Network Life Time Analysis of WSNs Using Particle Swarm Optimization

Yadav, Amita; Kumar, Suresh; Singh, Vijendra

doi:10.1016/j.procs.2018.05.092

Cited by 45 publications

(26 citation statements)

References 11 publications

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“…An optimal probability of CHs in LEACH is estimated using a genetic algorithm in [21]. Moreover, an integration between LEACH and Particle Swarm Optimization (PSO) named LEACH-PSO proposed in [24]. Cluster selection in LEACH-PSO is done based on energy consumption.…”

Section: B Ai-based Clustering Methodsmentioning

confidence: 99%

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CSOCA: Chicken Swarm Optimization Based Clustering Algorithm for Wireless Sensor Networks

2020

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The rapid advancement in Wireless Sensor Network (WSN) technology has enabled smart environments to provide ubiquitous real-time applications in various fields such as industry, smart city, transport, health and Internet of Things (IoT). Energy is the most significant resource in WSNs as it has a direct effect on their lifetime. The efficient use of energy is required for the lifetime extension of WSNs. One of the well-known methods for achieving high scalability and efficient resource allocation in WSN is a clustering of sensor nodes. In this paper, the Chicken Swarm Optimization based Clustering Algorithm (CSOCA) is proposed to improve energy efficiency in WSNs. The chicken swarm optimization is discretized by applying a sigmoid function to individuals. Moreover, we proposed CSOCA with Genetic Algorithm (CSOCA-GA) which is an improvement to CSOCA by employing the Genetic Algorithm's processes in CSOCA. CSOCA-GA utilizes crossover and mutation processes for individuals with low fitness value to extend the population diversity. CSOCA and CSOCA-GA are tested and compared with other similar algorithms to confirm their effectiveness in terms of extending WSN lifetime and reducing energy consumption. INDEX TERMS Wireless sensor network, clustering, Internet of Things, genetic algorithm, chicken swarm optimization.

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Section: B Ai-based Clustering Methodsmentioning

confidence: 99%

“…For the next tests, we consider one point crossover and mutation rate of 0.05% for CSOCA-GA. The performance results of COSCA, and CSOCA-GA are compared with LEACH-MS [25], LEACH-PSO [24], EODC [32], GCDC [33] and SFFA [31] algorithms.…”

Section: B Cosca and Cosca-ga Evaluationsmentioning

confidence: 99%

CSOCA: Chicken Swarm Optimization Based Clustering Algorithm for Wireless Sensor Networks

2020

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“…They considered the parameters such as degree of the node, remaining energy of the node, the distance from BS to the sensor node, and the average transmission distance to its neighbors for achieving the load-balanced clustering. An approach called LEACH-PSO [60] has been proposed for improving the network lifetime by selecting an optimum number of CHs in every round. In this work, the particle swarm optimization method is integrated with LEACH for forming the clusters.…”

Section: Existing Routing Techniquesmentioning

confidence: 99%

Data Collection Protocols in Wireless Sensor Networks

Guravaiah¹,

Kavitha²,

Velusamy³

2021

Wireless Sensor Networks - Design, Deployment and Applications

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In recent years, wireless sensor networks have became the effective solutions for a wide range of IoT applications. The major task of this network is data collection, which is the process of sensing the environment, collecting relevant data, and sending them to the server or BS. In this chapter, classification of data collection protocols are presented with the help of different parameters such as network lifetime, energy, fault tolerance, and latency. To achieve these parameters, different techniques such as multi-hop, clustering, duty cycling, network coding, aggregation, sink mobility, directional antennas, and cross-layer solutions have been analyzed. The drawbacks of these techniques are discussed. Finally, the future work for routing protocols in wireless sensor networks is discussed.

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“…Most of the recent research optimization techniques in WSN such as Ant Colony Optimization, Particle Swarm Optimization and Fuzzy logic-based algorithms have been proposed to extend the lifetime of the network [1,13]. Hierarchical routing protocols such as clustering are mainly used to improve the network lifetime.…”

Section: Introductionmentioning

confidence: 99%

“…With the help of the novel meta-heuristic algorithms, clustering problems are solved in an optimal and efficient manner with less computational time. The results from the algorithms proposed in [13] are not satisfactory, thus many improvements are needed to solve the clustering problem with the help of meta-heuristic approaches [5,24].…”

Section: Introductionmentioning

confidence: 99%

Grey Wolf Optimization Based Energy Efficiency Management System for Wireless Sensor Networks

Pratha

Asanambigai

Mugunthan³

2021

Preprint

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Wireless Sensor Networks (WSN) is the fundamental technology for the Internet of Things (IoT). It is a network formed from several sensor nodes to sense the changes in the environment. The nodes are battery powered that performs sensing and transmission of information to other nodes in the network. Thus, the energy of the sensor node plays a crucial role in WSN. Thus, intelligent models are anticipated to solve the network problems by optimizing or minimizing the mechanism inorder to improve the energy efficiency. In this paper, a combined meta-heuristic approach called Grey Wolf Optimization based Game theoretical Approach (GWOGA) is proposed that helps for clustering to find the best solutions for selection of aggregation points and this optimal selection of aggregation points lead the nodes to maximize its battery/lifetime. Experimental and simulation analysis shows that the GWOGA outperforms the existing models and retains the lifetime of the network.

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Network Life Time Analysis of WSNs Using Particle Swarm Optimization

Cited by 45 publications

References 11 publications

CSOCA: Chicken Swarm Optimization Based Clustering Algorithm for Wireless Sensor Networks

CSOCA: Chicken Swarm Optimization Based Clustering Algorithm for Wireless Sensor Networks

Data Collection Protocols in Wireless Sensor Networks

Grey Wolf Optimization Based Energy Efficiency Management System for Wireless Sensor Networks

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