A Novel Meta-heuristic Differential Evolution Algorithm for Optimal Target Coverage in Wireless Sensor Networks

Naik, Chandra; Shetty, D. Pushparaj

doi:10.1007/978-3-030-16681-6_9

Cited by 5 publications

(3 citation statements)

References 8 publications

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“…In this experiment, we first executed algorithms DE, 46 binary PSO, 47 and improved GA 35 by 225 possible positions and 50 target points. The comparison is executed in terms of number of selected positions and the variance.…”

Section: Simulations and Resultsmentioning

confidence: 99%

“…The proposed algorithm is compared with DE, 46 PSO, 47 and improved GA, 35 in terms of the number of positions required to place sensor nodes and network connectivity.…”

Section: Simulations and Resultsmentioning

confidence: 99%

“…The values of the three objective functions f1, f2, and f3 for several tested weight values are given in Table 3. In this experiment, we first executed algorithms DE, 46 binary PSO, 47 and improved GA 35 by 225 possible positions and 50 target points. The comparison is executed in terms of number of selected positions and the variance.…”

Section: Optimal Node Placementmentioning

confidence: 99%

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Node placement optimization using particle swarm optimization and iterated local search algorithm in wireless sensor networks

Chelbi

Dhahri

Bouaziz

2021

Int J Communication

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Summary Coverage and connectivity are the two most challenging issues in target‐based wireless sensor networks (WSNs). For that, node placement is one of the fundamental concerns that affect the performance of coverage and connectivity in WSN. This paper introduces a new approach by combining particle swarm optimization and iterated local search (PSO‐ILS) to have an optimum coverage and connectivity rate with the minimum number of nodes. In one side, to maintain the full coverage of targets, the PSO‐ILS is used to deploy the minimum number of sensor nodes. In other side, to achieve the full connectivity, the optimal position determination (OPD) algorithm was conceived to identify the optimal candidate positions which can be used by the PSO‐ILS to place the minimum number of relay nodes. The obtained results considered over a number of runs are compared with canonical PSO, differential evolution (DE), and genetic algorithms (GAs). The outcomes derived from this comprehensive analysis determine that PSO‐ILS provides an effectual improvement in contrary to the methods PSO, DE, and GA in terms of the selected potential positions to ensure full coverage of target points and the number of relay nodes required to achieve full connectivity.

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Section: Simulations and Resultsmentioning

confidence: 99%

“…The proposed algorithm is compared with DE, 46 PSO, 47 and improved GA, 35 in terms of the number of positions required to place sensor nodes and network connectivity.…”

Section: Simulations and Resultsmentioning

confidence: 99%