as a viable new approach to stochastic combinatorial optimization. The main characteristics of this model are positive feedback, distributed computation, and the use of a constructive greedy heuristic. Positive feedback accounts for rapid discovery of good solutions, distributed computation avoids premature convergence, and the greedy heuristic helps find acceptable solutions in the early stages of the search process. We apply the proposed methodology to the classical traveling salesman problem (TSP), and report simulation results. We also discuss parameter selection and the early setups of the model, and compare it with tabu search and simulated annealing using TSP. To demonstrate the robustness of the approach, we show how the ant system (AS) can be applied to other optimization problems like the asymmetric traveling salesman, the quadratic assignment and the job-shop scheduling. Finally we discuss the salient characteristics-global data structure revision, distributed communication and probabilistic transitions of the AS.
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paper_title: QoS Challenges and Opportunities in Wireless Sensor/Actuator Networks

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A wireless sensor/actuator network (WSAN) is a group of sensors and actuators that are geographically distributed and interconnected by wireless networks. Sensors gather information about the state of physical world. Actuators react to this information by performing appropriate actions. WSANs thus enable cyber systems to monitor and manipulate the behavior of the physical world. WSANs are growing at a tremendous pace, just like the exploding evolution of Internet. Supporting quality of service (QoS) will be of critical importance for pervasive WSANs that serve as the network infrastructure of diverse applications. To spark new research and development interests in this field, this paper examines and discusses the requirements, critical challenges, and open research issues on QoS management in WSANs. A brief overview of recent progress is given.
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paper_title: Computational Intelligence PC Tools

paper_content:

Computational intelligence is an emerging field in computer science which combines fuzzy logic, neural networks, and genetic algorithms for a flexible yet powerful approach to scientific computing. Because computational intelligence combines three interrelated, mathematically-based tools, it has a wide variety of applications, from engineering and process control to experts systems. This book takes a hands-on, desktop-applications approach to the topic, featuring examples of specific real-world implementations and detailed case studies, with all pertinent code and software included on a floppy disk packaged with the book. Features: * Concise introduction to the concepts of fuzzy logic, neural networks, and genetic algorithms, and how they relate to one another within the context of computational intelligence. * Computational intellignece applications, including self-organizing feature maps, fuzzy calculator, evolutionary programming, and fuzzy neural networks. * Detailed case studies from engineering (F-16 flight system), systems control (mass transit scheduling), and medicine (appendicitis diagnosis). * Windows floppy disk with both source code and executable, self-contained programs for desktop implementation of all of the book's applications.
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paper_title: A new optimizer using particle swarm theory

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The optimization of nonlinear functions using particle swarm methodology is described. Implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm. Benchmark testing of both paradigms is described, and applications, including neural network training and robot task learning, are proposed. Relationships between particle swarm optimization and both artificial life and evolutionary computation are reviewed.
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paper_title: Particle swarm optimization

paper_content:

A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed. The relationships between particle swarm optimization and both artificial life and genetic algorithms are described.
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paper_title: A modified particle swarm optimizer

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Evolutionary computation techniques, genetic algorithms, evolutionary strategies and genetic programming are motivated by the evolution of nature. A population of individuals, which encode the problem solutions are manipulated according to the rule of survival of the fittest through "genetic" operations, such as mutation, crossover and reproduction. A best solution is evolved through the generations. In contrast to evolutionary computation techniques, Eberhart and Kennedy developed a different algorithm through simulating social behavior (R.C. Eberhart et al., 1996; R.C. Eberhart and J. Kennedy, 1996; J. Kennedy and R.C. Eberhart, 1995; J. Kennedy, 1997). As in other algorithms, a population of individuals exists. This algorithm is called particle swarm optimization (PSO) since it resembles a school of flying birds. In a particle swarm optimizer, instead of using genetic operators, these individuals are "evolved" by cooperation and competition among the individuals themselves through generations. Each particle adjusts its flying according to its own flying experience and its companions' flying experience. We introduce a new parameter, called inertia weight, into the original particle swarm optimizer. Simulations have been done to illustrate the significant and effective impact of this new parameter on the particle swarm optimizer.
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paper_title: Distributed sensor placement with sequential particle swarm optimization

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Sequential particle swarm optimization (S-PSO) is a modification of PSO suitable for high-dimensional optimization problems. S-PSO iteratively optimizes the objective function over randomly selected subspaces of the parameter search space instead of the entire parameter space at once as in standard PSO. This approach is advantageous since fewer particles are needed to solve the lower-dimension subproblems. S-PSO is applied to the distributed sonar sensor placement problem, where not only the dimensionality issue arises, but also the computational complexity of the objective function increases with the problem size. Simulations show that S-PSO outperforms standard PSO both in terms of convergence and computational efficiency.
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paper_title: Cluster-head identification in ad hoc sensor networks using particle swarm optimization

paper_content:

We propose a new application of the optimization technique known as particle swarm optimization (PSO) to the problem of clustering nodes. The PSO approach is an evolutionary programming technique where a 'swarm' of test solutions, analogous to a natural swarm of bees, ants or termites, is allowed to interact and cooperate to find the best solution to the given problem. In a typical optimization, some function or fitness is used as a criterion for the optimization. Here we use application specific criteria, where we are equalizing the number of nodes, and candidate cluster-heads in each cluster, with the objective of minimizing the energy expended by the nodes while maximizing the total data gathered. The objective criteria fit with the implementation of a wireless, ad hoc, sensor network with cluster-head routing and data aggregation.
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paper_title: Improving sensing coverage of wireless sensor networks by employing mobile robots

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To provide proper coverage of their random deployment regions, wireless sensor networks (WSN) should employ abundant nodes. In this paper we correct such situations by employing some mobile robots as mobile nodes in WSN which can actively move to desired locations for repairing the broken networks. According to the pre-research work we know that the number of nodes employed by WSN is closely relevant to quality of service (QoS) in sensing coverage when the nodes are randomly deployed in the target region. A modified particle swarm optimization (PSO) named particle swarm genetic optimization (PSGO), which imports selection and mutation operators in PSO to overcome the premature fault of classical PSO, is proposed to redeploy the mobile robots according to the node density for repairing the sensing coverage hole after their initial random deployment. It is suggested by the simulated experiment results that the WSN employing the mobile robots can improve the QoS in sensing coverage than the stationary WSN by redeploying mobile robots according to the node density.
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paper_title: Co-evolutionary particle swarm optimization to solve min-max problems

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A co-evolutionary particle swarm optimization (PSO) to solve constrained optimization problems is proposed. First, we introduce the augmented Lagrangian to transform a constrained optimization to a min-max problem with the saddle-point solution. Next, a co-evolutionary PSO algorithm is developed with one PSO focusing on the minimum part of the min-max problem with the other PSO focusing on the maximum part of the min-max problem. The two PSOs are connected through the fitness function. In the fitness calculation of one PSO, the other PSO serves as the environment to that PSO. The new algorithm is tested on three benchmark functions. The simulation results illustrate the efficiency and effectiveness of the new co-evolutionary particle swarm algorithm.
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paper_title: An Improved Co-evolutionary Particle Swarm Optimization for Wireless Sensor Networks with Dynamic Deployment

paper_content:

The effectiveness of wireless sensor networks (WSNs) depends on the coverage and target detection probability provided by dynamic deployment, which is usually supported by the virtual force (VF) algorithm. However, in the VF algorithm, the virtual force exerted by stationary sensor nodes will hinder the movement of mobile sensor nodes. Particle swarm optimization (PSO) is introduced as another dynamic deployment algorithm, but in this case the computation time required is the big bottleneck. This paper proposes a dynamic deployment algorithm which is named “virtual force directed co-evolutionary particle swarm optimization” (VFCPSO), since this algorithm combines the co-evolutionary particle swarm optimization (CPSO) with the VF algorithm, whereby the CPSO uses multiple swarms to optimize different components of the solution vectors for dynamic deployment cooperatively and the velocity of each particle is updated according to not only the historical local and global optimal solutions, but also the virtual forces of sensor nodes. Simulation results demonstrate that the proposed VFCPSO is competent for dynamic deployment in WSNs and has better performance with respect to computation time and effectiveness than the VF, PSO and VFPSO algorithms.
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paper_title: Bio-inspired Algorithms for Autonomous Deployment and Localization of Sensor Nodes

paper_content:

Optimal deployment and accurate localization of sensor nodes have a strong influence on the performance of a wireless sensor network (WSN). This paper considers real-time autonomous deployment of sensor nodes from an unmanned aerial vehicle (UAV). Such a deployment has importance, particularly in ad hoc WSNs, for emergency applications, such as disaster monitoring and battlefield surveillance. The objective is to deploy the nodes only in the terrains of interest, which are identified by segmentation of the images captured by a camera on board the UAV. Bioinspired algorithms, particle swarm optimization (PSO) and bacterial foraging algorithm (BFA), are presented in this paper for image segmentation. In addition, PSO and BFA are presented for distributed localization of the deployed nodes. Image segmentation for autonomous deployment and distributed localization are formulated as multidimensional optimization problems, and PSO and BFA are used as optimization tools. Comparisons of the results of PSO and BFA for autonomous deployment and distributed localization are presented. Simulation results show that both the algorithms perform multilevel image segmentation faster than the exhaustive search for optimal thresholds. Besides, PSO-based localization is observed to be faster, and BFA-based localization is more accurate.
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paper_title: Allocating Multiple Base Stations under General Power Consumption by the Particle Swarm Optimization

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In this paper, a two-tiered wireless sensor networks consisting of small sensor nodes, application nodes and base-stations is considered. An algorithm based on particle swarm optimization (PSO) is proposed for multiple base stations under general power-consumption constraints. The proposed approach can search for nearly optimal BS locations in heterogeneous sensor networks, where application nodes may own different data transmission rates, initial energies and parameter values. Experimental results also show the good performance of the proposed PSO approach and the effects of the parameters on the results
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paper_title: An Experimental Study of the Small World Problem

paper_content:

Arbitrarily selected individuals (N=296) in Nebraska and Boston are asked to generate acquaintance chains to a target person in Massachusetts, employing “the small world method” (Milgram, 1967). Sixty-four chains reach the target person. Within this group the mean number of intermediaries between starters and targets is 5.2. Boston starting chains reach the target person with fewer intermediaries than those starting in Nebraska; subpopulations in the Nebraska group do not differ among themselves. The funneling of chains through sociometric “stars” is noted, with 48 per cent of the chains passing through three persons before reaching the target. Applications of the method to studies of large scale social structure are discussed.
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paper_title: A Survey on Wireless Sensor Networks Deployment

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In recent years extensive research has opened challenging issues for wireless sensor networks (WSNs) deployment. Among numerous challenges faced while designing architectures and protocols, maintaining connectivity and maximizing the network lifetime stand out as critical considerations. WSNs are formed by a large number of resource-constrained and inexpensive nodes, which has an impact on protocol design and network scalability. Sensor networks have enabled a range of applications where the objective is to observe an environment and collect information about the observed phenomena or events. This has lead to the emergence of a new generation sensor networks called sensor actuator networks. Approaches developed to query sensor-actuator networks (SANETs) are either application-specific or generic. Application-specific SANETs provide limited reusability, are net cost effective and may require extensive programming efforts to make the network able to serve new applications. A WSNs should be able to operate for long time with little or no external management. The sensor nodes must be able to configurate themselves in the presence of adverse situations. In this work, dealing with challenges for WSNs deployment, we start with mobility-based communication in WSNs. Then, we introduce service-oriented SANETs (SOSANETs) as an approach to build customizable SANETs. In the second part, we describe localization systems and analyze self configurability, situation awareness and intrusion detection system. In the third part, we present wireless distributed detection as well as a model for WSN simulation. Finally, conclusions and proposals for future research are given.
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paper_title: Extending wireless sensor network lifetime with base station repositioning

paper_content:

In wireless sensor networks, the major source of a sensor node failure is battery exhaustion and replacing this energy source in the field is usually not practical. Therefore, the use of energy efficient infrastructure, such as repositioning the base station in a clustered wireless sensor network, is able to prolong the lifetime of the network and improve the overall network data. In this paper, we proposed an energy-efficient protocol for the repositioning of mobile base station using particle swarm optimization (PSO) in wireless sensor networks. Simulation results demonstrate that the proposed protocol can improve the network lifetime, data delivery and energy consumption compared to existing energy-efficient protocols developed for this network.
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paper_title: Prolonging lifetime of wireless sensor networks with mobile base station using particle swarm optimization

paper_content:

Wireless sensor networks are a family of networks in wireless communication system and have the potential to become significant subsystem of engineering applications. In view of the fact that the sensor nodes in wireless sensor networks are typically irreplaceable, this type of network should operate with minimum possible energy in order to improve overall energy efficiency. Therefore, the protocols and algorithms developed for sensor networks must incorporate energy consumption as the highest priority optimization goal. Since the base station in sensor networks is usually a node with high processing power, high storage capacity and the battery used can be rechargeable, the base station can be utilized to collect data from each sensor node in the sensing area by moving closer to the transmitting node. In this paper, we proposed an energy-efficient protocol for the movement of mobile base station using particle swarm optimization (PSO) method in wireless sensor networks. Simulation results demonstrate that the proposed protocol can improve the network lifetime, data delivery and energy consumption compared to existing energy-efficient protocols developed for this network.
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paper_title: Energy-Aware Clustering for Wireless Sensor Networks using Particle Swarm Optimization

paper_content:

Wireless sensor networks (WSNs) are mainly characterized by their limited and non-replenishable energy supply. Hence, the need for energy efficient infrastructure is becoming increasingly more important since it impacts upon the network operational lifetime. Sensor node clustering is one of the techniques that can expand the lifespan of the whole network through data aggregation at the cluster head. In this paper, we present an energy-aware clustering for wireless sensor networks using particle swarm optimization (PSO) algorithm which is implemented at the base station. We define a new cost function, with the objective of simultaneously minimizing the intra-cluster distance and optimizing the energy consumption of the network. The performance of our protocol is compared with the well known cluster-based protocol developed for WSNs, LEACH (low-energy adaptive clustering hierarchy) and LEACH-C, the later being an improved version of LEACH. Simulation results demonstrate that our proposed protocol can achieve better network lifetime and data delivery at the base station over its comparatives.
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paper_title: A Particle Swarm Optimization based approach for the maximum coverage problem in cellular base stations positioning

paper_content:

