Abstract
Dominating sets are among the most well-studied concepts in graph theory, with many real-world applications especially in the area of wireless sensor networks. One way to increase network lifetime in wireless sensor networks consists of assigning sensors to disjoint dominating node sets, which are then sequentially used by a sleep–wake cycling mechanism. This paper presents a greedy heuristic for solving a weighted version of the maximum disjoint dominating sets problem for energy conservation purposes in wireless sensor networks. Moreover, an integer linear programming model is presented. Experimental results based on a large set of 640 problem instances show, first, that the integer linear programming model is only useful for small problem instances. Moreover, they show that our algorithm outperforms recent local search algorithms from the literature with respect to both solution quality and computation time.
Highlights
Wireless sensor networks (WSNs) have received increasing attention in the last decade due to their potential applications in various fields such as environmental monitoring, medical and health applications, security surveillance and emergency operations [1]
In order to conduct a fair comparison to the three local search algorithms (LS, FD and Variable Depth (VD)) we used the original source code provided by the authors of [23]
From above observations we can say that the poor performance of the three local search algorithms proposed by Pino et al [23] is mainly due to greedy heuristic used for producing the initial solutions for their local search approaches. Since this greedy heuristic was developed for the maximum disjoint dominating sets (MDDS) problem, and the local search approaches try to improve these solutions by making swaps among nodes of different disjoint dominating sets, they are limited to the size of the solutions found by the initial greedy heuristic, which cannot be changed later
Summary
Citation: Balbal, S.; Bouamama, S.; Blum, C. A Greedy Heuristic for and Christian Blum 3, * Department of Computer Science, Mechatronics Laboratory (LMETR)—E1764200, Ferhat Abbas University Artificial Intelligence Research Institute (IIIA-CSIC), Campus of the UAB, 08193 Bellaterra, Spain
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