Abstract
Wireless sensor networking is a promising technology that can lead to automatic, intelligent, easier and more secure systems. A wireless sensor network (WSN) consists of small battery powered devices with limited energy resources. One of the major challenges in WSN lies in the energy constraint and computation resources available at the sensor nodes. One way to achieve energy efficiency would be through the use of a clustering technique. In this paper, we propose computational intelligence (CI) approaches to deal with the problem of sensor nodes clustering in a WSN with the ultimate goal to reduce energy expenditures and thus to extend the lifetime of the network. The main motivation is that CI brings about flexibility, autonomous behavior, and robustness against topology changes, communication failures, and scenario changes. The main features of the proposed work span over two aspects. First, four metaheuristics have been adapted to deal with the tackled problem namely genetic algorithms, evolution strategies, particle swarm optimization and artificial bees colony. In this context, a suitable solution representation scheme has been developed and accordingly the algorithms operators, and overall dynamics have been defined. Second, the four developed algorithms are combined with an exhaustive search that is triggered whenever the number of alive sensor nodes drops below a given threshold. The performance of the proposed algorithms has been assessed and compared to the most known state of the art clustering based method namely low energy adaptive clustering hierarchy algorithm (LEACH). The obtained results show that the proposed approaches optimize in an efficient manner the lifetime of WSNs. They also show that the proposed algorithms compete and even outperform LEACH algorithm.