Adaptive Group Teaching Based Clustering and Data Aggregation with Routing in Wireless Sensor Network

Nowadays, Wireless Sensor Networks (WSNs) is enhancing for different applications. Simultaneously, energy consumption for processing the tasks in most of the applications has also been increased. The nodes in the network may die while utilizing more energy for executing the tasks. The death of nodes causes various issues in data transmission. In most cases, the nodes next to the sink may have more traffic and it makes the energy to drain quickly. Such situation can be handled with an optimization algorithm. In this paper, an energy efficient data aggregation approach is proposed by the combination of multi-objective clustering and optimization-based routing algorithm. Initially, the K-Means clustering technique is employed for cluster formation. Further, cluster head is selected using Adaptive Group Teaching Optimization Algorithm based on multiple objectives mainly considers on reducing the energy usage for preventing nodes from death. A minimum spanning tree algorithm is employed to create the communication route form sink node to cluster head. Finally, the path for transmitting the data to the base station is identified with the Improved Deer Hunting Optimization algorithm. Performance of presented routing algorithm is compared along with existing routing approaches to compare the performance and is found to be performing better than them.