Optimized Sink node Deployment in WSN Using Genetic Algorithms through Coverage and Cost Constraints

Wireless Sensor Networks (WSN) is the main infrastructure of a smart building. An optimal installation of a WSN is needed to ensure the best operating of the smart building. WSN suffer from a multitude of constraints, the major ones are: (1) optimal coverage (to ensure best QoS), (2) deployment cost (installation cost), and (3) network lifetime (network energy consumption). In order to ensure a WSN deployment that meets these three goals, we must formulate our problem as a multi-objective one, which can be solved by different methods. Find the optimal sensors positions in a deployment space can be considered as NP-hard problem [1]. If we consider a (M) deployment space and a set of (N) sensors to be deployed, the possible combination is equal to M!/(M-N)! [2].
Until this day, there are no specific solutions to deal with such problems. To get approximation of an optimal solution, the use of meta-heuristic methods is needed. In this paper, we attempt to propose a proper modeling of the three principal WSN deployment objectives. The deployment space is considered as a building. To solve the problem and to get optimal sensors positions we use MultiObjective Genetic Algorithms (MOGA).