A smart methodology for deterministic deployment of wireless sensor networks

Senouci, Mustapha Réda; Boudaren, Mohamed El Yazid; Senouci, Mohamed Abdelkrim; Mellouk, Abdelhamid

doi:10.1109/saconet.2014.6867774

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…From 29, it can be seen clearly that for different (k, m) combinations, our optimizer is able to place fewer nodes while ensuring full k-coverage and m-connectivity, except for (4,1) it is greater than [23,24]. Surprisingly for these two studies, the number of nodes jumped from 42 to 47 for [24] and from 44 to 49 for [23] for the couple (4,2). This finding was surprising, and it needs further investigations.…”

Section: Comparisonsmentioning

confidence: 91%

“…Nowadays, wireless sensor networks (WSNs) represent the most used solution in applications dedicated to intelligent environments, notably military, agricultural, health, surveillance, domestic, and lighting [1,2,3,4]. All of these applications require an optimal deployment of hundreds of wireless interconnected sensor nodes to maintain quality of service (QoS) [5].…”

Section: Introductionmentioning

confidence: 99%

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MOONGA: Multi-Objective Optimization of Wireless Network Approach Based on Genetic Algorithm

et al. 2020

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In high-density wireless sensor networks, the quality of service in terms of sensing coverage, connectivity, lifetime, energy consumption and cost is closely linked to the position of the nodes in the network. Consequently, the placement of a large number of nodes while simultaneously optimizing several measurements is considered to be an NP-difficult problem. In this article, we propose a new approach to optimizing the problem of node placement. To achieve this objective, we started by studying the main approaches existing in the literature in order to identify their limits. In order to have accurate solutions, existing physical models are studied, improved, and validated with real measurements. Then, we proposed a new formulation of the deployment optimization problem as a constrained multi-objective optimization problem. This allowed us to develop an optimizer, based on the multi-objective genetic algorithm and the weighted sum optimization method, which we called MOONGA (multi-objective wireless network optimization using the genetic algorithm). This optimizer makes it possible to generate an optimal deployment according to the topology, the environment, the specifications of different applications and the preferences of the network designer users. The algorithms that we have developed and implemented within the framework of experiments carried out on test data in order to prove the effectiveness of our approach. The analysis of the results found confirm well the interest and the superiority of our proposed approach compared to main studied approaches.

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