Optimization Techniques Incorporating Evolutionary Model in Wireless Sensor Network: A Survey
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paper_title: ELEPHANT SWARM OPTIMIZATION FOR WIRELESS SENSOR NETWORKS -A CROSS LAYER MECHANISM

paper_content:

The dominant quality of service ( ) oriented parameters in wireless sensing networks ( ) are network lifetime, network throughput, overheads and of course the overall network efficiency. Power is considered as one of the most important factors in depicting the quality of a particular network and even in power is considered as the most influensive parameter. The life time of any network is in proportion with the power level allied with the network components. Therefore the enhancement of the lifetime of wireless sensor networks has ignited the entire communication research society and scientific society to develop a robust system architecture that can effectively optimize the parameters like network lifetime, throughput etc. Now days a number of algorithms and protocols are being developed based on the nature of evolution and its characteristics to develop real time protocols for certain optimization problems. Considering the behavior of elephant swarm as an inspiration the researcher of this research paper has developed a robust communication protocol for enhancing the network with enhanced network lifetime and throughput. The authors of this research manuscript draw inspiration from the behavior of large elephant swarms and incorporate their behavior into wireless sensor networks. The characterizing behavior of elephant swarm has been incorporated using the robust cross layer approach. The elephant swarm optimization technique being discussed in this research paper facilitates a robust as well as optimized routing technique, adaptive radio link optimization and balanced scheduling so as to achieve a cumulative enhanced network performance. In this research work the proposed elephant swarm optimization has been compared with another robust evolutionary computing based optimization technique called as Particle swarm Optimization ( ). The experimental study presented proves that the Elephant Swarm Optimization technique enhances the network life time by about 26.8%. INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGY (IJCET) ISSN 0976 – 6367(Print) ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), pp. 45-60 © IAEME: www.iaeme.com/ijcet.asp Journal Impact Factor (2013): 6.1302 (Calculated by GISI) www.jifactor.com IJCET
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paper_title: Bio-Mimic Optimization Strategies in Wireless Sensor Networks: A Survey

paper_content:

For the past 20 years, many authors have focused their investigations on wireless sensor networks. Various issues related to wireless sensor networks such as energy minimization (optimization), compression schemes, self-organizing network algorithms, routing protocols, quality of service management, security, energy harvesting, etc., have been extensively explored. The three most important issues among these are energy efficiency, quality of service and security management. To get the best possible results in one or more of these issues in wireless sensor networks optimization is necessary. Furthermore, in number of applications (e.g., body area sensor networks, vehicular ad hoc networks) these issues might conflict and require a trade-off amongst them. Due to the high energy consumption and data processing requirements, the use of classical algorithms has historically been disregarded. In this context contemporary researchers started using bio-mimetic strategy-based optimization techniques in the field of wireless sensor networks. These techniques are diverse and involve many different optimization algorithms. As far as we know, most existing works tend to focus only on optimization of one specific issue of the three mentioned above. It is high time that these individual efforts are put into perspective and a more holistic view is taken. In this paper we take a step in that direction by presenting a survey of the literature in the area of wireless sensor network optimization concentrating especially on the three most widely used bio-mimetic algorithms, namely, particle swarm optimization, ant colony optimization and genetic algorithm. In addition, to stimulate new research and development interests in this field, open research issues, challenges and future research directions are highlighted.
---
paper_title: Bio-Mimic Optimization Strategies in Wireless Sensor Networks: A Survey

paper_content:

For the past 20 years, many authors have focused their investigations on wireless sensor networks. Various issues related to wireless sensor networks such as energy minimization (optimization), compression schemes, self-organizing network algorithms, routing protocols, quality of service management, security, energy harvesting, etc., have been extensively explored. The three most important issues among these are energy efficiency, quality of service and security management. To get the best possible results in one or more of these issues in wireless sensor networks optimization is necessary. Furthermore, in number of applications (e.g., body area sensor networks, vehicular ad hoc networks) these issues might conflict and require a trade-off amongst them. Due to the high energy consumption and data processing requirements, the use of classical algorithms has historically been disregarded. In this context contemporary researchers started using bio-mimetic strategy-based optimization techniques in the field of wireless sensor networks. These techniques are diverse and involve many different optimization algorithms. As far as we know, most existing works tend to focus only on optimization of one specific issue of the three mentioned above. It is high time that these individual efforts are put into perspective and a more holistic view is taken. In this paper we take a step in that direction by presenting a survey of the literature in the area of wireless sensor network optimization concentrating especially on the three most widely used bio-mimetic algorithms, namely, particle swarm optimization, ant colony optimization and genetic algorithm. In addition, to stimulate new research and development interests in this field, open research issues, challenges and future research directions are highlighted.
---
paper_title: Bio-Mimic Optimization Strategies in Wireless Sensor Networks: A Survey

