Enhanced Weighted Clustering Algorithm For Mobile Ad Hoc Networks

Mobile Ad Hoc networks (MANET) consist of wireless hosts that communicate with each other in the absence of a fixed infrastructure. If an existing communication infrastructure is expensive, inconvenient or even impossible to setup, mobile users can still communicate with each other by the MANET. Examples include battlefield scenarios, disaster relief and short-term scenarios such as public events. Because there is no fixing infrastructure, nodes in a MANET must act as both hosts and routers. The task of finding and maintaining routes in an Ad Hoc network is nontrivial since host mobility causes frequent unpredictable topological changes. Several protocols have been proposed in the literature for routing over multiple hops in MANETs, the notable ones being AODV and DSR (Dynamic Source Routing). However, as the size of a MANET grows, flat routing schemes do not scale well in terms of performance. Routing protocols such as DSR that discover routes by reactive flooding, and perform well for small networks, can result in low bandwidth utilization in large networks under high load due to longer source routes and thus a large byte overhead.In the context of mobile multi-hop wireless networks, D. J. Baker and A. Ephremides introduced the notion of cluster-based organization through the concept of a distributed"linked cluster architecture", which was mainly used for hierarchical routing, and for network adaptability to changes in connectivity. Creating a good clustering algorithm is one of the most difficult tasks for the designer; this is evidence by the number of various approaches. A Weighted Clustering Algorithm (WCA), which takes into consideration the ideal degree, transmission power, mobility, and battery power of mobile nodes, has been proved to be better than existing ones and also adaptable to different kinds of network conditions. In this paper, an enhanced version of WCA algorithm is presented. Simulation experiments are conducted to compare the performance of our algorithm with the original WCA in terms of the number of clusters, reaffiliation frequency, and dominant set updates etc. The result demonstrates that the proposed heuristic does provide better performance than WCA with a lower overhead.