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

IEEE Access

卷:8

Y. Seresstou¹  K. Raoof¹  M. N. Omri²  M. Mbarki²  S. E. Bouzid²  C. Dridi³ 

[1] LAUM Laboratory, UMR CNRS 6613, Le Mans University, Le Mans, France;

[2] MARS Research Laboratory, LR 17ES05, University of Sousse, ISITCom, Sousse, Tunisia;

[3] NANOMISENE Laboratory, LR16CRMN01, Centre for Research on Microelectronics and Nanotechnology of Sousse, Technopole of Sousse, Sousse, Tunisia;

关键词: WSN;    deployment;    multi-objective optimization;    sensing coverage;    connectivity;    cost;   

DOI  :  10.1109/ACCESS.2020.2999157

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown