Mobility Estimation-Based Clustering for Energy-Efficient Routing in IoT-Enabled VANETs

Abstract

Combining Internet of Things (IoT) devices with Vehicular Ad hoc Networks (VANETs) offers substantial benefits for traffic management, transportation efficiency, and road safety. However, challenges related to energy consumption, routing efficiency, and stability remain significant obstacles, particularly as modern VANETs increasingly rely on Electric Vehicles (EVs) and Solar-Powered Roadside Units (SP-RSUs), which have limited energy budgets. Existing routing protocols often fail due to the impact of high vehicular mobility and restricted energy resources. This affects periodic rerouting, unstable communication, and decreases network lifetime. This paper suggests a Mobility Estimation-Based Clustering Routing (MEBCR) protocol to treat these issues. In a united framework, the proposed MEBCR merges a hybrid mobility estimation module, including the Kalman Filter and Gauss-Markov approaches, together with cluster formation and energy-aware routing strategies. This design is essential for sustaining the processes in energy-restricted environments, guaranteeing reliable communication and a prolonged network. By comparing with existing protocols, simulation outcomes show that MEBCR preserves 10-28% extra energy and 13-42% node survival. Additionally, it reduces cluster variations by approximately 48-60%. These outcomes confirm the efficiency and robustness of the proposed protocol, making it a suitable solution for green and intelligent transportation systems in next-generation networks.