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.