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
Abstract
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