Hybrid Teaching Learning Approach for Improving Network Lifetime in Wireless Sensor Networks

In a wireless sensor network (WSN), data gathering is more effectually done with the clustering process. Clustering is a critical strategy for improving energy efficiency and extending the longevity of a network. Hierarchical modeling-based clustering is proposed to enhance energy efficiency where nodes that hold higher residual energy may be clustered to collect data and broadcast it to the base station. Moreover, existing approaches may not consider data redundancy while collecting data from adjacent nodes or overlapping nodes. Here, an improved clustering approach is anticipated to attain energy efficiency by implementing MapReduction for regulating mapping and reducing complexity in routing mechanisms for eliminating redundancy and overlapping. In order to optimize the network performance, this work considers intelligent behaviors’ to adapt with network changes and to introduce computational intelligence ability. In the proposed research, improved teaching learning based optimization is used to evaluate the coordinates of target nodes and nodes upgradation for determining energy consumption. Node upgradation is performed by integrating Map reduction to attain modification in Hop size of nodes. This variation reduces communication complexities. Therefore, network lifetime is increased, and redundancy is reduced. While comparing with existing approaches here, sleep and wake-up nodes are considered for data transmission. The proposed algorithm clearly demonstrates 50%, 16% & 12% improvement in nodes lifetime, residual energy and throughput respectively compared to other models. Also it shows progressive improvement in reducing average waiting time, average queuing time and average energy utilization as 30%, 20% and 46% respectively. Simulation has been done in NS-2 environment for distributed heterogeneous networks.