Clustering is one of the important methods for prolonging the network lifetime in wireless sensor networks (WSNs). It involves grouping of sensor nodes into clusters and electing cluster heads (CHs) for all the clusters. CHs collect the data from respective cluster’s nodes and forward the aggregated data to base station. A major challenge in WSNs is to select appropriate cluster heads. In this paper, we present a fuzzy decision-making approach for the selection of cluster heads. Fuzzy multiple attribute decision-making (MADM) approach is used to select CHs using three criteria including residual energy, number of neighbors, and the distance from the base station of the nodes. The simulation results demonstrate that this approach is more effective in prolonging the network lifetime than the distributed hierarchical agglomerative clustering (DHAC) protocol in homogeneous environments.
Wireless sensor networks (WSNs) demand low power and energy efficient hardware and software. Dynamic Power Management (DPM) technique reduces the maximum possible active states of a wireless sensor node by controlling the switching of the low power manageable components in power down or off states. During DPM, it is also required that the deadline of task execution and performance are not compromised. It is seen that operational level change can improve the energy efficiency of a system drastically (up to 90%). Hence, DPM policies have drawn considerable attention. This review paper classifies different dynamic power management techniques and focuses on stochastic modeling scheme which dynamically manage wireless sensor node operations in order to minimize its power consumption. This survey paper is expected to trigger ideas for future research projects in power aware wireless sensor network arenas.
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