Multi-objective set cover problem for reliable and efficient wireless sensor

Abstract EN

Achieving energy-efficient Wireless Sensor Network (WSN) that monitors all targets at all times is an essential challenge facing many large-scale surveillance applications.Singleobjective set cover problem (SCP) is a well-known NP-hard optimization problem used to set a minimum set of active sensors that efficiently cover all the targeted area.

Realizing that designing energy-efficient WSN and providing reliable coverage are in conflict with each other, a multi-objective optimization tool is a strong choice for providing a set of approximate Pareto optimal solutions (i.e., Pareto Front) that come up with tradeoff between these two objectives.

Thus, in the context of WSNs design problem, our main contribution is to turn the definition of single-objective (SCP) into a multi-objective problem by adopting an additional conflicting objective to be optimized.

To the best of our knowledge, improving coverage reliability of WSNs has not been explored while simultaneously solving SCP problem.

This paper addresses the problem of improving coverage reliability of WSNsusing a realistic sensing model to handle coverage uncertainty.

To this end, this paper formulates the so-called multi-objective SCP with the goal of selecting the minimum number of sensors so that the selected set reliably covers all the targets.To cope with two optimization objectives rather than one objective, this paperinvestigates the use of a multi-objective evolutionary algorithm, the so-called nondominated sorting genetic algorithm for tackling the formulated problem.

Moreover, it adopts a heuristic crossover operator designed specifically to improve the performance of the algorithm.The effectiveness of the algorithm is verified in terms of sensors cost and coverage reliability under extensive simulations.