Mobility Aware Vehicular Cloud Systems based on Edge Computing

Abstract

Remote sensing from a single integrated system in autonomous vehicles often leads to false alerts and deadlock conditions, posing major challenges to safety and functionality. To address this, cloud-based vehicle control systems have been explored, as they can synchronize data from multiple vehicles using distributed sensors. However, cloud systems face inherent limitations such as long-haul connectivity issues, increased latency, and packet loss due to interference. As a result, Mobile Edge Computing (MEC) has emerged as a promising solution in next-generation wireless networks, particularly in 5G environments. This paper proposes an edge computing (EC) based method for vehicle charging, aligned with a broad, data-driven development model. In this framework, mobility-aware edge servers interact with nearby vehicles, providing real-time information on the availability of charging stations (CSs), collecting dynamic data from moving vehicles, and implementing decentralized big data processing. This method reduces dependency on the cloud, enabling faster response times and more efficient data handling. If the access schedule between edge servers and the cloud is optimized, with cloud reliance reduced by 50%, the system maintains the same reliability while benefiting from continuous edge-based monitoring. This innovative model improves scalability, enhances energy efficiency, and supports the seamless operation of future intelligent transportation ecosystems.