Genetic Algorithm-Based Heuristic for Solving Target Coverage Problem in Wireless Sensor Networks

Manju, .; Singh, Deepak; Chand, Satish; Kumar, Bijendra

doi:10.1007/978-981-10-4603-2_24

Cited by 8 publications

(4 citation statements)

References 9 publications

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“…[19] replaced the defective sensors with inactive sensors in DSNs, which caused an increase in the network lifespan. The authors in [20] proposed a genetic‐based algorithm to solve the target coverage problem in WSNs. Their algorithm puts several sensors in each cover set to provide coverage to monitor all targets in the network.…”

Section: Literature Reviewmentioning

confidence: 99%

Solving the target coverage problem in multilevel wireless networks capable of adjusting the sensing angle using continuous learning automata

et al. 2021

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Today, a directional sensor network is a popular environment for solving the target coverage problem. Monitoring all targets in a DSN is a crucial challenge to scholars working in this field of study. Adjusting the angle and range of the sensors can be an efficient technique for improving the network performance. In this way, the network has the most extended lifespan and, at the same time, spends the least time to find the best cover set. In this method, each sensor dynamically adjusts its own sensing angle in order to find the targets by choosing the best range. The present study proposed a continuous learning automata‐based method to choose the optimum sensing angle for the sensors in a DSN. Then, to evaluate the proposed algorithm performance, its results were compared to those of a conventional automata‐based method whose algorithm worked based on continuous automata. The comparative analysis confirmed the superiority of the proposed method over the conventional automata‐based method regarding the extension of the network lifespan.

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Section: Literature Reviewmentioning

confidence: 99%

Solving the target coverage problem in multilevel wireless networks capable of adjusting the sensing angle using continuous learning automata

et al. 2021

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“…The algorithm saves a track of the number of targets, that are the sensor node monitor and its remaining energy in order to find the optimal path that increases the lifetime of network. Manju et al [21] used a genetic algorithm to propose a solution heuristic to target coverage problem, that groups the sensor nodes of the network into subsets covering all targets, to form this subsets the authors select the minimum number of sensor nodes which have the maximum of remaining energy. In [22] The authors proposed a randomize algorithm to solve the target coverage problem in wireless senor networks, this algorithm allows to group the sensor nodes in not joint sets.…”

Section: Related Workmentioning

confidence: 99%

Maximum Lifetime Target Coverage in Wireless Sensor Networks

Saadi

Bounceur

Euler

et al. 2019

Wireless Pers Commun

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The challenge in the deployment of wireless sensor networks is to ensure the coverage of targets with high energy efficiency, particularly when coverage constraint and energy constraint must be taken into account. Many algorithms have been suggested over the years to enhance network lifetime as sensor nodes are powered by batteries. These algorithms divide a set of sensor nodes into a number of subsets which can monitor all targets. The subsets are either joint or disjoint. In this paper, we propose an efficient method to compute the maximum lifetime target coverage. Our proposed method is centered around a target that is covered by a minimum number of sensor nodes. Based on this approach, we also propose an algorithm to expand the network lifetime which guarantees the monitoring of all targets by forming subsets of sensor nodes. Our obtained results are compared with the work reported in [1]. They are also compared with a method of computing the maximum lifetime target coverage (MLTC). The results obtained by simulation show that the proposed algorithm enhances the network lifetime, which illustrates the efficiency of this algorithm.

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“…Literature [16] proposed a new heuristic algorithm based on a GA to optimize the target coverage of WSNs. This algorithm can extend the life of the network to a large extent and has a faster convergence speed and better performance than the comparison algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Binary Adaptive Clone Shuffled Frog Leaping Algorithm for Three‐Dimensional Low‐Energy Target Coverage Optimization in Environmental Monitoring Wireless Sensor Networks

Liu

Yang

et al. 2021

Journal of Sensors

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In recent years, more and more researchers have paid attention to the three-dimensional target coverage of environmental monitoring wireless sensor networks (EMWSNs) under real environmental conditions. However, the target coverage method studied in the traditional two-dimensional plane is full of loopholes when applied in the real three-dimensional physical world. Most coverage algorithms usually only optimize for a single problem of target coverage or network energy consumption and cannot reduce network energy consumption while improving coverage. This paper proposes a novel binary adaptive clone shuffled leapfrog algorithm (BACSFLA) suitable for EMWSNs. BACSFLA has an excellent performance in the coverage of three-dimensional nodes, which can significantly reduce the network energy consumption of ENWSNs in the coverage process, and greatly improve the coverage of nodes. Through simulation experiments, BACSFLA was compared with simulated annealing (SA) and genetic algorithm (GA) in the same conditional parameters. The coverage rate of BACSFLA in EMWSNs is 3.9 % higher than that of GA and 5.4 % higher than that of SA. The network energy consumption of BACSFLA is 36.0 % lower than GA and 35.9 % lower than SA. Moreover, BACSFLA can significantly reduce the calculation time and get better results in a shorter time.

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Genetic Algorithm-Based Heuristic for Solving Target Coverage Problem in Wireless Sensor Networks

Cited by 8 publications

References 9 publications

Solving the target coverage problem in multilevel wireless networks capable of adjusting the sensing angle using continuous learning automata

Solving the target coverage problem in multilevel wireless networks capable of adjusting the sensing angle using continuous learning automata

Maximum Lifetime Target Coverage in Wireless Sensor Networks

A Binary Adaptive Clone Shuffled Frog Leaping Algorithm for Three‐Dimensional Low‐Energy Target Coverage Optimization in Environmental Monitoring Wireless Sensor Networks

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