In this study, an optimal method of clustering homogenous wireless sensor networks using a multi-objective two nested genetic algorithm is presented. The top-level algorithm is a multi-objective genetic algorithm whose goal is to obtain clustering schemes in which the network lifetime is optimized for different delay values. The low level GA is used in each cluster in order to get the most efficient topology for data transmission from sensor nodes to the cluster head (CH). The presented clustering method is not restrictive, while existing intelligent clustering methods impose certain conditions such as performing 2- tiered clustering. A random deployed model is used to demonstrate the efficiency of the proposed algorithm. A comparison is made between the presented algorithm and the other GA-based clustering methods. The results obtained indicate that by using the proposed method, the network’s lifetime would be extended much more than it would be when using the other methods.

Keywords