The demand for cellular systems has grown in recent years and sometimes it is not an easy task to design such systems. We propose in this paper a new approach to tackle the maximum coverage problem in cellular systems using Particle Swarm Optimization (PSO). We adapted the PSO since we have associated the position of the base stations to the particles. We also developed two mechanisms to avoid overllaping among the cells and to maximize the coverage of the entire system. We tested our approach in two scenarios in different configurations. We believe that the results are interesting and with future works we can create a commercial tool to solve the real problem.
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paper_title: Optimized sink node path using particle swarm optimization

paper_content:

A wireless sensor network (WSN) is comprised of large number of sensors distributed in a monitoring field and a sink node to gather process and control data. The performance of the network depends on the behavior of the sink node and its location. An optimized sink node path will be efficient and economical for operation of the network. In this paper, we propose a novel method to derive the optimum path of a sink node in a fixed network of sensor nodes considering practical difficulties such as the limitation in the sink movement. The proposed evolutionary computing technique based simulator PSO-SIMSENS is an integrated system of particle swarm optimization and a sensor network simulator with an appropriate fitness function. This system can be configured for numerous applications such as manufacturing, bush fire monitoring etc. The simulation results show that our approach achieves efficient performance of WSN with maximum field coverage while sink node is mobile.
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paper_title: Optimization of Sensor Node Locations in a Wireless Sensor Network

paper_content:

In this paper, a localization system for unknown emitter nodes in a wireless sensor network (WSN) system is presented. For this system, it is assumed that there are four anchor nodes with known locations and one or more unknown nodes which transmit RF signals that can be received by the four anchor nodes. The only available information to the system is the received signal strength indicator which is in general not very accurate. To obtain better estimated location of the sensor nodes, the particle swarm optimization (PSO) scheme that can be realized in real time is investigated in this paper. Both simulation and experimental results of the proposed approach are presented.
---
paper_title: Bio-inspired node localization in wireless sensor networks

paper_content:

Many applications of wireless sensor networks (WSNs) require location information of the randomly deployed nodes. A common solution to the localization problem is to deploy a few special beacon nodes having location awareness, which help the ordinary nodes to localize. In this approach, non-beacon nodes estimate their locations using noisy distance measurements from three or more non-collinear beacons they can receive signals from. In this paper, the ranging-based localization task is formulated as a multidimensional optimization problem, and addressed using bio-inspired algorithms, exploiting their quick convergence to quality solutions. An investigation on distributed iterative localization is presented in this paper. Here, the nodes that get localized in an iteration act as references for remaining nodes to localize. The problem has been addressed using particle swarm optimization (PSO) and bacterial foraging algorithm (BFA). A comparison of the performances of PSO and BFA in terms of the number of nodes localized, localization accuracy and computation time is presented.
---
paper_title: Cluster-head identification in ad hoc sensor networks using particle swarm optimization

paper_content:

We propose a new application of the optimization technique known as particle swarm optimization (PSO) to the problem of clustering nodes. The PSO approach is an evolutionary programming technique where a 'swarm' of test solutions, analogous to a natural swarm of bees, ants or termites, is allowed to interact and cooperate to find the best solution to the given problem. In a typical optimization, some function or fitness is used as a criterion for the optimization. Here we use application specific criteria, where we are equalizing the number of nodes, and candidate cluster-heads in each cluster, with the objective of minimizing the energy expended by the nodes while maximizing the total data gathered. The objective criteria fit with the implementation of a wireless, ad hoc, sensor network with cluster-head routing and data aggregation.
---
paper_title: Wireless Sensor Network Localization Techniques

paper_content:

Wireless sensor network localization is an important area that attracted significant research interest. This interest is expected to grow further with the proliferation of wireless sensor network applications. This paper provides an overview of the measurement techniques in sensor network localization and the one-hop localization algorithms based on these measurements. A detailed investigation on multi-hop connectivity-based and distance-based localization algorithms are presented. A list of open research problems in the area of distance-based sensor network localization is provided with discussion on possible approaches to them.
---
paper_title: A particle swarm optimization approach for the localization of a wireless sensor network

paper_content:

For many wireless sensor network applications, the localization of sensor nodes is an essential requirement. In this paper, a low cost localization system based on the measurements from a pedometer and communication ranging between neighboring nodes is presented. Unlike most of the existing methods that require good network connectivity, the proposed system works well in a sparse network. The localization information is obtained through a probability based algorithm that requires the solving of a nonlinear optimization problem. To obtain the optimum location of the sensor nodes, the particle swarm optimization (PSO) scheme that can be realized with a microcontroller for real time application is investigated in this paper. Experimental results show that the proposed approach yields good performance.
---
paper_title: Energy-Aware Clustering for Wireless Sensor Networks using Particle Swarm Optimization

paper_content:

Wireless sensor networks (WSNs) are mainly characterized by their limited and non-replenishable energy supply. Hence, the need for energy efficient infrastructure is becoming increasingly more important since it impacts upon the network operational lifetime. Sensor node clustering is one of the techniques that can expand the lifespan of the whole network through data aggregation at the cluster head. In this paper, we present an energy-aware clustering for wireless sensor networks using particle swarm optimization (PSO) algorithm which is implemented at the base station. We define a new cost function, with the objective of simultaneously minimizing the intra-cluster distance and optimizing the energy consumption of the network. The performance of our protocol is compared with the well known cluster-based protocol developed for WSNs, LEACH (low-energy adaptive clustering hierarchy) and LEACH-C, the later being an improved version of LEACH. Simulation results demonstrate that our proposed protocol can achieve better network lifetime and data delivery at the base station over its comparatives.
---
paper_title: Cluster Heads Election Analysis for Multi-hop Wireless Sensor Networks Based on Weighted Graph and Particle Swarm Optimization

paper_content:

Continued advances of wireless communication technologies have enabled the deployment of large scale wireless sensor networks. The sensors' limited power makes energy consumption a critical issue. In single-hop wireless sensor networks, cluster heads election method based on residual energy can obtain better energy efficiency than the method in which cluster heads are elected in turns or by probabilities. Is it the same in multi-hop wireless sensor networks? In this paper we proposed and evaluated a routing optimization scheme based on graph theory and particle swarm optimization algorithm for multi-hop wireless sensor network. Our algorithm synthesized the intuitionist advantages of graph theory and optimal search capability of PSO. The result in multi-hop networks is completely different from that in single-hop wireless sensor networks. The result shows that there is very little difference from these methods. The reason is discussed in detail.
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paper_title: A clustering scheme for hierarchical control in multi-hop wireless networks

paper_content:

In this paper we present a clustering scheme to create a hierarchical control structure for multi-hop wireless networks. A cluster is defined as a subset of vertices, whose induced graph is connected. In addition, a cluster is required to obey certain constraints that are useful for management and scalability of the hierarchy. All these constraints cannot be met simultaneously for general graphs, but we show how such a clustering can be obtained for wireless network topologies. Finally, we present an efficient distributed implementation of our clustering algorithm for a set of wireless nodes to create the set of desired clusters.
---
paper_title: Particle swarm optimization for the clustering of wireless sensors

paper_content:

Clustering is necessary for data aggregation, hierarchical routing, optimizing sleep patterns, election of extremal sensors, optimizing coverage and resource allocation, reuse of frequency bands and codes, and conserving energy. Optimal clustering is typically an NP-hard problem. Solutions to NP-hard problems involve searches through vast spaces of possible solutions. Evolutionary algorithms have been applied successfully to a variety of NP-hard problems. We explore one such approach, Particle Swarm Optimization (PSO), an evolutionary programming technique where a 'swarm' of test solutions, analogous to a natural swarm of bees, ants or termites, is allowed to interact and cooperate to find the best solution to the given problem. We use the PSO approach to cluster sensors in a sensor network. The energy efficiency of our clustering in a data-aggregation type sensor network deployment is tested using a modified LEACH-C code. The PSO technique with a recursive bisection algorithm is tested against random search and simulated annealing; the PSO technique is shown to be robust. We further investigate developing a distributed version of the PSO algorithm for clustering optimally a wireless sensor network.
---
paper_title: Cluster-head identification in ad hoc sensor networks using particle swarm optimization

paper_content:

We propose a new application of the optimization technique known as particle swarm optimization (PSO) to the problem of clustering nodes. The PSO approach is an evolutionary programming technique where a 'swarm' of test solutions, analogous to a natural swarm of bees, ants or termites, is allowed to interact and cooperate to find the best solution to the given problem. In a typical optimization, some function or fitness is used as a criterion for the optimization. Here we use application specific criteria, where we are equalizing the number of nodes, and candidate cluster-heads in each cluster, with the objective of minimizing the energy expended by the nodes while maximizing the total data gathered. The objective criteria fit with the implementation of a wireless, ad hoc, sensor network with cluster-head routing and data aggregation.
---
paper_title: Energy-efficient communication protocol for wireless microsensor networks

paper_content:

Wireless distributed microsensor systems will enable the reliable monitoring of a variety of environments for both civil and military applications. In this paper, we look at communication protocols, which can have significant impact on the overall energy dissipation of these networks. Based on our findings that the conventional protocols of direct transmission, minimum-transmission-energy, multi-hop routing, and static clustering may not be optimal for sensor networks, we propose LEACH (Low-Energy Adaptive Clustering Hierarchy), a clustering-based protocol that utilizes randomized rotation of local cluster based station (cluster-heads) to evenly distribute the energy load among the sensors in the network. LEACH uses localized coordination to enable scalability and robustness for dynamic networks, and incorporates data fusion into the routing protocol to reduce the amount of information that must be transmitted to the base station. Simulations show the LEACH can achieve as much as a factor of 8 reduction in energy dissipation compared with conventional outing protocols. In addition, LEACH is able to distribute energy dissipation evenly throughout the sensors, doubling the useful system lifetime for the networks we simulated.
---
paper_title: Particle Swarm Optimisers for Cluster formation in Wireless Sensor Networks

paper_content:

We describe the results of a performance evaluation of four extensions of Particle Swarm Optimisation (PSO) to reduce energy consumption in wireless sensor networks. Communication distances are an important factor to be reduced in sensor networks. By using clustering in a sensor network we can reduce the total communication distance, thus increasing the life of a network. We adopt a distance based clustering criterion for sensor network optimisation. From PSO perspective, we study the suitability of four different PSO algorithms for our application and propose modifications. An important modification proposed is to use a boundary checking routine for re-initialisation of a particle which moves outside the set boundary.
---
paper_title: Linear Decision Fusion under the Control of Constrained PSO for WSNs

paper_content:

A major application of a distributed WSN (wireless sensor network) is to monitor a specific area for detecting some events such as disasters and enemies. In order to achieve this objective, each sensor in the network is required to collect local observations which are probably corrupted by noise, make a local decision regarding the presence or absence of an event, and then send its local decision to a fusion center. After that, the fusion center makes the final decision depending on these local decisions and a decision fusion rule, so an efficient decision fusion rule is extremely critical. It is obvious that the decision-making capability of each node is different owing to the dissimilar signal noise ratios and some other factors, so it is easy to understand that a specific sensor contribution to the global decision should be constrained by this sensor decision-making capability, and, based on this idea, we establish a novel linear decision fusion model for WSNs. Moreover, the constrained particle swarm optimization (constrained PSO) algorithm is creatively employed to control the parameters of this model in this paper and we also apply the typical penalty function to solve the constrained PSO problem. The emulation results indicate that our design is capable of achieving very high accuracy.
---
paper_title: Improving personal identification accuracy using multisensor fusion for building access control applications

paper_content:

This paper discusses a multimodal biometric sensor fusion approach for controlling building access. The motivation behind using multimodal biometrics is to improve universality and accuracy of the system. A Bayesian framework is implemented to fuse the decisions received from multiple biometric sensors. The system accuracy improves for a subset of decision fusion rules. The optimal rule is a function of the error cost and a priori probability of an intruder. This Bayesian framework formalizes the design of a system that can adaptively increase or reduce the security level. This is important to systems designed for varying security needs and user access requirements. The additional biometric modes and variable error costs give the system adaptability improving system acceptability. This paper presents the framework using three different biometric systems: voice, face, and hand biometric systems.
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paper_title: Swarm intelligence based optimization and control of decentralized serial sensor networks

paper_content:

In this paper threshold design and hierarchy management of serial sensor networks employed for distributed detection is accomplished using a hybrid of ant colony optimization and particle swarm optimization. The particle swarm optimization determines the optimal thresholds, decision rules for the sensors. The ant colony optimization algorithm determines the hierarchy of sensor decision communication, affecting the accuracy. The problem of hierarchy management is known as ldquowho reports to whom?rdquo problem in sensor networks. The new algorithm is tested on a suite of 10 heterogeneous sensors. Probabilistic measures including probability of error and Bayesian risk are adopted to evaluate the performance of the sensor network. The new sensor management methodology is compared to (a) static hierarchy network, (b) a network with the best sensor at the top of the hierarchy and (c) incrementally best hierarchy. Results show 40% performance improvements in terms of Bayesian risk value.
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paper_title: Multi-Source Temporal Data Aggregation in Wireless Sensor Networks

paper_content:

Data aggregation has been emerged as a basic approach in wireless sensor networks (WSNs) in order to reduce the number of transmissions of sensor nodes.This paper proposes an energy-efficient multi-source temporal data aggregation model called MSTDA in WSNs. In MSTDA model, a feature selection algorithm using particle swarm optimization (PSO) is presented to simplify the historical data source firstly. And then a data prediction algorithm based on improved BP neural network with PSO (PSO-BPNN) is proposed. This MSTDA model, which helps to find out potential laws according to historical data sets, is deployed at both the base station (BS) and the node. Only when the deviation between the actual and the predicted value at the node exceeds a certain threshold, the sampling value and new model are sent to BS. The experiments on the dataset which comes from the actual data collected from 54 sensors deployed in the Intel Berkeley Research lab made a satisfied performance. When the error threshold greater than 0.15, it can decrease more than 80% data transmissions.
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paper_title: Dynamic sensor management using multi-objective particle swarm optimizer

paper_content:

This paper presents a Swarm Intelligence based approach for sensor management of a multi sensor network. Alternate sensor configurations and fusion strategies are evaluated by swarm agents, and an optimum configuration and fusion strategy evolves. An evolutionary algorithm, particle swarm optimization, is modified to optimize two objectives: accuracy and time. The output of the algorithm is the choice of sensors, individual sensor’s thresholds and the optimal decision fusion rule. The results achieved show the capability of the algorithm in selecting optimal configuration for a given requirement consisting of multiple objectives.
---
paper_title: Optimized sink node path using particle swarm optimization

paper_content:

A wireless sensor network (WSN) is comprised of large number of sensors distributed in a monitoring field and a sink node to gather process and control data. The performance of the network depends on the behavior of the sink node and its location. An optimized sink node path will be efficient and economical for operation of the network. In this paper, we propose a novel method to derive the optimum path of a sink node in a fixed network of sensor nodes considering practical difficulties such as the limitation in the sink movement. The proposed evolutionary computing technique based simulator PSO-SIMSENS is an integrated system of particle swarm optimization and a sensor network simulator with an appropriate fitness function. This system can be configured for numerous applications such as manufacturing, bush fire monitoring etc. The simulation results show that our approach achieves efficient performance of WSN with maximum field coverage while sink node is mobile.
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paper_title: In-network aggregation techniques for wireless sensor networks: a survey

paper_content:

In this article we provide a comprehensive review of the existing literature on techniques and protocols for in-network aggregation in wireless sensor networks. We first define suitable criteria to classify existing solutions, and then describe them by separately addressing the different layers of the protocol stack while highlighting the role of a cross-layer design approach, which is likely to be needed for optimal performance. Throughout the article we identify and discuss open issues, and propose directions for future research in the area
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paper_title: Optimal Power Scheduling for Correlated Data Fusion in Wireless Sensor Networks via Constrained PSO

paper_content:

Optimal power scheduling for distributed detection in a Gaussian sensor network is addressed for both independent and correlated observations. We assume amplify-and-forward local processing at each node. The wireless link between sensors and the fusion center is assumed to undergo fading and coefficients are assumed to be available at the transmitting sensors. The objective is to minimize the total network power to achieve a desired fusion error probability at the fusion center. For i.i.d. observations, the optimal power allocation is derived analytically in closed form. When observations are correlated, first, an easy to optimize upper bound is derived for sufficiently small correlations and the power allocation scheme is derived accordingly. Next, an evolutionary computation technique based on particle swarm optimization is developed to find the optimal power allocation for arbitrary correlations. The optimal power scheduling scheme suggests that the sensors with poor observation quality and bad channels should be inactive to save the total power expenditure of the system. It is shown that the probability of fusion error performance based on the optimal power allocation scheme outperforms the uniform power allocation scheme especially when either the number of sensors is large or the local observation quality is good.
---
paper_title: An ant colony optimization approach for reputation and quality-of-service-based security in wireless sensor networks

paper_content:

In wireless sensor networks (WSN), message security is an important concern. The protection of integrity and confidentiality of information and the protection from unauthorized access are important issues. However, due to factors such as resource limitations, absence of centralized access points, open wireless medium and small size of the sensor nodes, the implementation of security in WSN is a challenging task. ::: ::: ::: ::: In this paper, we propose a protocol, quality-based distance vector routing (QDV), for securing WSN using concepts based on Ant colony optimization ACO [1]. Two fundamental parameters—quality-of-service (QoS) and reputation [2]—are used. The high value of reputation of a node signifies that the node is trusted and is more reliable for data communication purposes. As a node shows signs of misbehavior, its reputation decreases, which, in turn, affects its quality-of-security QSec [2], thereby disabling the malicious nodes from gaining access to the network. By incorporating these two factors, we are able to distinguish the nodes present in the network. We, then, present a method to achieve “equilibrium” where the node is able to guarantee that its neighbors are secure. Copyright © 2008 John Wiley & Sons, Ltd.
---
paper_title: 2ASenNet: A multiple QoS metrics hierarchical routing protocol based on swarm intelligence optimization for WSN

paper_content:

The increasing demand for real-time application in wireless sensor network (WSN) has made the Quality of Service (QoS) based communication protocols a hot research topic. Most routing protocols consider energy saving as the main objective and assume data traffic with unconstrained delivery requirements to be a given. However, with the arrival of wireless multimedia sensor networks, the transmission of imaging and video information requires both energy efficiency and QoS assurance (e.g. bandwidth, packet loss and delay constraints). Swarm intelligence algorithms are the relatively effective strategy for distributed optimization and show the desirable properties of being adaptive, scalable, and robust in particular of routing in WSN. In this paper, In order to achieve the balancing of energy-efficiency with QoS improvements for applications specific, a multiple QoS metrics hierarchical routing protocol based on swarm intelligence optimization (combination of improving ACO and AFSO) for WSN is proposed, namely 2ASenNet. The 2ASenNet protocol built a hierarchical structure on the network before choosing suitable paths to meet various QoS requirements. Then, the 2ASenNet protocol adopted hybrid ant behavior to produce diverse original paths, adding AFSO to ACO's per iterative process, and the optimization path was explored according to multiple QoS constrained. Simulation results have shown the efficiency of this novel approach, while simultaneously reducing the consumption of constrained resources as much as possible.
---
paper_title: Enhanced Ant Colony algorithm for self-optimized data assured routing in wireless sensor networks

paper_content:

Wireless sensor network (WSN) deploys tiny wireless sensor nodes to communicate with each other with limited processing speed, power and security measures. A recent WSN routing protocol defined as Secure Real-Time Load Distribution (SRTLD) has been developed to provide realtime transfer, high delivery ratio, and longer sensor node lifetime. SRTLD has been compared with LQER, MMSpeed, RTPC and RPAR. However, SRTLD uses broadcast packets to perform neighbour discovery for every packet transfer every hop, thus consume high energy. A novel Biological inspired self-organized Secure Autonomous Routing Protocol (BIOSARP) to enhance SRTLD with self-optimized and autonomous secure routing mechanism. The BIOSARP routing protocol depends on the optimal forwarding decision obtained by Ant Colony Optimization (ACO). The pheromone value in ACO is computed based on end-to-end delay, remaining battery power, and packet reception rate metrics similar to SRTLD. The proposed BIOSARP has been designed to reduce overhead broadcast packet in order to minimize the delay, packet loss and power consumption in WSN. In this paper we have presented the improved ACO algorithm that has been utilized in BIOSARP to perform self-optimized routing in WSN. BIOSARP has been studied and verified through simulation in network simulator 2 (NS-2). In simulation study BIOSARP normalized overhead is 12.1% less as compare to E&D ANTS and achieves 14% higher delivery ratio with 9% less power consumption when compared to SRTLD. Hence, the results confirm that BIOSARP offers better performance and can be practically implemented in WSN applications as structural and environmental monitoring, battlefield surveillance.
---
paper_title: Ant colony optimization for wireless sensor networks routing

paper_content:

Due to energy limitation of sensors, maximization of lifetime is the key to performance of WSN routing protocol. In this paper, we introduce a heuristic way to reduce energy consumption in WSNs routing process using Ant Colony Optimization. We introduce three Ant Colony Optimization algorithms, the Ant System, Ant Colony System and improved AS and their application in WSN routing process. The simulation results show that ACO is an effective way to reduce energy consumption and maximize WSN lifetime.
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paper_title: Energy-aware Routing Algorithm in WSN using predication-mode

paper_content:

The paper presents a Predication mode based Routing Algorithm based on ACO (PRACO) to achieve the energy-aware data-gathering routing structure in wireless sensor networks (WSN). We adopt series model ARMA to analyze dynamic tendency in data traffic and deduce the construction of load factor, which can help to reveal the future energy status of sensor in WSN. By checking the load factor in heuristic factor and guided by novel pheromone updating rule, artificial ants can foresee the local energy state of networks and the corresponding actions could be adaptively taken to enhance the energy efficiency in routing construction. The simulation results show that the proposed routing building scheme can effectively reinforce the robustness of routing structure by mining the temporal associability and introducing ACO to balance the total energy cost for data transmission, which also contributes to minimize the total communication consumption, meanwhile, to prolong the lifetime of networks.
---
paper_title: An improved routing mechanism using bio-inspired for energy balancing in wireless sensor networks

paper_content:

Planning of energy-efficient protocols is critical for wireless sensor networks (WSNs) because of the constraints on sensor node's energy. Therefore, the routing protocol should be able to achieve uniform power dissipation during transmission to the sink node. In this paper, we present a self-optimization scheme for WSN which is able to utilize and optimize the sensor nodes' resources, especially the batteries, to achieve balanced energy consumption across all sensors. This method is based on Ant Colony Optimization (ACO) meta heuristic which is adopted to enhance the paths with the best quality function. The assessment of this function depends on multi-criteria metrics such as the minimum residual battery power, hop numbers and average energy of both route and network. This method also distribute the traffic load of sensor nodes throughout the WSN leading to reduced energy usage, extended network life time and reduced packet loss. Simulation results show that our scheme performs much better than Energy Efficient Ant-Based Routing (EEABR) in terms of energy consumption and efficiency.
---
paper_title: Sensor Deployment of Wireless Sensor Networks Based on Ant Colony Optimization with Three Classes of Ant Transitions

paper_content:

The problem of minimum-cost and connectivity- guaranteed grid coverage (MCGC) is one of the most critical issues for the implementation of wireless sensor networks (WSNs). In this paper, a novel algorithm, ant colony optimization with three classes of ant transitions (ACO-TCAT) is proposed to decrease inferior solutions and narrow the searching range of the algorithm and finally to solve this problem. Simulation results are conducted to demonstrate the effectiveness of our proposed approach.
---
paper_title: Efficient deployment algorithms for ensuring coverage and connectivity of wireless sensor networks

paper_content:

Sensor deployment is a critical issue since it reflects the cost and detection capability of a wireless sensor network. Although lots of work has addressed this issue, most of them assume that the sensing field is an open space and there is a special relationship between the communication range and sensing range of sensors. In this work, we consider the sensing field as an arbitrary-shaped region possibly with obstacles. Besides, we allow an arbitrary relationship between the communication range and sensing range, thus eliminating the constraints of existing results. Our approach is to partition the sensing field into smaller sub-regions based on the shape of the field, and then to deploy sensors in these sub-regions. Simulation results show that our method requires fewer sensors compared to existing results.
---
paper_title: EasiDesign: An Improved Ant Colony Algorithm for Sensor Deployment in Real Sensor Network System

paper_content:

In this paper, we formulate coverage requirements of the sensor network as a minimum-cost connectivity guaranteed point k-coverage problem. An improved ant colony algorithm (EasiDesign) is proposed to achieve the approximate solution to this optimization problem. We made modifications in the convergence strategy and the ant state transition rule of the general ant colony optimization. Considering the practicability issues, we design the obstacle avoidance and the routing cost tradeoff strategies to ensure that EasiDesign can work efficiently. The simulation results show that EasiDesign uses less sensor nodes than the existing works in the same scenario. The performance is also demonstrated through a real sensor network system for the environment monitoring in the Forbidden City.
---
paper_title: Versatile low power media access for wireless sensor networks

paper_content:

We propose B-MAC, a carrier sense media access protocol for wireless sensor networks that provides a flexible interface to obtain ultra low power operation, effective collision avoidance, and high channel utilization. To achieve low power operation, B-MAC employs an adaptive preamble sampling scheme to reduce duty cycle and minimize idle listening. B-MAC supports on-the-fly reconfiguration and provides bidirectional interfaces for system services to optimize performance, whether it be for throughput, latency, or power conservation. We build an analytical model of a class of sensor network applications. We use the model to show the effect of changing B-MAC's parameters and predict the behavior of sensor network applications. By comparing B-MAC to conventional 802.11-inspired protocols, specifically SMAC, we develop an experimental characterization of B-MAC over a wide range of network conditions. We show that B-MAC's flexibility results in better packet delivery rates, throughput, latency, and energy consumption than S-MAC. By deploying a real world monitoring application with multihop networking, we validate our protocol design and model. Our results illustrate the need for flexible protocols to effectively realize energy efficient sensor network applications.
---
paper_title: Demonstration of a WSN Application in Relic Protection and an Optimized System Deployment Tool

paper_content:

This paper presents an overview of our demonstration of a real application of a WSN system for relic protection applied in the Forbidden City of China. We briefly introduce the EasiNet hardware platform we have developed and the corresponding mesh-architecture of the system constructed. We present a sensor deployment optimization tool based on ant colony optimization technology (DT-ACO) to meet the requirements of network connectivity, sensing coverage and a minimized overall cost of the system. We describe a novel power-aware cross-layer scheme (PACS) to facilitate adjustable system lifetime and surveillance accuracy. We also show some detailed results of the application case.
---
paper_title: Ant colony optimization based sensor deployment protocol for wireless sensor networks

paper_content:

