A Improved Prognostic Weighted Clustering Method to Lower Overheads in Mobile Ad-hoc Networks

Abstract

  By choosing the best candidate to be the cluster leader, our enhanced forecast weighted clustering algorithm (EFWCA) can lower the computational overhead. This innovative method increases network stability and can manage massive traffic. Routing problems are common in wireless mobile ad hoc networks (MANET) because of their unpredictable nature and limited resources. Since there is no central router in ad hoc networks, routing is a crucial method for data transmission. Each node acts as a router. Numerous routing strategies, many of which employ the flooding method, have been recommended in order to find a route. By flooding the network with routing packets, the flooding approach allows for the discovery of routes. By using this technique, routing packets go around the network endlessly, consuming bandwidth and battery life in the process as well as degrading throughput. Clustering and effective flooding techniques are two options to address the mentioned issue. In order to create clusters and choose a cluster head for a homogeneous network, the researchers have proposed many methods. Here, the recommended method is run via a network simulation and its performance is assessed over a range of parameters.