Enhanced Biogeography Based Optimization (EBBO) for longevity and optimized energy consumption of Wireless Sensor Network

The major challenges of the remotely deployed wireless sensor network are selecting optimal cluster heads and energy-efficient routing. To deal with these challenges researchers often used an approach of hierarchal cluster-based routing where cluster heads are given additional jobs like data aggregation transferring aggregated data to a base station which leads to high power consumption. High power consumption leads to the short life of a network hence to extend the network’s life, care should be taken while selecting cluster heads in the wireless sensor network. Biogeography-based optimization (BBO) is a well-known optimization algorithm that has a broad scope to reveal its full potential in dealing with combinatorial optimization problems such as clustering. Therefore, this paper proposed an Enhanced Biogeography Based Optimization (EBBO) based cluster-head selection method. The proposed method is used to optimize the energy consumption in the network. In general, the sensor nodes are un-attendant and have limited energy backup. The energy optimization of such nodes can lead to the longevity and stability of the network. To evaluate the performance of the algorithm Enhanced Biogeography Based Optimization (EBBO), it is compared with some existing clustering methods. The simulation results show an effective increase in the lifetime of the sensor network as compared to other methods.