paper_content:

For the past 20 years, many authors have focused their investigations on wireless sensor networks. Various issues related to wireless sensor networks such as energy minimization (optimization), compression schemes, self-organizing network algorithms, routing protocols, quality of service management, security, energy harvesting, etc., have been extensively explored. The three most important issues among these are energy efficiency, quality of service and security management. To get the best possible results in one or more of these issues in wireless sensor networks optimization is necessary. Furthermore, in number of applications (e.g., body area sensor networks, vehicular ad hoc networks) these issues might conflict and require a trade-off amongst them. Due to the high energy consumption and data processing requirements, the use of classical algorithms has historically been disregarded. In this context contemporary researchers started using bio-mimetic strategy-based optimization techniques in the field of wireless sensor networks. These techniques are diverse and involve many different optimization algorithms. As far as we know, most existing works tend to focus only on optimization of one specific issue of the three mentioned above. It is high time that these individual efforts are put into perspective and a more holistic view is taken. In this paper we take a step in that direction by presenting a survey of the literature in the area of wireless sensor network optimization concentrating especially on the three most widely used bio-mimetic algorithms, namely, particle swarm optimization, ant colony optimization and genetic algorithm. In addition, to stimulate new research and development interests in this field, open research issues, challenges and future research directions are highlighted.
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paper_title: Adaptive Energy-Aware Gathering Strategy for Wireless Sensor Networks

paper_content:

Energy hole problem is considered one of the most severe threats in wireless sensor networks. In this paper the idea of exploiting sink mobility for the purpose of culling the energy hole problem in hierarchical large-scale wireless sensor networks based on bees algorithm is presented. In the proposed scheme, a mobile sink equipped with a powerful transceiver and battery, traverses the entire field, and periodically gathers data from network cluster heads. The mobile sink follows an adaptive gathering strategy resilient to both connected and disconnected networks. The proposed gathering strategy geared to eliminate multihop relays required by all cluster heads to reach the mobile sink, balancing the traffic load across all network heads, meanwhile, reducing the loss that data may incur due to buffer overflow. Furthermore, enabling the mobile sink to navigate safely within cluttered and uncluttered fields augments the proposed gathering strategy. Extensive simulations are conducted in order to validate the...
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paper_title: Frog call-inspired self-organizing anti-phase synchronization for wireless sensor networks

paper_content:

In this paper, we focus on the calling behavior of Japanese tree frogs, which make calls alternately with their neighbors in order to increase the probability of mating. This behavior can be applied in phase control which realizes collision-free transmission scheduling in wireless communication. We propose a self-organizing scheduling scheme inspired by this frog calling behavior for reliable data transmission in wireless sensor networks. Simulation results show that our proposed method for phase control is capable of reducing data transmission failures and improves the data collection ratio up to 24 % compared to a random transmission method.
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paper_title: ELEPHANT SWARM OPTIMIZATION FOR WIRELESS SENSOR NETWORKS -A CROSS LAYER MECHANISM

paper_content:

