ENERGY EFFICIENT CLUSTERING USING SMO AND ROUTING WITH FIREFLY ALGORITHM IN WIRELESS SENSOR NETWORKS

Energy efficiency is still a challenging way to extend the life and stability of wireless sensor networks (WSNs). The use of clustering remains difficult, despite the fact that it has improved energy efficiency through the selection of better cluster heads. In the currently used cluster-head selection approaches, the locations where cluster-heads are wanted are initially searched. The nodes that are closest to these locations are then selected as the cluster heads. This location-based technique has limited selection accuracy, heavy computation, and selects duplicate nodes. To tackle these problems, we propose to apply the sampling-based spider monkey optimisation (SMO) technique. Nodes in the sampling population are taken into account while selecting cluster heads. The nodes are used to select the cluster heads. Consequently, the problems caused by the dispersed locations of the cluster heads and nodes have been resolved. Although the Firefly algorithm is a novel optimisation method that draws inspiration from nature, more research is necessary to fully explore its potential in addressing a range of problems. Routing is one well-known technique for increasing the longevity of wireless sensor networks. Currently, the linked challenge listed above is being solved by using the firefly approach. The routing algorithm is developed using a novel fitness function based on node degree, distance, and residual energy.

Index Terms: Wireless Sensor Network, Routing Algorithm, Ant Colony Optimization, Spider Monkey Optimization (SMO), Firefly Algorithm.