A New Crossover Method and Fitness Scaling for Reducing Energy Consumption of Wireless Sensor Networks.

Ef cient energy in the wireless sensor networks (WSNs) is a critical issue because sensor nodesareequippedwithone-timeorlow-energybatteries.Inthesenetworks,ef cientenergysavingmethods involve clustering network nodes to avoid long-distance communications with base stations (BS) to conserve their energy over a long period of time and extend their lifetimes. In other words, the choice of cluster heads (CHs) to improve routing and energy ef ciency plays a central role in extending the network lifetime. This study proposes a new central cluster algorithm based on an improved genetic algorithm (EGA) that nds appropriate numbers of CHs in networks. This enhancement concerns about the application of two new crossover methods: Whole Arithmetic Crossover (WOX), and Local Crossover (LX) methods. This study exploredtheimpactofthetwoaforementionedcrossovermethodsonWSNenergyef ciencyandtheeffectof applying the scaled tness function onnetworklifetime.