The dominant quality of service ( ) oriented parameters in wireless sensing networks ( ) are network lifetime, network throughput, overheads and of course the overall network efficiency. Power is considered as one of the most important factors in depicting the quality of a particular network and even in power is considered as the most influensive parameter. The life time of any network is in proportion with the power level allied with the network components. Therefore the enhancement of the lifetime of wireless sensor networks has ignited the entire communication research society and scientific society to develop a robust system architecture that can effectively optimize the parameters like network lifetime, throughput etc. Now days a number of algorithms and protocols are being developed based on the nature of evolution and its characteristics to develop real time protocols for certain optimization problems. Considering the behavior of elephant swarm as an inspiration the researcher of this research paper has developed a robust communication protocol for enhancing the network with enhanced network lifetime and throughput. The authors of this research manuscript draw inspiration from the behavior of large elephant swarms and incorporate their behavior into wireless sensor networks. The characterizing behavior of elephant swarm has been incorporated using the robust cross layer approach. The elephant swarm optimization technique being discussed in this research paper facilitates a robust as well as optimized routing technique, adaptive radio link optimization and balanced scheduling so as to achieve a cumulative enhanced network performance. In this research work the proposed elephant swarm optimization has been compared with another robust evolutionary computing based optimization technique called as Particle swarm Optimization ( ). The experimental study presented proves that the Elephant Swarm Optimization technique enhances the network life time by about 26.8%. INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGY (IJCET) ISSN 0976 – 6367(Print) ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), pp. 45-60 © IAEME: www.iaeme.com/ijcet.asp Journal Impact Factor (2013): 6.1302 (Calculated by GISI) www.jifactor.com IJCET
---
paper_title: Bio-Mimic Optimization Strategies in Wireless Sensor Networks: A Survey

paper_content:

For the past 20 years, many authors have focused their investigations on wireless sensor networks. Various issues related to wireless sensor networks such as energy minimization (optimization), compression schemes, self-organizing network algorithms, routing protocols, quality of service management, security, energy harvesting, etc., have been extensively explored. The three most important issues among these are energy efficiency, quality of service and security management. To get the best possible results in one or more of these issues in wireless sensor networks optimization is necessary. Furthermore, in number of applications (e.g., body area sensor networks, vehicular ad hoc networks) these issues might conflict and require a trade-off amongst them. Due to the high energy consumption and data processing requirements, the use of classical algorithms has historically been disregarded. In this context contemporary researchers started using bio-mimetic strategy-based optimization techniques in the field of wireless sensor networks. These techniques are diverse and involve many different optimization algorithms. As far as we know, most existing works tend to focus only on optimization of one specific issue of the three mentioned above. It is high time that these individual efforts are put into perspective and a more holistic view is taken. In this paper we take a step in that direction by presenting a survey of the literature in the area of wireless sensor network optimization concentrating especially on the three most widely used bio-mimetic algorithms, namely, particle swarm optimization, ant colony optimization and genetic algorithm. In addition, to stimulate new research and development interests in this field, open research issues, challenges and future research directions are highlighted.
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paper_title: Localization of wireless sensor network using Bees Optimization Algorithm

paper_content:

Wireless Sensor Networks (WSNs) are an emerging technology that draws a significant amount of research attention. Localization of the nodes in these networks plays a key enabling role in WSN applications. In this paper, the use of Bees Optimization Algorithm (BOA) for localizing the nodes of the wireless sensor networks is investigated. BOA is population-based search algorithm that performs a neighborhood search combined with random search. It is inspired by the natural foraging behavior of honey bees. The summation of the squared range error between the node and the anchors is used as the objective function to be minimized in this work. Different simulation tests with different topologies are conducted based on normal random distribution for time of arrival (TOA) measurements and log-normal distribution for received signal strength (RSS) measurements. The simulation test results showed effectiveness of the proposed approach in case of TOA measurements when compared with the lower variance achievable by any unbiased location estimator.
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paper_title: Energy-Efficient Coverage of Wireless Sensor Networks Using Ant Colony Optimization With Three Types of Pheromones

paper_content:

