Efficient clustering method can competently scale down the energy consumption of sensor nodes (SNs) in wireless sensor networks (WSNs). Selection of the best-suited SNs for the role of cluster heads (CHs) can lead to ...
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Efficient clustering method can competently scale down the energy consumption of sensor nodes (SNs) in wireless sensor networks (WSNs). Selection of the best-suited SNs for the role of cluster heads (CHs) can lead to effective clustering process. In past few decades, a number of clustering protocols have been designed to handle these issues in distributed WSNs. However, most of these employed estimation/randomized algorithms for CH selection due to lack of globalized energy awareness problem in distributed WSNs. This paper resolves the problem by using proposed Modified Intelligent CH election based on Bacterial foraging optimization algorithm (M-ichb), which searches actual higher residual energy SNs for CH selection in distributed WSNs. M-ichb algorithm does not require any estimation/randomized algorithms during CH selection process, which resolves the issue of energy unawareness problem in the WSN. Moreover in general, most of the existing clustering algorithms have been designed either for homogeneous or heterogeneous WSNs. However in contrary, proposed M-ichb algorithm is designed for both homogeneous as well as heterogeneous WSNs in this paper. Furthermore, in many critical applications i.e., military surveillance, traffic management, natural disaster forecasting and structural health monitoring;reliability of data from each SN is the most crucial aspect. In this prospect, elongated stability region (from the network initiation till first node dies) of the network is the prime necessity. For this, we have applied proposed M-ichb algorithm on conventional stability based clustering protocols i.e., LEACH, SEP and DEEC and proposed M-ichb based stable protocols viz MILEACH, MIrLEACH, MISEP and MIDEEC protocols. Simulation results confirm that proposed MILEACH, MIrLEACH, MISEP and MIDEEC protocols are capable in searching actual higher residual energy nodes for CH selection without using any estimation/randomized algorithm, while maintaining distributive nature o
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