Abstract
The target of wireless sensor networks is to gather data regarding certain phenomena. This is why they are meant to last as long as possible. An efficient strategy to extend the wireless sensor network lifetime is to organize sensors hierarchically into clusters and, for each cluster, select one of the sensors as the cluster head. The task of the cluster head is to collect data from the cluster members and retransmit it to the base station across the network. By means of cluster organization, the process of acquiring data is enhanced and, consequently, the lifetime of the network is increased. Nevertheless, in addition to data collection and transmission, every cluster head performs additional tasks. Thus, they spend their energy quicker than the cluster members. This is the reason why cluster head selection is a multi-objective optimization problem. In this paper, we consider the three objectives, distance, delay, and residual energy, and apply three multi-objective evolutionary algorithms, specifically NSGA-II, SMS-EMOA, and MOEA/D, for studying the problem by means of solving several instances. We analyze the conflict between the objectives, present a proper multi-objective performance comparison of the algorithms, and investigate the efficiency of the found solutions regarding network energy consumption.
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