Multi-Objective WSN Deployment Using Genetic Algorithms Under Cost, Coverage, and Connectivity Constraints

A wireless sensor network (WSN) deployment requires the identification of optimal network nodes (sensor and sink) positions in an area of interest, to ensure the best network performances (Senouci et al. in Smart Communications in Network Technologies (SaCoNeT), 2014 International Conference on, IEEE, pp 1–6, 43). The deployment process can be divided in two main parts: (1) WSN model construction, and (2) placement optimization. Few research works were interested by WSN deployment in indoor environment, even though, most of them consider the objectives (coverage, cost, connectivity) individually without considering the sensors and sink in the same time. This paper proposes a multi-objective deployment strategy (MODS), where all important objectives are integrated. The MODS uses the multi-objective evolutionary algorithms to get near optimal solution for WSN deployment problem. An original coding solution, integrating both network cost and nodes positions is proposed. A comparative study between two evolutionary strategies (classical GA, and NSGA-II) was performed to identify the use case of each one. Obtained results showed the interest of the proposed methodology.

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