Research highlights? We consider the problem of sensor deployment to achieve complete coverage of the service region and maximize the lifetime of the network. ? We model the deployment problem as the multiple knapsack problem. ? Based on ACO algorithm, we proposed a deployment scheme to prolong the network lifetime, while ensuring complete coverage of the service region. Sensor deployment is one of the most important issues in wireless sensor networks, because an efficient deployment scheme can reduce the deployment cost and enhance the detection capability of the wireless sensor networks. In addition, it can enhance the quality of monitoring in wireless sensor networks by increasing the coverage area. Ant colony optimization (ACO) algorithm provides a natural and intrinsic way of exploration of search space for multiple knapsack problem (MKP). In this work, we consider the problem of sensor deployment to achieve complete coverage of the service region and maximize the lifetime of the network. We model the deployment problem as the multiple knapsack problem. Based on ACO algorithm, we proposed a deployment scheme to prolong the network lifetime, while ensuring complete coverage of the service region. The simulations show that our algorithm can prolong the lifetime of the network.
---
paper_title: Adaptive Clustering for Energy Efficient Wireless Sensor Networks Based on Ant Colony Optimization

paper_content:

Energy supply in wireless sensor networks (WSNs) is limited and non-replenishable, and energy efficiency is the most important feature in designing these networks. One way to reduce the energy consumption of WSNs and hence prolong the lifespan of these networks is to use adaptive clustering algorithms. In this paper, we use Ant Colony Optimization for Clustering (ACO-C) to propose a new energy aware clustering protocol for WSNs. By using appropriate cost functions, implemented at the base station, our algorithm minimizes and distributes the cost of long distance transmissions and data aggregating among all sensor nodes evenly. Simulation results show the effectiveness of our protocol over other well known clustering algorithms in terms of both network lifetime and data delivery to the base station.
---
paper_title: An improved fuzzy unequal clustering algorithm for wireless sensor network

paper_content:

This paper proposes a novel energy efficient unequal clustering scheme for large scale wireless sensor networks(WSNs) which aims to balance the node power consumption and prolong the network lifetime as long as possible. Our approach focuses on energy efficient clustering scheme and inter-cluster routing protocol. On the one hand, considering each node's some local information such as energy level, distance to base station and local density, we use fuzzy logic system to determine one node's chance of becoming cluster head and estimate the cluster-head radius. On the other hand, the application of Ant Colony Optimization (ACO) to inter-cluster routing constructs energy-aware routing between cluster heads and base station, which reduces the energy consumption of cluster heads and solves the hot spots problem that occurs in multi-hop WSN routing protocol to a large extent. The confirmation experiment results have indicated the proposed clustering scheme has more superior performance than other methods such as LEACH and EEUC.
---
paper_title: An energy-efficient unequal clustering mechanism for wireless sensor networks

paper_content:

Clustering provides an effective way for prolonging the lifetime of a wireless sensor network. Current clustering algorithms usually utilize two techniques, selecting cluster heads with more residual energy and rotating cluster heads periodically, to distribute the energy consumption among nodes in each cluster and extend the network lifetime. However, they rarely consider the hot spots problem in multihop wireless sensor networks. When cluster heads cooperate with each other to forward their data to the base station, the cluster heads closer to the base station are burdened with heavy relay traffic and tend to die early, leaving areas of the network uncovered and causing network partition. To address the problem, we propose an energy-efficient unequal clustering (EEUC) mechanism for periodical data gathering in wireless sensor networks. It partitions the nodes into clusters of unequal size, and clusters closer to the base station have smaller sizes than those farther away from the base station. Thus cluster heads closer to the base station can preserve some energy for the inter-cluster data forwarding. We also propose an energy-aware multihop routing protocol for the inter-cluster communication. Simulation results show that our unequal clustering mechanism balances the energy consumption well among all sensor nodes and achieves an obvious improvement on the network lifetime
---
paper_title: Uneven clustering routing algorithm for Wireless Sensor Networks based on ant colony optimization

paper_content:

In order to make good use of the limited energy, ant colony optimization (ACO) was applied to inter-cluster routing mechanism. An uneven clustering routing algorithm for Wireless Sensor Networks (WSNs) based on ant colony optimization (ACO) was proposed. The algorithm utilized the dynamic adaptability and optimization capabilities of the ant colony to get the optimum route between the cluster head. Meanwhile, it organized different cluter in different size based on the distance between cluster heads and sink node, and cluters closter to sink had smaller sizes than those farther away from the sink, thus the closer cluster heads could preserve energy for the inter-cluster data forwarding. Simulation result indicates that the algorithm effectively balances the network energy comsuption and prolongs the network life cycle compared with LEACH and PARA.
---
paper_title: An energy efficient routing protocol for cluster-based wireless sensor networks using ant colony optimization

paper_content:

In this paper, we propose an efficient routing algorithm for large scale cluster-based wireless sensor networks. The technique uses two routing levels. In the first level (intra-cluster), cluster members send data directly to their cluster head. In the second level (inter-cluster), the cluster heads use ant colony optimization (ACO) algorithm, which is a biologically inspired paradigm for optimization approach, to find a route to the base station. As only cluster heads participate in the inter-cluster routing operation, the method can provide a smooth operation more effectively. The delay of the algorithm is minimized by using the ant colony optimization algorithm along with clustering. To assess the efficiency of the proposed algorithm, we compare the method with some previous routing algorithms. The results show lower power consumption and more load balancing.
---
paper_title: Energy-efficient communication protocol for wireless microsensor networks

paper_content:

Wireless distributed microsensor systems will enable the reliable monitoring of a variety of environments for both civil and military applications. In this paper, we look at communication protocols, which can have significant impact on the overall energy dissipation of these networks. Based on our findings that the conventional protocols of direct transmission, minimum-transmission-energy, multi-hop routing, and static clustering may not be optimal for sensor networks, we propose LEACH (Low-Energy Adaptive Clustering Hierarchy), a clustering-based protocol that utilizes randomized rotation of local cluster based station (cluster-heads) to evenly distribute the energy load among the sensors in the network. LEACH uses localized coordination to enable scalability and robustness for dynamic networks, and incorporates data fusion into the routing protocol to reduce the amount of information that must be transmitted to the base station. Simulations show the LEACH can achieve as much as a factor of 8 reduction in energy dissipation compared with conventional outing protocols. In addition, LEACH is able to distribute energy dissipation evenly throughout the sensors, doubling the useful system lifetime for the networks we simulated.
---
paper_title: Maximum lifetime data aggregation in distributed intelligent robot network based on ACO

paper_content:

Providing multimedia traffic support in distributed intelligent robot network (DIRN) as a kind of wireless sensor and actor network (WSAN) is addressed. Since multimedia traffic has stringent bounds on end-to-end delay resource reservation for transmitting such traffic has to be done. The existing methods used for multimedia traffic provide inefficient utilization of network resources and affect call acceptance and drop ratio of multimedia traffic severely. Hence a data aggregation scheme based on ant optimization algorithm using bionic swarm intelligence for supporting multimedia traffic is proposed to overcome those limitations and to help reduce the traffic to the sink node in turn reducing the power consumption of intermediate node. Lifetime maximization can balance the traffic across the network so as to avoid overwhelming the bottleneck nodes. Key issues and configurations are discussed and studied, such as influence of location of aggregation point, impact of network shape and balance based energy-efficient methods. Extensive simulations are done to assess the performance of the scheme under varying network condition for carrying multimedia traffic. A practical implementation with real DIRN has been carried out to validate the enhanced efficiency, stability and accuracy of the proposed algorithm, which is proved to lead to longer network lifetime in comparison to other traditional data aggregation schemes such as minimum energy gathering algorithm (MEGA) for supporting multimedia traffic in real time.
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paper_title: Ant-aggregation: ant colony algorithm for optimal data aggregation in wireless sensor networks

paper_content:

Data aggregation is an essential paradigm for energy efficient routing in energy constraint wireless sensor networks. The complexity of optimal data aggregation is NP-hard. Ant colony system, a population-based algorithm, provides natural and intrinsic way of exploration of search space in optimization settings in determining optimal data aggregation. The simulation results shows improvement in energy efficiency depending on the number of source nodes in the sensor network which is 45% energy efficiency using optimal aggregation compared to approximate aggregation schemes in moderate number of source whereas 20% energy efficiency in large number of source nodes. The proposed ant-aggregation algorithm is simulated in MATLAB.
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paper_title: A novel energy-efficient data gathering algorithm for wireless sensor networks

paper_content:

Data gathering is one of the most important operations in wireless sensor networks. Since the nodes operate on limited power, it is a critical task to design an energy-efficient data gathering algorithm. In this paper, we propose an energy-efficient data gathering algorithm (EDGA) in which the network is grouped into clusters (each with a clusterhead) and the nodes form chains in each cluster. Firstly, a node, according to support degree from neighbors and its remaining energy, independently makes its decision to compete for becoming a clusterhead. Furthermore, the clusterhead adapts ant colony optimization (ACO) to schedule access sequence of nodes (chain). Simulation results show that EDGA can reduce energy consumption efficiently and prolong the network lifetime when compared with the conventional clustering method.
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paper_title: Data Gathering Communication in Wireless Sensor Networks Using Ant Colony Optimization

paper_content:

This paper introduces a centralized approach to data gathering and communication for wireless sensor networks. Inspired by the social behaviors of ants, we clearly partition the work for the base station and sensor nodes according to their different functions and capabilities. A near-optimal chain is achieved by using an ant colony optimization method ruing in the base station. The sensor nodes in the network then form a bi-direction chain structure, which is self-adaptive to any minor changes. The simulation results show that the developed AntChain algorithm performs much better than the LEACH and PEGASIS methods, in terms of energy-efficiency, data integrity and life time, when the base station
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paper_title: Ant-Colony Optimization Based In-Network Data Aggregation in Wireless Sensor Networks

paper_content:

In-network data aggregation is an important technique for data collection in wireless sensor networks. It improves energy efficiency and alleviates congestive routing traffic by eliminating data redundancy in the message forwarding process. Ant-colony aggregation is a distributed algorithm that provides an intrinsic way of exploring the search space to optimize settings for optimal data aggregation. This paper aims to refine the heuristic function and the aggregation node selection method to maximize energy efficiency and to extend network lifetime. Two proposed algorithms are shown to yield longer maximum lifetime than the conventional algorithm with the same hop-count delay. One of the proposed algorithms is shown to have improved scalability than the conventional algorithm.
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paper_title: Genetic algorithm for hierarchical wireless sensor networks

paper_content:

Large scale wireless sensor networks (WSNs) can be used for various pervasive and ubiquitous applications such as security, health-care, industry automation, agriculture, environment and habitat monitoring. As hierarchical clusters can reduce the energy consumption requirements for WSNs, we investigate intelligent techniques for cluster formation and management. A genetic algorithm (GA) is used to create energy efficient clusters for data dissemination in wireless sensor networks. The simulation results show that the proposed intelligent hierarchical clustering technique can extend the network lifetime for different network deployment environments.
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paper_title: Genetic Algorithm for Energy Efficient Clusters in Wireless Sensor Networks

paper_content:

We use a genetic algorithm (GA) to create energy efficient clusters for routing in wireless sensor networks. The simulation results show that the proposed intelligent hierarchical clustering technique is more energy efficient than a few existing cluster-based routing protocols. Further, the gradual energy depletion in sensor nodes is also investigated
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paper_title: Sensor Network Optimization Using a Genetic Algorithm

paper_content:

In this paper, we propose an efficient method based on genetic algorithms (GAs) to solve a sensor network optimization problem. Long communication distances between sensors and a sink (or destination) in a sensor network can greatly drain the energy of sensors and reduce the lifetime of a network. By clustering a sensor network into a number of independent clusters using a GA, we can greatly minimize the total communication distance, thus prolonging the network lifetime. Simulation results show that our algorithm can quickly find a good solution. This approach is also applicable to multiple network topologies (uniform or non-uniform) or shortest distance optimization problems . Keyword: Genetic algorithm, clustering, network optimization, shortest distance.
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paper_title: An optimal energy-efficient clustering method in wireless sensor networks using multi-objective genetic algorithm

paper_content:

In this study, an optimal method of clustering homogeneous wireless sensor networks using a multi-objective two-nested genetic algorithm is presented. The top level algorithm is a multi-objective genetic algorithm (GA) whose goal is to obtain clustering schemes in which the network lifetime is optimized for different delay values. The low level GA is used in each cluster in order to get the most efficient topology for data transmission from sensor nodes to the cluster head. The presented clustering method is not restrictive, whereas existing intelligent clustering methods impose certain conditions such as performing two-tiered clustering. A random deployed model is used to demonstrate the efficiency of the proposed algorithm. In addition, a comparison is made between the presented algorithm other GA-based clustering methods and the Low Energy Adaptive Clustering Hierarchy protocol. The results obtained indicate that using the proposed method, the network's lifetime would be extended much more than it would be when using the other methods. Copyright © 2011 John Wiley & Sons, Ltd.
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paper_title: Evolutionary Genetic Algorithm for Efficient Clustering of Wireless Sensor Networks

paper_content:

Sensor nodes forming a sensor network usually have limited energy capacity so it is important to minimize sensor nodes' energy consumption because of difficulty in supplying additional energy for the sensor nodes. Much attention has been given to the clustering technique as an efficient way of reducing the energy consumption of a sensor node. Energy saving results can vary greatly depending on the number and size of clusters and the distance among the sensor nodes. In this paper, we aim to find an optimal cluster formation by applying a genetic algorithm in which the chromosome contains the information about the relative position of the nodes. The Location-aware two-dimensional GA (LA2D-GA) proposed in this paper can performs more efficient gene evolution than one-dimensional GA (1D-GA) by giving unique location information to each node. The effectiveness of our algorithm is shown by simulation.
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paper_title: A New Clustering Protocol for Wireless Sensor Networks Using Genetic Algorithm Approach

paper_content:

This paper examines the optimization of the lifetime and energy consumption of Wireless Sensor Networks (WSNs). These two competing objectives have a deep influence over the service qualification of networks and according to recent studies, cluster formation is an appropriate solution for their achievement. To transmit aggregated data to the Base Station (BS), logical nodes called Cluster Heads (CHs) are required to relay data from the fixed-range sensing nodes located in the ground to high altitude aircraft. This study investigates the Genetic Algorithm (GA) as a dynamic technique to find optimum states. It is a simple framework that includes a proposed mathematical formula, which increasing in coverage is benchmarked against lifetime. Finally, the implementation of the proposed algorithm indicates a better efficiency compared to other simulated works.
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paper_title: Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms

paper_content:

