A Novel Weight-Based Clustering Algorithm for Mobile Ad Hoc Network

ABSTRACT In recent years, many studies have been conducted in Mobile Ad Hoc Networks field in order to make a virtual infrastructure consisting of nodes. The common goal of all was to select a node called clusterhead which guarantees relationships between nodes. In this paper, we have presented a new clustering algorithm in Mobile Ad Hoc Network based on nodes weight. For calculating node weight we present four new parameter, cluster density, consumed energy , stability and number of nodes moving towards a node. The goal of this algorithm is to decrease the number of cluster forming, maintain stable clustering structure and maximize lifespan of mobile nodes in the system. In simulation, the proposed algorithm has been compared with WCA, MOBIC and the Lowest_ID algorithm. The results of simulation reveal that the proposed algorithm achieves the goals. KEY WORDS Mobile Ad Hoc Network, Clustering Algorithm, weighted function. 1. INTRODUCTION Mobile ad hoc networks (MANETs) consist of mobile devices that form the wireless networks without any fixed infrastructure or centralized administration. In these networks each node communicates the other nodes immediately or via intermediate nodes. Relying packets to neighboring nodes along with path from the source node to the destination node are done by intermediate nodes. A well-known method for saving the energy and communication bandwidth is dividing mobile nodes to groups that named clusters. By organizing nodes into clusters, topology information can be aggregated. This is because the number of nodes of a cluster is smaller then the number of nodes of the entire network. Each node only stores fraction of the total network routing information. Therefore, the number of routing entries and the exchanges of routing information between nodes are reduced[3]. In Ad hoc networks, clustering algorithm and select suitable nodes in clusters as cluster heads are so important. This is just because, cluster heads act as local coordinators and handle various network functions such as packets routing and forwarding. As election of optimal cluster heads is an NP-hard problem [1], many heuristic clustering algorithms have been proposed [2-8]. Several clustering strategies have been proposed to increase the stability, routing performance, scalability, bandwidth utilization, and resource allocation efficiency. However, most of the clustering schemes are not mobility-aware or assume low mobility which cannot reflect the distinct dynamic character of MANETs. Mobility is characterized as an inherent phenomenon of MANETs,