Swarm Intelligence Based Throughput Enhancement Using Cluster Based Approach in Wireless Ad-Hoc Networks

Abstract

MANET has become an active area of research and clustering has become an essential research area as it enhances the system performance by minimizing battery power. As host mobility causes recurrent random topological alterations, a number of approaches have been dedicated in specific to the design of clustering approaches to organize all the hosts in a MANET into a clustering architecture. One of the main problems in MANET in the current scenario is the insignificant end-to-end Throughput. A notable amount of energy is utilized every time a signal is sent and received by a mobile node. Many such signals and power are wasted to update the positional information of the nodes in the scenario. This research work focuses on using a novel clustering approach for managing power in ad-hoc network. The selection of cluster head from mobile nodes and cluster formation is done using swarm intelligence approaches such as Particle Swarm Optimization, Artificial Bee Colony, etc. Swarm Intelligence based approaches minimizes the cost reduction for locating head nodes in cluster and it becomes a semi distributed method as it is implemented within cluster rather than the base station. The selection criteria of the objective function are based on the residual energy, intra-cluster distance, node degree and head count of the probable cluster heads. This power management approach within cluster would help in reducing the system power consumption, improves Packet Delivery Ratio (PDR) and hence end-to-end network throughput can be enhanced. Hybrid Optimization algorithms can be utilized for better performance with increased network lifetime.