The Efficient-Energy Coverage (EEC) problem is an important issue when implementing Wireless Sensor Networks (WSNs) because of the need to limit energy use. In this paper, we propose a new approach to solving the EEC problem using a novel Ant Colony Optimization (ACO) algorithm. The proposed ACO algorithm has a unique characteristic that conventional ACO algorithms do not have. The proposed ACO algorithm (Three Pheromones ACO, TPACO) uses three types of pheromones to find the solution efficiently, whereas conventional ACO algorithms use only one type of pheromone. One of the three pheromones is the local pheromone, which helps an ant organize its coverage set with fewer sensors. The other two pheromones are global pheromones, one of which is used to optimize the number of required active sensors per Point of Interest (PoI), and the other is used to form a sensor set that has as many sensors as an ant has selected the number of active sensors by using the former pheromone. The TPACO algorithm has another advantage in that the two user parameters of ACO algorithms are not used. We also introduce some techniques that lead to a more realistic approach to solving the EEC problem. The first technique is to utilize the probabilistic sensor detection model. The second method is to use different kinds of sensors, i.e., heterogeneous sensors in continuous space, not a grid-based discrete space. Simulation results show the effectiveness of our algorithm over other algorithms, in terms of the whole network lifetime.
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paper_title: Frog call-inspired self-organizing anti-phase synchronization for wireless sensor networks

paper_content:

In this paper, we focus on the calling behavior of Japanese tree frogs, which make calls alternately with their neighbors in order to increase the probability of mating. This behavior can be applied in phase control which realizes collision-free transmission scheduling in wireless communication. We propose a self-organizing scheduling scheme inspired by this frog calling behavior for reliable data transmission in wireless sensor networks. Simulation results show that our proposed method for phase control is capable of reducing data transmission failures and improves the data collection ratio up to 24 % compared to a random transmission method.
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paper_title: A Biologically Inspired Sensor Wakeup Control Method for Wireless Sensor Networks

paper_content:

This paper presents an artificial ant colony approach to distributed sensor wakeup control (SWC) in wireless sensor networks (WSN) to accomplish the joint task of surveillance and target tracking. Each sensor node is modeled as an ant, and the problem of target detection is modeled as the food locating by ants. Once the food is found, the ant will release pheromone. The communication, invalidation, and fusion of target information are modeled as the processes of pheromone diffusion, loss, and accumulation. Since the accumulated pheromone can measure the existence of a target, it is used to determine the probability of ant-searching activity in the next round. To the best of our knowledge, this is the first biologically inspired SWC method in the WSN. Such a biologically inspired method has multiple desirable advantages. First, it is distributive and does not require a centralized control or cluster leaders. Therefore, it is free of the problems caused by leader failures and can save the communication cost for leader selection. Second, it is robust to false alarms because the pheromone is accumulated temporally and spatially and thus is more reliable for wakeup control. Third, the proposed method does not need the knowledge of node position. Two theorems are presented to analytically determine the key parameters in the method: the minimum and maximum pheromone. Simulations are carried out to evaluate the performance of the proposed method in comparison with representative methods.
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paper_title: Power Reduction of Wireless Sensor Networks Using Ant Colony Optimization

paper_content:

Power consumption is one of the most important problems for wireless sensor networks because of the battery limitation in each sensor. This paper presents an ant colony optimization- (ACO-) based routing algorithm to reduce power consumption. First, a grade table is built and referred to generate several possible routing paths. Then, the ACO explores these paths to reduce the power consumption of the nodes. The simulations indicate that the proposed algorithm obtains more balanced transmission among the nodes and reduces the power consumption of the routing.
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Title: Optimization Techniques Incorporating Evolutionary Model in Wireless Sensor Network: A Survey

Section 1: Challenges in WSN
Description 1: Discuss the essential features and technical challenges of Wireless Sensor Networks (WSNs), including routing, time synchronization, localization, security and privacy, scalability, and energy conservation.

Section 2: Need For Optimization in WSN
Description 2: Explain the importance of optimization in WSNs, detailing how optimization is critical for energy efficiency, cost, and application requirements, and discussing hardware and software aspects for enhancing network lifetime.

Section 3: Optimization Techniques Incorporating Evolutionary Model
Description 3: Describe various evolutionary techniques used to optimize WSNs, including Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), BEES Optimization Algorithm, Frog Leaping Optimization, Elephant Swarm Optimization, and Genetic Algorithm (GA).

Section 4: Related Papers
Description 4: Provide an overview of related research papers, summarizing their contributions to the field of WSN optimization using evolutionary and bio-inspired techniques.

Section 5: Conclusion
Description 5: Summarize the survey, emphasizing the significance of using evolutionary methods for network optimization and suggesting the potential of a cross-layered approach for more effective and energy-efficient WSNs.