Article
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Adaptive Load Balancing Approach to Mitigate Network Congestion in VANETS
Version 1
: Received: 11 July 2024 / Approved: 11 July 2024 / Online: 15 July 2024 (15:59:38 CEST)
How to cite: Haider, S. E.; Khan, M. F.; Saeed, Y. Adaptive Load Balancing Approach to Mitigate Network Congestion in VANETS. Preprints 2024, 2024071012. https://doi.org/10.20944/preprints202407.1012.v1 Haider, S. E.; Khan, M. F.; Saeed, Y. Adaptive Load Balancing Approach to Mitigate Network Congestion in VANETS. Preprints 2024, 2024071012. https://doi.org/10.20944/preprints202407.1012.v1
Abstract
Load balancing to alleviate network congestion remains a critical challenge in Vehicular Ad-Hoc Networks (VANETs). During route and response scheduling, RSUs risk being overloaded beyond their calculated capacity. Despite recent advancements like RSU-based load transfer, NP-Hard hierarchical geography routing, RSU-based MAC schemes, simplified clustering, and network activity control, a significant gap persists in employing a load-balancing server for effective traffic management. We propose a server-based network congestion handling mechanism (SBNC) in VANETs to bridge this gap. Our approach clusters RSUs within specified ranges and incorporates dedicated load balancing and network scheduler RSUs to manage route selection and request-response scheduling, thereby balancing RSU loads. We introduce three key algorithms: optimal placement of dedicated RSUs, a scheduling policy for packets/data/requests/responses, and a congestion control algorithm for load balancing. Using the VanetMobiSim library of Network Simulator-2 (NS-2), we evaluate our approach based on residual energy consumption, end-to-end delay, packet delivery ratio (PDR), and control packet overhead. Results indicate substantial improvements in load balancing through our proposed server-based approach.
Keywords
Network Congestion; Load Balancing; VANET; Clustering
Subject
Computer Science and Mathematics, Computer Networks and Communications
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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