Particle Swarm Optimization in Wireless-Sensor Networks: A Brief Survey
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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 Design Space of Wireless Sensor Networks ∗

paper_content:

In the recent past, wireless sensor networks have found their way into a wide variety of applications and systems with vastly varying requirements and characteristics. As a consequence, it is becoming increasingly difficult to discuss typical requirements regarding hardware issues and software support. This is particularly problematic in a multidisciplinary research area such as wireless sensor networks, where close collaboration between users, application domain experts, hardware designers, and software developers is needed to implement efficient systems. In this article we discuss the consequences of this fact with regard to the design space of wireless sensor networks by considering its various dimensions. We justify our view by demonstrating that specific existing applications occupy different points in the design space.
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paper_title: Wireless sensor network survey

paper_content:

A wireless sensor network (WSN) has important applications such as remote environmental monitoring and target tracking. This has been enabled by the availability, particularly in recent years, of sensors that are smaller, cheaper, and intelligent. These sensors are equipped with wireless interfaces with which they can communicate with one another to form a network. The design of a WSN depends significantly on the application, and it must consider factors such as the environment, the application's design objectives, cost, hardware, and system constraints. The goal of our survey is to present a comprehensive review of the recent literature since the publication of [I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, A survey on sensor networks, IEEE Communications Magazine, 2002]. Following a top-down approach, we give an overview of several new applications and then review the literature on various aspects of WSNs. We classify the problems into three different categories: (1) internal platform and underlying operating system, (2) communication protocol stack, and (3) network services, provisioning, and deployment. We review the major development in these three categories and outline new challenges.
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paper_title: A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems

paper_content:

Several extensions to evolutionary algorithms (EAs) and particle swarm optimization (PSO) have been suggested during the last decades offering improved performance on selected benchmark problems. Recently, another search heuristic termed differential evolution (DE) has shown superior performance in several real-world applications. In this paper, we evaluate the performance of DE, PSO, and EAs regarding their general applicability as numerical optimization techniques. The comparison is performed on a suite of 34 widely used benchmark problems. The results from our study show that DE generally outperforms the other algorithms. However, on two noisy functions, both DE and PSO were outperformed by the EA.
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paper_title: Foundations of Global Genetic Optimization

paper_content:

This book is devoted to the application of genetic algorithms in continuous global optimization. Some of their properties and behavior are highlighted and formally justified. Various optimization techniques and their taxonomy are the background for detailed discussion. The nature of continuous genetic search is explained by studying the dynamics of probabilistic measure, which is utilized to create subsequent populations. This approach shows that genetic algorithms can be used to extract some areas of the search domain more effectively than to find isolated local minima. The biological metaphor of such behavior is the whole population surviving by rapid exploration of new regions of feeding rather than caring for a single individual. One group of strategies that can make use of this property are two-phase global optimization methods. In the first phase the central parts of the basins of attraction are distinguished by genetic population analysis. Afterwards, the minimizers are found by convex optimization methods executed in parallel.
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paper_title: Biomimicry of bacterial foraging for distributed optimization and control

paper_content:

We explain the biology and physics underlying the chemotactic (foraging) behavior of E. coli bacteria. We explain a variety of bacterial swarming and social foraging behaviors and discuss the control system on the E. coli that dictates how foraging should proceed. Next, a computer program that emulates the distributed optimization process represented by the activity of social bacterial foraging is presented. To illustrate its operation, we apply it to a simple multiple-extremum function minimization problem and briefly discuss its relationship to some existing optimization algorithms. The article closes with a brief discussion on the potential uses of biomimicry of social foraging to develop adaptive controllers and cooperative control strategies for autonomous vehicles. For this, we provide some basic ideas and invite the reader to explore the concepts further.
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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: Performance Comparison of Optimization Algorithms for Clustering in Wireless Sensor Networks

paper_content:

Clustering in wireless sensor networks (WSNs) is one of the techniques that can expand the lifetime of the whole network through data aggregation at the cluster head. This paper presents performance comparison between particle swarm optimization (PSO) and genetic algorithms (GA) with a new cost function that has the objective of simultaneously minimizing the intra-cluster distance and optimizing the energy consumption of the network. Furthermore, a comparison is made with the well known cluster-based protocols developed for WSNs, LEACH (low-energy adaptive clustering hierarchy) and LEACH-C, the later being an improved version of LEACH, as well as the traditional K-means clustering algorithm. Simulation results demonstrate that the proposed protocol using PSO algorithm has higher efficiency and can achieve better network lifetime and data delivery at the base station over its comparatives.
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paper_title: A Survey on Wireless Sensor Networks Deployment

paper_content:

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: 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: 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: 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: A Theory of Network Localization

paper_content:

In this paper, we provide a theoretical foundation for the problem of network localization in which some nodes know their locations and other nodes determine their locations by measuring the distances to their neighbors. We construct grounded graphs to model network localization and apply graph rigidity theory to test the conditions for unique localizability and to construct uniquely localizable networks. We further study the computational complexity of network localization and investigate a subclass of grounded graphs where localization can be computed efficiently. We conclude with a discussion of localization in sensor networks where the sensors are placed randomly
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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.
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paper_title: Optimizing the Localization of a Wireless Sensor Network in Real Time Based on a Low-Cost Microcontroller

paper_content:

