Energy efficiency based lifetime improvement for wireless body area network

Abstract

The efficient selection of cluster head nodes for the transmission of the data to the sink node is the major requirement of the wireless body area network. The present work deals with the efficient selection of the cluster head node and uniform rotation of the cluster head based on the four important parameters, i.e. residual energy of the node, mean value of the Euclidean distance, priority level based on data sensitivity, and device physical capability. The proposed energy‐efficient Bayesian clustering algorithm uses naïve Bayesian approach for the appropriate selection of the cluster head node. The selected cluster head node collects the data from the sensor nodes in each round. The collected data is first aggregated and then transmitted to the base station. In the steady‐state phase, the proposed algorithm follows energy‐analysed‐TDMA based energy‐efficient data transmission method which further reduces the energy consumption as compared to the standard LEACH protocol. The performance of the proposed protocol is compared to the existing protocol on the MATLAB simulator. The proposed scheme increases the overall lifetime of the network by approximately 1.5 times and also reduces the average energy consumption of the network.

Efficient Hybrid Fault-Management Clustering Algorithm (HFMCA) in WBANs Based on Weighted Bipartite Graph

“…Once a node becomes a CH, it advertises itself to all nodes and invites them to join the cluster, which forms a cluster network. Moreover, an energy-efficient Bayesian clustering algorithm (EEBCA) was proposed by Guo et al [9] that uses the naïve Bayesian technique for the relay selection. In each round, each sensor node calculates its relay node selection probability and transmits it to the BS.…”

“…To generate graphs, we used the NetworkX package [25]. We evaluated our model against RED-LEACH [8] and EEBCA [9] regarding energy consumption cost, network lifetime, and communication overhead.…”