BAT: Barabasi–Albert With Topsis Scale-Free Topology Evolution for Load-Balanced WSNs

Recent advancements in sensor technology have made wireless sensor networks (WSNs) a preferable choice for several applications. As sensor nodes rely on limited batteries in order to operate, clustering techniques are proven to be energy efficient and effective in the case of WSNs. Avoiding premature death of sensor nodes by load balancing and prolonging network lifetime is an essential aspect that needs to be addressed. Hence, there is always a need for protocol that can uniformly evolve energy consumption among all the nodes by load balancing the network with respect to energy. This work presents a clustering framework, named as BAT, that uses Barabasi–Albert (BA) model and Topsis (T) technique to effectively balance the load on all the sensor nodes present in the network. In the proposed BAT approach, network backbone topology is initially constructed, and later topology evolution happens according to the BA model. After network evolution, the best possible attachments are retained in order to form the final network. Best possible cluster heads (CHs) are determined by the weighted-objective function. The outcome of the proposed BAT protocol is evenly consumed energy among nodes over the whole network to avoid the energy hole. The simulation results reveal that the proposed approach has attained a better stable region and balanced energy-efficient consumption when compared to other simulated protocols.