Wireless Sensor Network Lifetime Extension via K-Medoids and MCDM Techniques in Uncertain Environment

In this study, the multi-criteria decision-making (MCDM) technique is used in collaboration with K-medoids clustering to establish a novel algorithm for extending the lifetime of wireless sensor networks (WSNs) in the presence of uncertainty. One of the most important problems in WSNs is the energy consumption. Furthermore, extending the network lifetime in WSNs is highly dependent on selecting the appropriate cluster heads (CHs), and this can be a challenging task for the decision makers. In addition, parameters associated with WSNs are completely unexpected due to uncertainty. Therefore, after proposing K-medoids clustering and a MCDM technique, we have developed a novel algorithm for extending the lifetime of WSNs. As criteria, we have taken into account four important aspects of the proposed WSN: the distance from sink, average distance of cluster nodes, reliability of cluster and residual energy. To represent uncertain parameters in this work, we have considered triangular fuzzy numbers (TFNs). Finally, an experiment involving a WSN under uncertainty was investigated, and the findings have been graphically displayed. In this research, it has been observed that the proposed strategy with the novel algorithm exhibits 42% greater network lifetime as compared with a hybrid energy efficient distributed (HEED) algorithm and 11% and 18% with respect to optimal clustering artificial bee colony (OCABC) and particle swarm optimization (PSO) applied to a clustering optimization problem. We have also conducted statistical hypotheses for the purpose of confirming the presented outcomes.