In trying to solve multiobjective optimization problems, many traditional methods scalarize the objective vector into a single objective. In those cases, the obtained solution is highly sensitive to the weight vector used in the scalarization process and demands that the user have knowledge about the underlying problem. Moreover, in solving multiobjective problems, designers may be interested in a set of Pareto-optimal points, instead of a single point. Since genetic algorithms (GAs) work with a population of points, it seems natural to use GAs in multiobjective optimization problems to capture a number of solutions simultaneously. Although a vector evaluated GA (VEGA) has been implemented by Schaffer and has been tried to solve a number of multiobjective problems, the algorithm seems to have bias toward some regions. In this paper, we investigate Goldberg's notion of nondominated sorting in GAs along with a niche and speciation method to find multiple Pareto-optimal points simultaneously. The proof-of-principle results obtained on three problems used by Schaffer and others suggest that the proposed method can be extended to higher dimensional and more difficult multiobjective problems. A number of suggestions for extension and application of the algorithm are also discussed.
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paper_title: Wireless sensor networks: A survey on the state of the art and the 802.15.4 and ZigBee standards

paper_content:

Wireless sensor networks are an emerging technology for low-cost, unattended monitoring of a wide range of environments. Their importance has been enforced by the recent delivery of the IEEE 802.15.4 standard for the physical and MAC layers and the forthcoming ZigBee standard for the network and application layers. The fast progress of research on energy efficiency, networking, data management and security in wireless sensor networks, and the need to compare with the solutions adopted in the standards motivates the need for a survey on this field.
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paper_title: Node Deployment in Large Wireless Sensor Networks: Coverage, Energy Consumption, and Worst-Case Delay

paper_content:

Node deployment is a fundamental issue to be solved in Wireless Sensor Networks (WSNs). A proper node deployment scheme can reduce the complexity of problems in WSNs as, for example, routing, data fusion, communication, etc. Furthermore, it can extend the lifetime of WSNs by minimizing energy consumption. In this paper, we investigate random and deterministic node deployments for large-scale WSNs under the following performance metrics: coverage, energy consumption, and message transfer delay. We consider three competitors: a uniform random, a square grid, and a pattern-based Tri-Hexagon Tiling (THT) node deployment. A simple energy model is formulated to study energy consumption for each deployment strategy. Using basic geometry we propose a novel strategy for calculating the relative frequency of exactly k-covered points, which uses k-coverage maps, for both a square grid and THT. To model and consequently control the worst-case delay of a given WSN we build upon the so-called sensor network calculus (a recent methodology introduced in [7]). Finally, we analyze tradeoffs between these performance metrics for each deployment strategy to show which strategy is preferable under what factors, e.g., the number of nodes.
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paper_title: An Evolutionary Algorithm to a Multi-Objective Deployment and Power Assignment Problem in Wireless Sensor Networks

paper_content:

Wireless sensor networks design requires high quality location assignment and energy efficient power assignment for maximizing the network coverage and lifetime. Classical deployment and power assignment approaches optimize these two objectives individually or by combining them together in a single objective or by constraining one and optimizing the other. In this article a multi-objective deployment and power assignment problem (DPAP) is formulated and a multi-objective evolutionary algorithm based on decomposition (MOEA/D) is specialized. Following the MOEA/D's framework the above multiobjective optimization problem (MOP) is decomposed into many scalar single objective problems. The sub-problems are solved simultaneously by using neighborhood information. Additionally, unique problem-specific, parameter-rising, genetic operators and local search heuristics were designed specifically for the DPAP. In addition, a new encoding scheme is designed to represent a WSN based on the DPAP's design variables. Simulation results show that MOEA/D provides a high quality set of alternative solutions without any prior knowledge on the objectives preference.
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paper_title: Genetic Algorithm Based Node Placement Methodology For Wireless Sensor Networks

paper_content:

Abstract — A Genetic Algorithm based multi-objective methodology was implemented for a self-organizing wireless sensor network. Design parameters such as network density, connectivity and energy consumption are taken into account for developing the fitness function. The genetic algorithm optimizes the operational modes of the sensor nodes along with clustering schemes and transmission signal strengths. The algorithm has been implemented in MATLAB using its Genetic Algorithm toolbox along with custom codes. The optimal designs so achieved by the algorithm conform to all the design parameters. Index Terms – Genetic Algorithms, Network Configuration , Sensor Placement, Wireless Sensor Networks. I. I NTRODUCTION A dvancements in technologies such as Sensing, Electronics and Computing have attracted tremendous research interest in the field of Wireless Sensor Networks (WSNs), apart from their enormous potential for both commercial and military applications. A WSN generally consists of a large number of low-cost, low-power, multifunctional, energy constrained sensor nodes with limited computational and communication capabilities [1]. In WSNs sensors may be deployed either randomly or deterministically depending upon the application [2]. Deployment in a battlefield or hazardous areas is generally random, whereas a deterministic deployment is preferred in amicable environments. In general a deterministic placement requires fewer sensor nodes than the random deployment to perform the same task. Network lifetime is one of the important parameters to optimize as energy resources in a WSN are limited due to operation on battery. Replacing or recharging of battery in the network may be infeasible. Though the overall function of the
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paper_title: Energy efficient coverage control in wireless sensor networks based on multi-objective genetic algorithm

paper_content:

Due to the constrained energy and computational resources available to sensor nodes, the number of nodes deployed to cover the whole monitored area completely is often higher than if a deterministic procedure were used. Activating only the necessary number of sensor nodes at any particular moment is an efficient way to save the overall energy of the system. A novel coverage control scheme based on multi-objective genetic algorithm is proposed in this paper. The minimum number of sensors is selected in a densely deployed environment while preserving full coverage. As opposed to the binary detection sensor model in the previous work, a more precise detection model is applied in combination with the coverage control scheme. Simulation results show that our algorithm can achieve balanced performance on different types of detection sensor models while maintaining high coverage rate. With the same number of deployed sensors, our scheme compares favorably with the existing schemes.
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paper_title: A multi-objective genetic algorithm based approach for energy efficient QoS-routing in two-tiered Wireless Sensor Networks

paper_content:

With the growing demand for real time services in Wireless Sensor Networks (WSNs), quality of service (QoS) based routing has emerged as an interesting research topic. But offering some QoS guarantee in sensor networks raises significant challenges. The network needs to cope with battery constraints, while providing QoS (end-to-end delay and reliability) guarantees. Designing such QoS routing protocols that optimize multiple objectives is computationally intractable. Higher power relay nodes can be used as cluster heads in a two-tiered WSN and these relay nodes may form a network among themselves to route data towards the sink. In this model, the QoS guarantee is determined mainly by these relay nodes. In this paper a solution based on NSGA-II is proposed for energy efficient QoS routing in cluster based WSNs. Simulation results demonstrate that the proposed protocol outperforms network performance by optimizing multiple QoS parameters and energy consumption.
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paper_title: GAR: An Energy Efficient GA-Based Routing for Wireless Sensor Networks

paper_content:

Routing with energy consideration has paid enormous attention in the field of Wireless Sensor Networks (WSNs). In Some WSNs, some high energy sensors called relay nodes are responsible to route the data towards a base station. Reducing energy consumption of these relay nodes allow us to prolong the lifetime and coverage of the WSN. In this paper, we present a Genetic algorithm based routing scheme called GAR (Genetic Algorithm-based Routing) that considers the energy consumption issues by minimizing the total distance travelled by the data in every round. Our GA based approach can quickly compute a new routing schedule based on the current network state. The scheme uses the advantage of computational efficiency of GA to quickly find out a solution to the problem. The experimental results demonstrate that the proposed algorithm is better than the existing techniques in terms of network life time, energy consumption and the total distance covered in each round.
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paper_title: CRCWSN: Presenting a Routing Algorithm by using Re-clustering to Reduce Energy Consumption in WSN

paper_content:

In this paper, we have presented an algorithm, based on genetics and re-clustering, to reduce energy consumption in Wireless Sensor Networks. Algorithm CRCWSN could be best used by selected chromosomes in different states. In this algorithm, a new technique of selecting cluster head(CH) has been initially used by genetic algorithm. These CHs have been used individually in each round to transmit data. In this research, considering distance and energy parameters, we have created a target function having more optimum conditions, compared to previous techniques. The created target function has been evaluated by input chromosome, and the combination of chromosomes has been done by a new technique having more efficiency compared to previous similar techniques. Consequently, the timing of generation repeat is based on local distribution in chromosomes, and their using in sending data from source to destination that decrease generations’ repeat, compared to previous methods. Results by simulation show that, at the end of each round, the number of alive nodes in the suggested algorithm increases, compared to previous methods, which increases network’s lifetime.
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paper_title: A Minimum Hop Routing Protocol for Home Security Systems Using Wireless Sensor Networks

paper_content:

A wireless sensor network is a data communication system that consists of from several to thousands of tiny wireless sensor nodes. These battery-powered sensor nodes cooperate with each other to accomplish data transmission. A variety of wireless sensor networks have been developed for different applications in the recent years. In this paper, a data routing protocol, which is designed for a wireless home security network, is proposed. Due to the lifetime of such a network depends entirely on the lifetime of the battery power, the proposed routing protocol chooses hop counts and battery power levels as metrics, in order to conserve as much energy as possible in both computations and data communications. In addition, when some of the nodes fail or run out of battery, the routing protocol could effectively adapt the change and find an alternative path. The robustness and the energy efficiency of the proposed routing protocol were tested, and the results turned out to be successful.
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paper_title: Genetic Algorithm based Multicast Routing in Wireless Sensor Networks - A Research Framework

paper_content:

Wireless sensor networks contain hundreds or thousands of small sensor nodes. These sensors are capable of communicating either among one another or directly to an external base station. Multicast based routing methods are challenging tasks in a wireless sensor network. It is due to various factors such as energy efficiency, QoS etc. Therefore it is important to have methods based on genetic algorithms. Though sufficient works have been done on the survey of genetic algorithm and multicasting based routing in wireless sensor networks, this paper presents an updated survey of genetic algorithm based multicast routing as a classification. Localization, mobility, query based, energy efficiency, data aggregation and QoS are the metrics used for the genetic algorithm based multicast routing in wireless sensor networks classification. Finally, a comparative study of all genetic algorithm based multicast routing techniques in wireless sensor networks is presented.
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paper_title: Maximizing the Lifetime of Two-Tiered Sensor Networks

paper_content:

Recent technological advances in the field of micro-electroc-mechanical systems (MEMS) have made the development of multi-functional sensor nodes technically and economically feasible. The lifetime of sensor networks is still limited as individual sensor nodes are usually powered by battery. In the past few years, the use of relay nodes in sensor networks has been proposed in literature for balanced data gathering, reduction of transmission range, connectivity and fault tolerance. In hierarchical sensor networks, higher-powered relay nodes can also be used as cluster heads. These relay nodes may form a network among themselves and route data towards the base station. In such a sensor network, the lifetime of the network is directly related to the lifetime of these relay nodes. In this paper, we have proposed an ILP solution for scheduling the data gathering of relay nodes such that the lifetime of the relay node network is maximized. We have compared our formulation with the direct transmit energy model and shown that it can lead to significant improvements. We have also proposed a re-scheduling approach which can further extend the maximized lifetime of the network.
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paper_title: Finding Agent-Based Energy-Efficient Routing in Sensor Networks using Parallel Genetic Algorithm

paper_content:

Recent advances in wireless sensor networks have led us to search for new routing protocols specifically those designed for sensor networks where energy awareness is an essential consideration. In this paper, a modified cost-energy (MCE) combined scheme for energy efficient routing in sensor networks has been proposed. We have added a parameter to CE combined scheme and obtained better results. We have used parallel genetic algorithm (PGA) to determine the parameters of MCE combined scheme. By using the proposed scheme, due to lower power consumption, the life time of the sensor network is prolonged. Simulation results show about 40% improvement in "average energy consumption per agent" of the network comparing MCE combined scheme with other schemes discussed in this paper.
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paper_title: A quantum genetic algorithm based QoS routing protocol for wireless sensor networks

paper_content:

In wireless sensor networks, a large amount of existing routing protocols consider energy efficiency as the main objective and assume data traffic with unconstrained delivery requirements. However, the introduction of image and video sensors demands certain quality of service (QoS) from the routing protocols and underlying networks. Finding routing paths that satisfy different metrics of the QoS requirement is a NP-complete problem. In this paper, a quantum genetic algorithm based energy-aware QoS routing protocol (QG-QoS) is proposed for wireless sensor networks, which can run efficiently with best-effort traffic. To the best of our knowledge, QG-QoS is the first quantum genetic algorithm based QoS routing protocol in wireless sensor networks. Simulation results show that our approach is better than previous protocols for different metrics.
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paper_title: A genetic algorithm based approach for energy efficient routing in two-tiered sensor networks

paper_content:

Higher power relay nodes can be used as cluster heads in two-tiered sensor networks to achieve improved network lifetime. The relay nodes may form a network among themselves to route data towards the base station. In this model, the lifetime of a network is determined mainly by the lifetimes of these relay nodes. An energy-aware communication strategy can greatly extend the lifetime of such networks. However, integer linear program (ILP) formulations for optimal, energy-aware routing quickly become computationally intractable and are not suitable for practical networks. In this paper, we have proposed an efficient solution, based on a genetic algorithm (GA), for scheduling the data gathering of relay nodes, which can significantly extend the lifetime of a relay node network. For smaller networks, where the global optimum can be determined, our GA based approach is always able to find the optimal solution. Furthermore, our algorithm can easily handle large networks, where it leads to significant improvements compared to traditional routing schemes.
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paper_title: A Tree Based Data Aggregation Scheme for Wireless Sensor Networks Using GA

paper_content:

