Genetic algorithm‐based meta‐heuristic for target coverage problem

Manju, .; Chand, Satish; Kumar, Bijendra

doi:10.1049/iet-wss.2017.0067

Cited by 23 publications

(6 citation statements)

References 18 publications

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“…Other few algorithms are also developed in the same to improve the network lifetime of WSN. They are non-dominated sorting genetic algorithm-II (NSGA-II), a genetic algorithm (GA)-based meta-heuristic model, wireless sensor network dynamic coverage and connectivity problem (WSN-DCCP) and an improved genetic algorithm (ROS_IGA) [ 36 , 37 , 38 , 39 ].…”

Section: Related Workmentioning

confidence: 99%

MWCSGA—Multi Weight Chicken Swarm Based Genetic Algorithm for Energy Efficient Clustered Wireless Sensor Network

Ajmi

Helali

Lorenz

et al. 2021

Sensors

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Nowadays due to smart environment creation there is a rapid growth in wireless sensor network (WSN) technology real time applications. The most critical resource in in WSN is battery power. One of the familiar methods which mainly concentrate in increasing the power factor in WSN is clustering. In this research work, a novel concept for clustering is introduced which is multi weight chicken swarm based genetic algorithm for energy efficient clustering (MWCSGA). It mainly consists of six sections. They are system model, chicken swarm optimization, genetic algorithm, CCSO-GA cluster head selection, multi weight clustering model, inter cluster, and intra cluster communication. In the performance evaluation the proposed model is compared with few earlier methods such as Genetic Algorithm-Based Energy-Efficient Adaptive Clustering Protocol For Wireless Sensor Networks (GA-LEACH), Low energy adaptive Clustering hierarchy approach for WSN (MW-LEACH) and Chicken Swarm Optimization based Genetic Algorithm (CSOGA). During the comparison it is proved that our proposed method performed well in terms of energy efficiency, end to end delay, packet drop, packet delivery ratio and network throughput.

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Section: Related Workmentioning

confidence: 99%

MWCSGA—Multi Weight Chicken Swarm Based Genetic Algorithm for Energy Efficient Clustered Wireless Sensor Network

Ajmi

Helali

Lorenz

et al. 2021

Sensors

View full text Add to dashboard Cite

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“…Manju et al [30], [31] proposed energy-based heuristic to extend the network lifetime further. They introduced a heuristic to constitute cover set with the objective of prioritizing sensors based on remaining energy and coverage of the poorly covered target.…”

Section: A Target Full Coveragementioning

confidence: 99%

Proficient QoS-Based Target Coverage Problem in Wireless Sensor Networks

et al. 2020

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Assuring the coverage towards the predefined set of targets, power-constrained wireless sensor networks (WSNs) consist of sensing devices (i.e., sensor nodes) that are associated with limited battery life and fixed sensing range. All the sensors are collectively responsible for covering these sets of objects. The standard target coverage problem is the one where continuous coverage is provided over a predefined set of targets for the maximum possible duration so that the scarce resource (battery power) can be optimally utilized. Therefore, in order to incorporate quality of service (QoS) in the network and ensure smooth monitoring of the desired target set, the paper addresses target Q-Coverage, which is one of the variants of standard target coverage problem where a target is covered by at least Q-sensors (pre-defined number) in every cover set. A cover set is a subset of sensors which cover whole targets in a single iteration. In this paper, a greedy heuristic based technique, i.e., maximum coverage small lifetime (MCSL) has been proposed, which restricts the usages of those sensors that poorly cover targets and promotes the usage of those sensors that have maximum coverage and energy. Simulations are performed on static wireless sensor network with varying Q values to test the efficiency of the proposed method. The performance of the proposed heuristic is compared with optimal upper bound based on network lifetime, and results prove that performance is improvised by 94%. The obtained results are further compared with the existing approaches to prove the superiority of the proposed work via extensive experimentations.INDEX TERMS Coverage constraint, critical target, energy-efficient network, maximum coverage small lifetime, maximum lifetime target coverage, q-coverage.

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“…Perform data simulation to judge the performance of EASA. Simulation results indicate the proposed algorithm can achieve a higher working life of LSWSNs over GA and PSO [8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 97%

Elite Adaptive Simulated Annealing Algorithm for Maximizing the Lifespan in LSWSNs

et al. 2021

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Large-scale wireless sensor networks (LSWSNs) are currently one of the most influential technologies and have been widely used in industry, medical, and environmental monitoring fields. The LSWSNs are composed of many tiny sensor nodes. These nodes are arbitrarily distributed in a certain area for data collection, and they have limited energy consumption, storage capabilities, and communication capabilities. Due to limited sensor resources, traditional network protocols cannot be directly applied to LSWSNs. Therefore, the issue of maximizing the LSWSNs’ lifetime by working with duty cycle design algorithm has been extensively studied in this paper. Encouraged by annealing algorithm, this work provides a new elite adaptive simulated annealing (EASA) algorithm to prolong LSWSNs’ lifetime. We then present a sensor duty cycle models, which can make sure the full coverage of the monitoring targets and prolong the network lifetime as much as possible. Simulation results indicate that the network lifetime of EASA algorithm is 21.95% longer than that of genetic algorithm (GA) and 28.33% longer than that of particle swarm algorithm (PSO).

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Genetic algorithm‐based meta‐heuristic for target coverage problem

Cited by 23 publications

References 18 publications

MWCSGA—Multi Weight Chicken Swarm Based Genetic Algorithm for Energy Efficient Clustered Wireless Sensor Network

MWCSGA—Multi Weight Chicken Swarm Based Genetic Algorithm for Energy Efficient Clustered Wireless Sensor Network

Proficient QoS-Based Target Coverage Problem in Wireless Sensor Networks

Elite Adaptive Simulated Annealing Algorithm for Maximizing the Lifespan in LSWSNs

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