The multi hop packet radio networks also named mobile ad-hoc networks (MANETs) have a dynamic topology due to the mobility of their nodes. 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 a wireless scenario. Further bandwidth is also wasted by sending control signals rather than using it effectively for data communication. To minimize this utilization, we propose a modified algorithm that uses Weighted Clustering Algorithm (WCA) for cluster formation and Mobility Prediction for cluster maintenance. Clustering is an effective technique for node management in a MANET. Cluster formation involves election of a mobile node as Cluster head and it controls the other nodes in the newly formed cluster. The connections between nodes and the cluster head changes rapidly in a mobile ad-hoc network. Thus cluster maintenance is also essential. Prediction of mobility based cluster maintenance involves the process of finding out the next position that a mobile node might take based on the previous locations it visited. In this paper we propose to reduce the overhead in communication by predicting mobility of node using linear auto regression and cluster formation.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2025 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.