Energy is one of the most important items to determine the network lifetime due to low power energy nodes included in the network. Generally, data aggregation tree concept is used to find an energy efficient solution. However, even the best aggregation tree does not share the load of data packets to the transmitting nodes fairly while it is consuming the lowest possible energy of the network. Therefore, after some rounds, this problem causes to consume the whole energy of some heavily loaded nodes and hence results in with the death of the network. In this paper, by using the Genetic Algorithm (GA), we investigate the energy efficient data collecting spanning trees to find a suitable route which balances the data load throughout the network and thus balances the residual energy in the network in addition to consuming totally low power of the network. Using an algorithm which is able to balance the residual energy among the nodes can help the network to withstand more and consequently extend its own lifetime. In this work, we calculate all possible routes represented by the aggregation trees through the genetic algorithm. GA finds the optimum tree which is able to balance the data load and the energy in the network. Simulation results show that this balancing operation practically increases the network lifetime.
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paper_title: Genetic algorithm for data aggregation trees in wireless sensor networks

paper_content:

This paper uses a genetic algorithm (GA) to generate balanced and energy efficient data aggregation spanning trees for wireless sensor networks. In a data gathering round, a single best tree consumes lowest energy from all nodes but assigns more load to some sensors. As a result, the energy resources of heavily loaded nodes will be depleted earlier than others. Therefore, we use a collection of trees that balances load among nodes and consumes less energy. Our proposed GA takes these two issues in generating aggregation trees. The GA is simulated in an open source simulator, J-sim. The simulation results show that proposed GA outperforms a few other data aggregation tree-based approaches in terms of extending network lifetime.
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paper_title: Data Aggregation and Routing In Wireless Sensor Networks: Optimal and Heuristic Algorithms

paper_content:

A fundamental challenge in the design of Wireless Sensor Networks (WSNs) is to maximize their lifetimes especially when they have a limited and non replenishable energy supply. To extend the network lifetime, power management and energy-efficient communication techniques at all layers become necessary. In this paper, we present solutions for the data gathering and routing problem with in-network aggregation in WSNs. Our objective is to maximize the network lifetime by utilizing data aggregation and in- network processing techniques. We particularly focus on the joint problem of optimal data routing with data aggregation en route such that the above mentioned objective is achieved. We present Grid-based Routing and Aggregator Selection Scheme (GRASS), a scheme for WSNs that can achieve low energy dissipation and low latency without sacrificing quality. GRASS embodies optimal (exact) as well as heuristic approaches to find the minimum number of aggregation points while routing data to the Base Station (BS) such that the network lifetime is maximized. Our results show that, when compared to other schemes, GRASS improves system lifetime with acceptable levels of latency in data aggregation and without sacrificing data quality.
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paper_title: Genetic Algorithm Based Node Placement Methodology For Wireless Sensor Networks

paper_content:

Abstract — A Genetic Algorithm based multi-objective methodology was implemented for a self-organizing wireless sensor network. Design parameters such as network density, connectivity and energy consumption are taken into account for developing the fitness function. The genetic algorithm optimizes the operational modes of the sensor nodes along with clustering schemes and transmission signal strengths. The algorithm has been implemented in MATLAB using its Genetic Algorithm toolbox along with custom codes. The optimal designs so achieved by the algorithm conform to all the design parameters. Index Terms – Genetic Algorithms, Network Configuration , Sensor Placement, Wireless Sensor Networks. I. I NTRODUCTION A dvancements in technologies such as Sensing, Electronics and Computing have attracted tremendous research interest in the field of Wireless Sensor Networks (WSNs), apart from their enormous potential for both commercial and military applications. A WSN generally consists of a large number of low-cost, low-power, multifunctional, energy constrained sensor nodes with limited computational and communication capabilities [1]. In WSNs sensors may be deployed either randomly or deterministically depending upon the application [2]. Deployment in a battlefield or hazardous areas is generally random, whereas a deterministic deployment is preferred in amicable environments. In general a deterministic placement requires fewer sensor nodes than the random deployment to perform the same task. Network lifetime is one of the important parameters to optimize as energy resources in a WSN are limited due to operation on battery. Replacing or recharging of battery in the network may be infeasible. Though the overall function of the
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paper_title: A TDMA scheduling scheme for many-to-one communications in wireless sensor networks

paper_content:

In wireless sensor networks, time division multiple access (TDMA)-based MAC can potentially reduce the delay and provide real-time guarantees as well as save power by eliminating collisions. In this kind of MAC, a common energy-saving strategy is to allow the sensors to turn their radio off when not engaged. However, too much state transitions between the active and sleep modes would also waste energy. In order to save this part of energy and further improve the time performance, a multi-objective TDMA scheduling problem for many-to-one sensor networks is presented. An effective optimization framework is then proposed, where genetic algorithm (GA) and particle swarm optimization (PSO) algorithm are hybridized to enhance the searching ability. Simulation results with different network sizes are given. Three algorithms are used for comparisons, which are PSO algorithm, max degree first coloring algorithm and node based scheduling algorithm. It is shown that the proposed hybrid algorithm is superior over these three algorithms on a specified objective, which can be the total time or the total energy for data collection. In addition, the results reveal that the proposed optimization framework can flexibly deal with a multi-objective optimization problem.
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paper_title: Adaptive Clustering for Energy Efficient Wireless Sensor Networks Based on Ant Colony Optimization

paper_content:

Energy supply in wireless sensor networks (WSNs) is limited and non-replenishable, and energy efficiency is the most important feature in designing these networks. One way to reduce the energy consumption of WSNs and hence prolong the lifespan of these networks is to use adaptive clustering algorithms. In this paper, we use Ant Colony Optimization for Clustering (ACO-C) to propose a new energy aware clustering protocol for WSNs. By using appropriate cost functions, implemented at the base station, our algorithm minimizes and distributes the cost of long distance transmissions and data aggregating among all sensor nodes evenly. Simulation results show the effectiveness of our protocol over other well known clustering algorithms in terms of both network lifetime and data delivery to the base station.
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paper_title: 2ASenNet: A multiple QoS metrics hierarchical routing protocol based on swarm intelligence optimization for WSN

paper_content:

The increasing demand for real-time application in wireless sensor network (WSN) has made the Quality of Service (QoS) based communication protocols a hot research topic. Most routing protocols consider energy saving as the main objective and assume data traffic with unconstrained delivery requirements to be a given. However, with the arrival of wireless multimedia sensor networks, the transmission of imaging and video information requires both energy efficiency and QoS assurance (e.g. bandwidth, packet loss and delay constraints). Swarm intelligence algorithms are the relatively effective strategy for distributed optimization and show the desirable properties of being adaptive, scalable, and robust in particular of routing in WSN. In this paper, In order to achieve the balancing of energy-efficiency with QoS improvements for applications specific, a multiple QoS metrics hierarchical routing protocol based on swarm intelligence optimization (combination of improving ACO and AFSO) for WSN is proposed, namely 2ASenNet. The 2ASenNet protocol built a hierarchical structure on the network before choosing suitable paths to meet various QoS requirements. Then, the 2ASenNet protocol adopted hybrid ant behavior to produce diverse original paths, adding AFSO to ACO's per iterative process, and the optimization path was explored according to multiple QoS constrained. Simulation results have shown the efficiency of this novel approach, while simultaneously reducing the consumption of constrained resources as much as possible.
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paper_title: Genetic algorithm for hierarchical wireless sensor networks

paper_content:

Large scale wireless sensor networks (WSNs) can be used for various pervasive and ubiquitous applications such as security, health-care, industry automation, agriculture, environment and habitat monitoring. As hierarchical clusters can reduce the energy consumption requirements for WSNs, we investigate intelligent techniques for cluster formation and management. A genetic algorithm (GA) is used to create energy efficient clusters for data dissemination in wireless sensor networks. The simulation results show that the proposed intelligent hierarchical clustering technique can extend the network lifetime for different network deployment environments.
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paper_title: Extending wireless sensor network lifetime with base station repositioning

paper_content:

In wireless sensor networks, the major source of a sensor node failure is battery exhaustion and replacing this energy source in the field is usually not practical. Therefore, the use of energy efficient infrastructure, such as repositioning the base station in a clustered wireless sensor network, is able to prolong the lifetime of the network and improve the overall network data. In this paper, we proposed an energy-efficient protocol for the repositioning of mobile base station using particle swarm optimization (PSO) in wireless sensor networks. Simulation results demonstrate that the proposed protocol can improve the network lifetime, data delivery and energy consumption compared to existing energy-efficient protocols developed for this network.
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paper_title: An improved fuzzy unequal clustering algorithm for wireless sensor network

paper_content:

This paper proposes a novel energy efficient unequal clustering scheme for large scale wireless sensor networks(WSNs) which aims to balance the node power consumption and prolong the network lifetime as long as possible. Our approach focuses on energy efficient clustering scheme and inter-cluster routing protocol. On the one hand, considering each node's some local information such as energy level, distance to base station and local density, we use fuzzy logic system to determine one node's chance of becoming cluster head and estimate the cluster-head radius. On the other hand, the application of Ant Colony Optimization (ACO) to inter-cluster routing constructs energy-aware routing between cluster heads and base station, which reduces the energy consumption of cluster heads and solves the hot spots problem that occurs in multi-hop WSN routing protocol to a large extent. The confirmation experiment results have indicated the proposed clustering scheme has more superior performance than other methods such as LEACH and EEUC.
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paper_title: Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms

paper_content:

In trying to solve multiobjective optimization problems, many traditional methods scalarize the objective vector into a single objective. In those cases, the obtained solution is highly sensitive to the weight vector used in the scalarization process and demands that the user have knowledge about the underlying problem. Moreover, in solving multiobjective problems, designers may be interested in a set of Pareto-optimal points, instead of a single point. Since genetic algorithms (GAs) work with a population of points, it seems natural to use GAs in multiobjective optimization problems to capture a number of solutions simultaneously. Although a vector evaluated GA (VEGA) has been implemented by Schaffer and has been tried to solve a number of multiobjective problems, the algorithm seems to have bias toward some regions. In this paper, we investigate Goldberg's notion of nondominated sorting in GAs along with a niche and speciation method to find multiple Pareto-optimal points simultaneously. The proof-of-principle results obtained on three problems used by Schaffer and others suggest that the proposed method can be extended to higher dimensional and more difficult multiobjective problems. A number of suggestions for extension and application of the algorithm are also discussed.
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paper_title: Genetic Algorithm for Energy Efficient Clusters in Wireless Sensor Networks

paper_content:

We use a genetic algorithm (GA) to create energy efficient clusters for routing in wireless sensor networks. The simulation results show that the proposed intelligent hierarchical clustering technique is more energy efficient than a few existing cluster-based routing protocols. Further, the gradual energy depletion in sensor nodes is also investigated
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paper_title: An optimal energy-efficient clustering method in wireless sensor networks using multi-objective genetic algorithm

paper_content:

In this study, an optimal method of clustering homogeneous wireless sensor networks using a multi-objective two-nested genetic algorithm is presented. The top level algorithm is a multi-objective genetic algorithm (GA) whose goal is to obtain clustering schemes in which the network lifetime is optimized for different delay values. The low level GA is used in each cluster in order to get the most efficient topology for data transmission from sensor nodes to the cluster head. The presented clustering method is not restrictive, whereas existing intelligent clustering methods impose certain conditions such as performing two-tiered clustering. A random deployed model is used to demonstrate the efficiency of the proposed algorithm. In addition, a comparison is made between the presented algorithm other GA-based clustering methods and the Low Energy Adaptive Clustering Hierarchy protocol. The results obtained indicate that using the proposed method, the network's lifetime would be extended much more than it would be when using the other methods. Copyright © 2011 John Wiley & Sons, Ltd.
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paper_title: Sensor Deployment of Wireless Sensor Networks Based on Ant Colony Optimization with Three Classes of Ant Transitions

paper_content:

The problem of minimum-cost and connectivity- guaranteed grid coverage (MCGC) is one of the most critical issues for the implementation of wireless sensor networks (WSNs). In this paper, a novel algorithm, ant colony optimization with three classes of ant transitions (ACO-TCAT) is proposed to decrease inferior solutions and narrow the searching range of the algorithm and finally to solve this problem. Simulation results are conducted to demonstrate the effectiveness of our proposed approach.
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paper_title: GAR: An Energy Efficient GA-Based Routing for Wireless Sensor Networks

paper_content:

Routing with energy consideration has paid enormous attention in the field of Wireless Sensor Networks (WSNs). In Some WSNs, some high energy sensors called relay nodes are responsible to route the data towards a base station. Reducing energy consumption of these relay nodes allow us to prolong the lifetime and coverage of the WSN. In this paper, we present a Genetic algorithm based routing scheme called GAR (Genetic Algorithm-based Routing) that considers the energy consumption issues by minimizing the total distance travelled by the data in every round. Our GA based approach can quickly compute a new routing schedule based on the current network state. The scheme uses the advantage of computational efficiency of GA to quickly find out a solution to the problem. The experimental results demonstrate that the proposed algorithm is better than the existing techniques in terms of network life time, energy consumption and the total distance covered in each round.
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paper_title: A Particle Swarm Optimization based approach for the maximum coverage problem in cellular base stations positioning

paper_content:

The demand for cellular systems has grown in recent years and sometimes it is not an easy task to design such systems. We propose in this paper a new approach to tackle the maximum coverage problem in cellular systems using Particle Swarm Optimization (PSO). We adapted the PSO since we have associated the position of the base stations to the particles. We also developed two mechanisms to avoid overllaping among the cells and to maximize the coverage of the entire system. We tested our approach in two scenarios in different configurations. We believe that the results are interesting and with future works we can create a commercial tool to solve the real problem.
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paper_title: Distributed sensor placement with sequential particle swarm optimization

paper_content:

