A novel genetic algorithm for lifetime maximization of wireless sensor networks with adjustable sensing range

Abstract

Most existing algorithms for optimizing the lifetime of wireless sensor networks (WSNs) are developed assuming that the sensing ranges of sensors are fixed. This paper1 focuses on adjustable WSNs and proposes a lifetime maximization approach, in which the active periods and sensing ranges of sensors are scheduled simultaneously subject to the constraints of target coverage and power limit. First, the lifetime maximization problem is converted to a problem of finding a set of coverage patterns that can lead to the best schedule when fed into a linear programming model. A genetic algorithm is then developed for coverage pattern finding. With each individual representing a coverage pattern, evolutionary operators and repair strategy are tailored to evolve the pattern set efficiently. Experimental results in a variety conditions show that the proposed approach is advantageous in both terms of computational time and solution quality. © 2018 Copyright is held by the owner/author(s).