A Comparison of Bio-Inspired Approaches for the Cluster-Head Selection Problem in WSN

A Wireless Sensor Network (WSN) is composed of a set of energy and processing-constrained devices that gather data about a set of phenomena. An efficient way to enlarge the lifetime of a wireless sensor network is clustering organization, which structures hierarchically the sensors in groups and assigns one of them as a cluster head. Such cluster head is responsible of specific tasks like gathering data from other cluster sensors and resending it to the base station through the network. Using a cluster-head organization, data gathering process is improved and by extension, the network lifetime is enlarged. However, due to the additional tasks that every cluster head has to perform, their own energy is spent faster than that of the other sensors in the cluster. Each time that a cluster head is out of battery, it is necessary to select a new cluster head from the survival sensors to continue with head duties. In this chapter, we present a performance comparison of three state-of-the-art MOEAs, namely NSGA-II, SMS-EMOA, and MOEA/D for the cluster-head selection problem in Wireless Sensor Networks.

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