Sequential particle swarm optimization (S-PSO) is a modification of PSO suitable for high-dimensional optimization problems. S-PSO iteratively optimizes the objective function over randomly selected subspaces of the parameter search space instead of the entire parameter space at once as in standard PSO. This approach is advantageous since fewer particles are needed to solve the lower-dimension subproblems. S-PSO is applied to the distributed sonar sensor placement problem, where not only the dimensionality issue arises, but also the computational complexity of the objective function increases with the problem size. Simulations show that S-PSO outperforms standard PSO both in terms of convergence and computational efficiency.
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paper_title: Efficient deployment algorithms for ensuring coverage and connectivity of wireless sensor networks

paper_content:

Sensor deployment is a critical issue since it reflects the cost and detection capability of a wireless sensor network. Although lots of work has addressed this issue, most of them assume that the sensing field is an open space and there is a special relationship between the communication range and sensing range of sensors. In this work, we consider the sensing field as an arbitrary-shaped region possibly with obstacles. Besides, we allow an arbitrary relationship between the communication range and sensing range, thus eliminating the constraints of existing results. Our approach is to partition the sensing field into smaller sub-regions based on the shape of the field, and then to deploy sensors in these sub-regions. Simulation results show that our method requires fewer sensors compared to existing results.
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paper_title: An energy-efficient unequal clustering mechanism for wireless sensor networks

paper_content:

Clustering provides an effective way for prolonging the lifetime of a wireless sensor network. Current clustering algorithms usually utilize two techniques, selecting cluster heads with more residual energy and rotating cluster heads periodically, to distribute the energy consumption among nodes in each cluster and extend the network lifetime. However, they rarely consider the hot spots problem in multihop wireless sensor networks. When cluster heads cooperate with each other to forward their data to the base station, the cluster heads closer to the base station are burdened with heavy relay traffic and tend to die early, leaving areas of the network uncovered and causing network partition. To address the problem, we propose an energy-efficient unequal clustering (EEUC) mechanism for periodical data gathering in wireless sensor networks. It partitions the nodes into clusters of unequal size, and clusters closer to the base station have smaller sizes than those farther away from the base station. Thus cluster heads closer to the base station can preserve some energy for the inter-cluster data forwarding. We also propose an energy-aware multihop routing protocol for the inter-cluster communication. Simulation results show that our unequal clustering mechanism balances the energy consumption well among all sensor nodes and achieves an obvious improvement on the network lifetime
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paper_title: A Minimum Hop Routing Protocol for Home Security Systems Using Wireless Sensor Networks

paper_content:

A wireless sensor network is a data communication system that consists of from several to thousands of tiny wireless sensor nodes. These battery-powered sensor nodes cooperate with each other to accomplish data transmission. A variety of wireless sensor networks have been developed for different applications in the recent years. In this paper, a data routing protocol, which is designed for a wireless home security network, is proposed. Due to the lifetime of such a network depends entirely on the lifetime of the battery power, the proposed routing protocol chooses hop counts and battery power levels as metrics, in order to conserve as much energy as possible in both computations and data communications. In addition, when some of the nodes fail or run out of battery, the routing protocol could effectively adapt the change and find an alternative path. The robustness and the energy efficiency of the proposed routing protocol were tested, and the results turned out to be successful.
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paper_title: Wireless sensor networks: A survey on the state of the art and the 802.15.4 and ZigBee standards

paper_content:

Wireless sensor networks are an emerging technology for low-cost, unattended monitoring of a wide range of environments. Their importance has been enforced by the recent delivery of the IEEE 802.15.4 standard for the physical and MAC layers and the forthcoming ZigBee standard for the network and application layers. The fast progress of research on energy efficiency, networking, data management and security in wireless sensor networks, and the need to compare with the solutions adopted in the standards motivates the need for a survey on this field.
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paper_title: Genetic Algorithm based Multicast Routing in Wireless Sensor Networks - A Research Framework

paper_content:

Wireless sensor networks contain hundreds or thousands of small sensor nodes. These sensors are capable of communicating either among one another or directly to an external base station. Multicast based routing methods are challenging tasks in a wireless sensor network. It is due to various factors such as energy efficiency, QoS etc. Therefore it is important to have methods based on genetic algorithms. Though sufficient works have been done on the survey of genetic algorithm and multicasting based routing in wireless sensor networks, this paper presents an updated survey of genetic algorithm based multicast routing as a classification. Localization, mobility, query based, energy efficiency, data aggregation and QoS are the metrics used for the genetic algorithm based multicast routing in wireless sensor networks classification. Finally, a comparative study of all genetic algorithm based multicast routing techniques in wireless sensor networks is presented.
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paper_title: Evolutionary Genetic Algorithm for Efficient Clustering of Wireless Sensor Networks

paper_content:

Sensor nodes forming a sensor network usually have limited energy capacity so it is important to minimize sensor nodes' energy consumption because of difficulty in supplying additional energy for the sensor nodes. Much attention has been given to the clustering technique as an efficient way of reducing the energy consumption of a sensor node. Energy saving results can vary greatly depending on the number and size of clusters and the distance among the sensor nodes. In this paper, we aim to find an optimal cluster formation by applying a genetic algorithm in which the chromosome contains the information about the relative position of the nodes. The Location-aware two-dimensional GA (LA2D-GA) proposed in this paper can performs more efficient gene evolution than one-dimensional GA (1D-GA) by giving unique location information to each node. The effectiveness of our algorithm is shown by simulation.
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paper_title: Node Deployment in Large Wireless Sensor Networks: Coverage, Energy Consumption, and Worst-Case Delay

paper_content:

Node deployment is a fundamental issue to be solved in Wireless Sensor Networks (WSNs). A proper node deployment scheme can reduce the complexity of problems in WSNs as, for example, routing, data fusion, communication, etc. Furthermore, it can extend the lifetime of WSNs by minimizing energy consumption. In this paper, we investigate random and deterministic node deployments for large-scale WSNs under the following performance metrics: coverage, energy consumption, and message transfer delay. We consider three competitors: a uniform random, a square grid, and a pattern-based Tri-Hexagon Tiling (THT) node deployment. A simple energy model is formulated to study energy consumption for each deployment strategy. Using basic geometry we propose a novel strategy for calculating the relative frequency of exactly k-covered points, which uses k-coverage maps, for both a square grid and THT. To model and consequently control the worst-case delay of a given WSN we build upon the so-called sensor network calculus (a recent methodology introduced in [7]). Finally, we analyze tradeoffs between these performance metrics for each deployment strategy to show which strategy is preferable under what factors, e.g., the number of nodes.
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paper_title: Enhanced Ant Colony algorithm for self-optimized data assured routing in wireless sensor networks

paper_content:

Wireless sensor network (WSN) deploys tiny wireless sensor nodes to communicate with each other with limited processing speed, power and security measures. A recent WSN routing protocol defined as Secure Real-Time Load Distribution (SRTLD) has been developed to provide realtime transfer, high delivery ratio, and longer sensor node lifetime. SRTLD has been compared with LQER, MMSpeed, RTPC and RPAR. However, SRTLD uses broadcast packets to perform neighbour discovery for every packet transfer every hop, thus consume high energy. A novel Biological inspired self-organized Secure Autonomous Routing Protocol (BIOSARP) to enhance SRTLD with self-optimized and autonomous secure routing mechanism. The BIOSARP routing protocol depends on the optimal forwarding decision obtained by Ant Colony Optimization (ACO). The pheromone value in ACO is computed based on end-to-end delay, remaining battery power, and packet reception rate metrics similar to SRTLD. The proposed BIOSARP has been designed to reduce overhead broadcast packet in order to minimize the delay, packet loss and power consumption in WSN. In this paper we have presented the improved ACO algorithm that has been utilized in BIOSARP to perform self-optimized routing in WSN. BIOSARP has been studied and verified through simulation in network simulator 2 (NS-2). In simulation study BIOSARP normalized overhead is 12.1% less as compare to E&D ANTS and achieves 14% higher delivery ratio with 9% less power consumption when compared to SRTLD. Hence, the results confirm that BIOSARP offers better performance and can be practically implemented in WSN applications as structural and environmental monitoring, battlefield surveillance.
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paper_title: Maximizing the Lifetime of Two-Tiered Sensor Networks

paper_content:

Recent technological advances in the field of micro-electroc-mechanical systems (MEMS) have made the development of multi-functional sensor nodes technically and economically feasible. The lifetime of sensor networks is still limited as individual sensor nodes are usually powered by battery. In the past few years, the use of relay nodes in sensor networks has been proposed in literature for balanced data gathering, reduction of transmission range, connectivity and fault tolerance. In hierarchical sensor networks, higher-powered relay nodes can also be used as cluster heads. These relay nodes may form a network among themselves and route data towards the base station. In such a sensor network, the lifetime of the network is directly related to the lifetime of these relay nodes. In this paper, we have proposed an ILP solution for scheduling the data gathering of relay nodes such that the lifetime of the relay node network is maximized. We have compared our formulation with the direct transmit energy model and shown that it can lead to significant improvements. We have also proposed a re-scheduling approach which can further extend the maximized lifetime of the network.
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paper_title: Improving sensing coverage of wireless sensor networks by employing mobile robots

paper_content:

To provide proper coverage of their random deployment regions, wireless sensor networks (WSN) should employ abundant nodes. In this paper we correct such situations by employing some mobile robots as mobile nodes in WSN which can actively move to desired locations for repairing the broken networks. According to the pre-research work we know that the number of nodes employed by WSN is closely relevant to quality of service (QoS) in sensing coverage when the nodes are randomly deployed in the target region. A modified particle swarm optimization (PSO) named particle swarm genetic optimization (PSGO), which imports selection and mutation operators in PSO to overcome the premature fault of classical PSO, is proposed to redeploy the mobile robots according to the node density for repairing the sensing coverage hole after their initial random deployment. It is suggested by the simulated experiment results that the WSN employing the mobile robots can improve the QoS in sensing coverage than the stationary WSN by redeploying mobile robots according to the node density.
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paper_title: Data Gathering Communication in Wireless Sensor Networks Using Ant Colony Optimization

paper_content:

This paper introduces a centralized approach to data gathering and communication for wireless sensor networks. Inspired by the social behaviors of ants, we clearly partition the work for the base station and sensor nodes according to their different functions and capabilities. A near-optimal chain is achieved by using an ant colony optimization method ruing in the base station. The sensor nodes in the network then form a bi-direction chain structure, which is self-adaptive to any minor changes. The simulation results show that the developed AntChain algorithm performs much better than the LEACH and PEGASIS methods, in terms of energy-efficiency, data integrity and life time, when the base station
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paper_title: Optimized sink node path using particle swarm optimization

paper_content:

A wireless sensor network (WSN) is comprised of large number of sensors distributed in a monitoring field and a sink node to gather process and control data. The performance of the network depends on the behavior of the sink node and its location. An optimized sink node path will be efficient and economical for operation of the network. In this paper, we propose a novel method to derive the optimum path of a sink node in a fixed network of sensor nodes considering practical difficulties such as the limitation in the sink movement. The proposed evolutionary computing technique based simulator PSO-SIMSENS is an integrated system of particle swarm optimization and a sensor network simulator with an appropriate fitness function. This system can be configured for numerous applications such as manufacturing, bush fire monitoring etc. The simulation results show that our approach achieves efficient performance of WSN with maximum field coverage while sink node is mobile.
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paper_title: A quantum genetic algorithm based QoS routing protocol for wireless sensor networks

paper_content:

In wireless sensor networks, a large amount of existing routing protocols consider energy efficiency as the main objective and assume data traffic with unconstrained delivery requirements. However, the introduction of image and video sensors demands certain quality of service (QoS) from the routing protocols and underlying networks. Finding routing paths that satisfy different metrics of the QoS requirement is a NP-complete problem. In this paper, a quantum genetic algorithm based energy-aware QoS routing protocol (QG-QoS) is proposed for wireless sensor networks, which can run efficiently with best-effort traffic. To the best of our knowledge, QG-QoS is the first quantum genetic algorithm based QoS routing protocol in wireless sensor networks. Simulation results show that our approach is better than previous protocols for different metrics.
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paper_title: An energy efficient routing protocol for cluster-based wireless sensor networks using ant colony optimization

paper_content:

In this paper, we propose an efficient routing algorithm for large scale cluster-based wireless sensor networks. The technique uses two routing levels. In the first level (intra-cluster), cluster members send data directly to their cluster head. In the second level (inter-cluster), the cluster heads use ant colony optimization (ACO) algorithm, which is a biologically inspired paradigm for optimization approach, to find a route to the base station. As only cluster heads participate in the inter-cluster routing operation, the method can provide a smooth operation more effectively. The delay of the algorithm is minimized by using the ant colony optimization algorithm along with clustering. To assess the efficiency of the proposed algorithm, we compare the method with some previous routing algorithms. The results show lower power consumption and more load balancing.
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paper_title: An Improved Co-evolutionary Particle Swarm Optimization for Wireless Sensor Networks with Dynamic Deployment

paper_content:

The effectiveness of wireless sensor networks (WSNs) depends on the coverage and target detection probability provided by dynamic deployment, which is usually supported by the virtual force (VF) algorithm. However, in the VF algorithm, the virtual force exerted by stationary sensor nodes will hinder the movement of mobile sensor nodes. Particle swarm optimization (PSO) is introduced as another dynamic deployment algorithm, but in this case the computation time required is the big bottleneck. This paper proposes a dynamic deployment algorithm which is named “virtual force directed co-evolutionary particle swarm optimization” (VFCPSO), since this algorithm combines the co-evolutionary particle swarm optimization (CPSO) with the VF algorithm, whereby the CPSO uses multiple swarms to optimize different components of the solution vectors for dynamic deployment cooperatively and the velocity of each particle is updated according to not only the historical local and global optimal solutions, but also the virtual forces of sensor nodes. Simulation results demonstrate that the proposed VFCPSO is competent for dynamic deployment in WSNs and has better performance with respect to computation time and effectiveness than the VF, PSO and VFPSO algorithms.
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paper_title: Bio-inspired Algorithms for Autonomous Deployment and Localization of Sensor Nodes

paper_content:

