Published Paper Details:

Non-rechargeable batteries with restrained energy capability are used to power the wireless sensor network nodes, but in hostile conditions, replacing a node battery is a difficult task. Thus, enhancing the energy efficiency resulting in increasing the lifetime of the network is a suitable decision. Recent work suggests that clustering is an efficient mechanism for reducing energy consumption, increasing network scalability, maintain load balancing, all of which contribute to maximizing total network life. Appropriate cluster head selection in a cluster is also crucial as it prominently affects the wireless sensor network life. Metaheuristic algorithms can be utilized effectively for this. This paper main contribution is to design a bee colony optimization technique named ABCGA dependent on an artificial bee method. In the proposed algorithm ABCGA, the artificial bee method is integrated with the features of the genetic algorithm for optimal cluster head selection. Also, data compression is done before data transmission, resulting in reduced energy consumption and increased network life. Simulation findings demonstrated that the proposed approach ABCGA outperforms the LEACH, PBC-CP, PSO, and HSA-PSO techniques in terms of the following factors- the number of active nodes, the number of nodes dead, Remaining Energy, and Throughput.