In this paper, a low-cost microcontroller-based system that uses the pedometer measurement and communication ranging between neighboring nodes of a wireless sensor network for localization is presented. Unlike most of the existing methods that require good network connectivity, the proposed system works well in a sparse network. As the localization requires solving of nonlinear equations in real time, two optimization approaches, namely, the Gauss-Newton algorithm and the particle swarm optimization have been studied. The localization and optimization algorithms have been implemented with a microcontroller. The performance has been evaluated with experimental results.
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paper_title: Localization in wireless sensor networks using particle swarm optimization

paper_content:

This paper proposes a novel and computationally efficient global optimization method based on swarm intelligence for locating nodes in a WSN environment. The mean squared range error of all neighbouring anchor nodes is taken as the objective function for this non linear optimization problem. The Particle Swarm Optimization (PSO) is a high performance stochastic global optimization tool that ensures the minimization of the objective function, without being trapped into local optima. The easy implementation and low memory requirement features of PSO make it suitable for highly resource constrained WSN environments. Computational experiments on data drawn from simulated WSNs show better convergence characteristics than the existing Simulated Annealing based WSN localization.
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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: 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: 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.
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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.
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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.
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paper_title: An application-specific protocol architecture for wireless microsensor networks

paper_content:

Networking together hundreds or thousands of cheap microsensor nodes allows users to accurately monitor a remote environment by intelligently combining the data from the individual nodes. These networks require robust wireless communication protocols that are energy efficient and provide low latency. We develop and analyze low-energy adaptive clustering hierarchy (LEACH), a protocol architecture for microsensor networks that combines the ideas of energy-efficient cluster-based routing and media access together with application-specific data aggregation to achieve good performance in terms of system lifetime, latency, and application-perceived quality. LEACH includes a new, distributed cluster formation technique that enables self-organization of large numbers of nodes, algorithms for adapting clusters and rotating cluster head positions to evenly distribute the energy load among all the nodes, and techniques to enable distributed signal processing to save communication resources. Our results show that LEACH can improve system lifetime by an order of magnitude compared with general-purpose multihop approaches.
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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.
---
paper_title: Performance Comparison of Optimization Algorithms for Clustering in Wireless Sensor Networks

paper_content:

Clustering in wireless sensor networks (WSNs) is one of the techniques that can expand the lifetime of the whole network through data aggregation at the cluster head. This paper presents performance comparison between particle swarm optimization (PSO) and genetic algorithms (GA) with a new cost function that has the objective of simultaneously minimizing the intra-cluster distance and optimizing the energy consumption of the network. Furthermore, a comparison is made with the well known cluster-based protocols developed for WSNs, LEACH (low-energy adaptive clustering hierarchy) and LEACH-C, the later being an improved version of LEACH, as well as the traditional K-means clustering algorithm. Simulation results demonstrate that the proposed protocol using PSO algorithm has higher efficiency and can achieve better network lifetime and data delivery at the base station over its comparatives.
---
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: Data-aggregation techniques in sensor networks: a survey

paper_content:

Wireless sensor networks consist of sensor nodes with sensing and com- munication capabilities. We focus on data-aggregation problems in energy- constrained sensor networks. The main goal of data-aggregation algorithms is to gather and aggregate data in an energy efficient manner so that net- work lifetime is enhanced. In this article we present a survey of data-aggre- gation algorithms in wireless sensor networks. We compare and contrast different algorithms on the basis of performance measures such as lifetime, latency, and data accuracy. We conclude with possible future research directions.
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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.
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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: 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.
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Title: Particle Swarm Optimization in Wireless-Sensor Networks: A Brief Survey

Section 1: PSO Algorithm
Description 1: This section briefly outlines the Particle Swarm Optimization (PSO) algorithm, including its principles, implementation, and the parameters typically considered in PSO research.

Section 2: Other Optimization Algorithms
Description 2: This section provides an overview of traditional optimization methods and other heuristic algorithms, comparing their computational complexities and advantages against PSO.

Section 3: Optimal WSN Deployment
Description 3: This section discusses the applications of PSO in optimal Wireless Sensor Network (WSN) deployment, covering strategies for stationary and mobile node positioning and base station positioning.

Section 4: Node Localization in WSNs
Description 4: This section explores how PSO is used for node localization in WSNs, detailing various approaches and their effectiveness in different conditions, including stationary and dynamic scenarios.

Section 5: Energy-Aware Clustering (EAC) in WSNs
Description 5: This section delves into the role of PSO in energy-aware clustering to enhance the network's lifespan by optimizing cluster-head selection and clustering strategies.

Section 6: Data Aggregation in WSNs
Description 6: This section discusses how PSO contributes to data aggregation, optimizing transmission power allocation, defining local thresholds, and configuring sensors for improved data fusion and reduced communication overhead.

Section 7: Conclusion
Description 7: This section summarizes the advantages and limitations of PSO in solving various WSN issues, projects future research directions, and provides concluding remarks on the applicability of PSO in WSN environments.