Optimal deployment and accurate localization of sensor nodes have a strong influence on the performance of a wireless sensor network (WSN). This paper considers real-time autonomous deployment of sensor nodes from an unmanned aerial vehicle (UAV). Such a deployment has importance, particularly in ad hoc WSNs, for emergency applications, such as disaster monitoring and battlefield surveillance. The objective is to deploy the nodes only in the terrains of interest, which are identified by segmentation of the images captured by a camera on board the UAV. Bioinspired algorithms, particle swarm optimization (PSO) and bacterial foraging algorithm (BFA), are presented in this paper for image segmentation. In addition, PSO and BFA are presented for distributed localization of the deployed nodes. Image segmentation for autonomous deployment and distributed localization are formulated as multidimensional optimization problems, and PSO and BFA are used as optimization tools. Comparisons of the results of PSO and BFA for autonomous deployment and distributed localization are presented. Simulation results show that both the algorithms perform multilevel image segmentation faster than the exhaustive search for optimal thresholds. Besides, PSO-based localization is observed to be faster, and BFA-based localization is more accurate.
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paper_title: Allocating Multiple Base Stations under General Power Consumption by the Particle Swarm Optimization

paper_content:

In this paper, a two-tiered wireless sensor networks consisting of small sensor nodes, application nodes and base-stations is considered. An algorithm based on particle swarm optimization (PSO) is proposed for multiple base stations under general power-consumption constraints. The proposed approach can search for nearly optimal BS locations in heterogeneous sensor networks, where application nodes may own different data transmission rates, initial energies and parameter values. Experimental results also show the good performance of the proposed PSO approach and the effects of the parameters on the results
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paper_title: Demonstration of a WSN Application in Relic Protection and an Optimized System Deployment Tool

paper_content:

This paper presents an overview of our demonstration of a real application of a WSN system for relic protection applied in the Forbidden City of China. We briefly introduce the EasiNet hardware platform we have developed and the corresponding mesh-architecture of the system constructed. We present a sensor deployment optimization tool based on ant colony optimization technology (DT-ACO) to meet the requirements of network connectivity, sensing coverage and a minimized overall cost of the system. We describe a novel power-aware cross-layer scheme (PACS) to facilitate adjustable system lifetime and surveillance accuracy. We also show some detailed results of the application case.
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paper_title: Ant colony optimization based sensor deployment protocol for wireless sensor networks

paper_content:

Research highlights? We consider the problem of sensor deployment to achieve complete coverage of the service region and maximize the lifetime of the network. ? We model the deployment problem as the multiple knapsack problem. ? Based on ACO algorithm, we proposed a deployment scheme to prolong the network lifetime, while ensuring complete coverage of the service region. Sensor deployment is one of the most important issues in wireless sensor networks, because an efficient deployment scheme can reduce the deployment cost and enhance the detection capability of the wireless sensor networks. In addition, it can enhance the quality of monitoring in wireless sensor networks by increasing the coverage area. Ant colony optimization (ACO) algorithm provides a natural and intrinsic way of exploration of search space for multiple knapsack problem (MKP). In this work, we consider the problem of sensor deployment to achieve complete coverage of the service region and maximize the lifetime of the network. We model the deployment problem as the multiple knapsack problem. Based on ACO algorithm, we proposed a deployment scheme to prolong the network lifetime, while ensuring complete coverage of the service region. The simulations show that our algorithm can prolong the lifetime of the network.
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paper_title: A genetic algorithm based approach for energy efficient routing in two-tiered sensor networks

paper_content:

Higher power relay nodes can be used as cluster heads in two-tiered sensor networks to achieve improved network lifetime. The relay nodes may form a network among themselves to route data towards the base station. In this model, the lifetime of a network is determined mainly by the lifetimes of these relay nodes. An energy-aware communication strategy can greatly extend the lifetime of such networks. However, integer linear program (ILP) formulations for optimal, energy-aware routing quickly become computationally intractable and are not suitable for practical networks. In this paper, we have proposed an efficient solution, based on a genetic algorithm (GA), for scheduling the data gathering of relay nodes, which can significantly extend the lifetime of a relay node network. For smaller networks, where the global optimum can be determined, our GA based approach is always able to find the optimal solution. Furthermore, our algorithm can easily handle large networks, where it leads to significant improvements compared to traditional routing schemes.
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paper_title: Ant colony optimization for wireless sensor networks routing

paper_content:

Due to energy limitation of sensors, maximization of lifetime is the key to performance of WSN routing protocol. In this paper, we introduce a heuristic way to reduce energy consumption in WSNs routing process using Ant Colony Optimization. We introduce three Ant Colony Optimization algorithms, the Ant System, Ant Colony System and improved AS and their application in WSN routing process. The simulation results show that ACO is an effective way to reduce energy consumption and maximize WSN lifetime.
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paper_title: Energy-aware Routing Algorithm in WSN using predication-mode

paper_content:

The paper presents a Predication mode based Routing Algorithm based on ACO (PRACO) to achieve the energy-aware data-gathering routing structure in wireless sensor networks (WSN). We adopt series model ARMA to analyze dynamic tendency in data traffic and deduce the construction of load factor, which can help to reveal the future energy status of sensor in WSN. By checking the load factor in heuristic factor and guided by novel pheromone updating rule, artificial ants can foresee the local energy state of networks and the corresponding actions could be adaptively taken to enhance the energy efficiency in routing construction. The simulation results show that the proposed routing building scheme can effectively reinforce the robustness of routing structure by mining the temporal associability and introducing ACO to balance the total energy cost for data transmission, which also contributes to minimize the total communication consumption, meanwhile, to prolong the lifetime of networks.
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paper_title: An Evolutionary Algorithm to a Multi-Objective Deployment and Power Assignment Problem in Wireless Sensor Networks

paper_content:

Wireless sensor networks design requires high quality location assignment and energy efficient power assignment for maximizing the network coverage and lifetime. Classical deployment and power assignment approaches optimize these two objectives individually or by combining them together in a single objective or by constraining one and optimizing the other. In this article a multi-objective deployment and power assignment problem (DPAP) is formulated and a multi-objective evolutionary algorithm based on decomposition (MOEA/D) is specialized. Following the MOEA/D's framework the above multiobjective optimization problem (MOP) is decomposed into many scalar single objective problems. The sub-problems are solved simultaneously by using neighborhood information. Additionally, unique problem-specific, parameter-rising, genetic operators and local search heuristics were designed specifically for the DPAP. In addition, a new encoding scheme is designed to represent a WSN based on the DPAP's design variables. Simulation results show that MOEA/D provides a high quality set of alternative solutions without any prior knowledge on the objectives preference.
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paper_title: An improved routing mechanism using bio-inspired for energy balancing in wireless sensor networks

paper_content:

Planning of energy-efficient protocols is critical for wireless sensor networks (WSNs) because of the constraints on sensor node's energy. Therefore, the routing protocol should be able to achieve uniform power dissipation during transmission to the sink node. In this paper, we present a self-optimization scheme for WSN which is able to utilize and optimize the sensor nodes' resources, especially the batteries, to achieve balanced energy consumption across all sensors. This method is based on Ant Colony Optimization (ACO) meta heuristic which is adopted to enhance the paths with the best quality function. The assessment of this function depends on multi-criteria metrics such as the minimum residual battery power, hop numbers and average energy of both route and network. This method also distribute the traffic load of sensor nodes throughout the WSN leading to reduced energy usage, extended network life time and reduced packet loss. Simulation results show that our scheme performs much better than Energy Efficient Ant-Based Routing (EEABR) in terms of energy consumption and efficiency.
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paper_title: A New Clustering Protocol for Wireless Sensor Networks Using Genetic Algorithm Approach

paper_content:

This paper examines the optimization of the lifetime and energy consumption of Wireless Sensor Networks (WSNs). These two competing objectives have a deep influence over the service qualification of networks and according to recent studies, cluster formation is an appropriate solution for their achievement. To transmit aggregated data to the Base Station (BS), logical nodes called Cluster Heads (CHs) are required to relay data from the fixed-range sensing nodes located in the ground to high altitude aircraft. This study investigates the Genetic Algorithm (GA) as a dynamic technique to find optimum states. It is a simple framework that includes a proposed mathematical formula, which increasing in coverage is benchmarked against lifetime. Finally, the implementation of the proposed algorithm indicates a better efficiency compared to other simulated works.
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paper_title: Application of the Firefly Algorithm for Solving the Economic Emissions Load Dispatch Problem

paper_content:

Efficient and reliable power production is necessary to meet both the profitability of power systems operations and the electricity demand, taking also into account the environmental concerns about the emissions produced by fossil-fuelled power plants. The economic emission load dispatch problem has been defined and applied in order to deal with the optimization of these two conflicting objectives, that is, the minimization of both fuel cost and emission of generating units. This paper introduces and describes a solution to this famous problem using a new metaheuristic nature-inspired algorithm, called firefly algorithm, which was developed by Dr. Xin-She Yang at Cambridge University in 2007. A general formulation of this algorithm is presented together with an analytical mathematical modeling to solve this problem by a single equivalent objective function. The results are compared with those obtained by alternative techniques proposed by the literature in order to show that it is capable of yielding good optimal solutions with proper selection of control parameters.
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paper_title: A discrete firefly meta-heuristic with local search for makespan minimization in permutation flow shop scheduling problems

paper_content:

Article history: Received 23 January 2010 Received in revised form 23 April 2010 Accepted 26 April 2010 Available online 26 April 2010 During the past two decades, there have been increasing interests on permutation flow shop with different types of objective functions such as minimizing the makespan, the weighted mean flow-time etc. The permutation flow shop is formulated as a mixed integer programming and it is classified as NP-Hard problem. Therefore, a direct solution is not available and metaheuristic approaches need to be used to find the near-optimal solutions. In this paper, we present a new discrete firefly meta-heuristic to minimize the makespan for the permutation flow shop scheduling problem. The results of implementation of the proposed method are compared with other existing ant colony optimization technique. The preliminary results indicate that the new proposed method performs better than the ant colony for some well known benchmark problems. © 2010 Growing Science Ltd. All rights reserved.
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paper_title: Cross-Layer Design for Distributed Source Coding in Wireless Sensor Networks

paper_content:

In this paper, we investigate the cross-layer design of medium access control (MAC) protocols and distributed source coding (DSC). We closely model the interaction between the source compression and the medium access control layer for sensor network applications and determine the parameters that are most relevant to both the layers. Specifically, we exploit the spatio-temporal redundancy in the measurements at the sensors and adjust the MAC parameters accordingly. We use short code block lengths, which are easily realizable on resource-constrained sensor nodes. Experimental results with real sensor nodes validate our approach. Whereas this approach yields high energy savings in sensor networks applications, it also preserves the accuracy of the data recovered at the sink nodes.
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paper_title: Cross-layer multirate interaction with Distributed Source Coding in Wireless Sensor Networks

paper_content:

Distributed Source Coding (DSC) is an effective means in reducing data redundancy in wireless sensor networks (WSNs). However, the issues on designing multirate DSC with multirate transmission in WSNs have not been well addressed in the literature. In this paper, we propose a cross-layer approach to achieve optimal DSC data quality while assuring energy efficiency and latency requirement through adjusting multirate DSC data dependencies and network multirate transmission parameters jointly. Specifically, the DSC coding dependency among multirate source coding sensors are fine-tuned to balance compression efficiency and transmission robustness. Transmission rate and retransmission limit on each wireless link are optimized accordingly to achieve unequal error protection (UEP) among inter-dependent DSC streams. Simulation studies demonstrate that the proposed scheme ensures satisfactory DSC data quality and energy efficiency.
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paper_title: Engineering Optimisation by Cuckoo Search

paper_content:

A new metaheuristic optimisation algorithm, called Cuckoo Search (CS), was developed recently by Yang and Deb (2009). This paper presents a more extensive comparison study using some standard test functions and newly designed stochastic test functions. We then apply the CS algorithm to solve engineering design optimisation problems, including the design of springs and welded beam structures. The optimal solutions obtained by CS are far better than the best solutions obtained by an efficient particle swarm optimiser. We will discuss the unique search features used in CS and the implications for further research.
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paper_title: Lévy flights, non-local search and simulated annealing

paper_content:

We solve a problem of non-convex stochastic optimisation with help of simulated annealing of Levy flights of a variable stability index. The search of the ground state of an unknown potential is non-local due to big jumps of the Levy flights process. The convergence to the ground state is fast due to a polynomial decrease rate of the temperature.
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Title: Bio-Mimic Optimization Strategies in Wireless Sensor Networks: A Survey
Section 1: Introduction
Description 1: Introduce the context of bio-mimetic optimization strategies in wireless sensor networks (WSNs), their importance, and the rationale behind the survey.

Section 2: What is Optimization?
Description 2: Define optimization, describe its general components, and illustrate its relevance across various sectors, including WSNs.

Section 3: Optimization Algorithms
Description 3: Provide an overview of different types of optimization algorithms, including derivative-based, derivative-free, and bio-mimetic algorithms.

Section 4: Wireless Sensor Networks and Optimization
Description 4: Discuss optimization considerations specifically in the context of WSNs, focusing on the unique constraints and challenges of these networks.

Section 5: Domains of Optimizations in Wireless Sensor Networks
Description 5: Examine the key areas of optimization in WSNs, including energy efficiency, Quality of Service (QoS), and security, and their interdependencies.

Section 6: Survey of Existing Works
Description 6: Present a detailed survey of existing methodologies and studies on bio-mimetic optimization approaches specifically applied to WSNs.

Section 7: Open Research Issues and Future Directions
Description 7: Highlight the current gaps in the research, propose potential future research directions, and emphasize the need for integrated optimization approaches in WSNs.

Section 8: Conclusion and Future Work
Description 8: Summarize the key points discussed in the survey, the importance of effective optimization algorithms, and outline future research endeavors.