RSU resource capacity handling mechanism for V2R communication in vanet urban highway mobility

Vehicular ad hoc networks (VANETs) are crucial technologies for wireless sharing of information and promoting innovative applications for road safety, traffic management, and entertainment. However, the proliferation of these applications comes with a massive amount of data that can overwhelm the capacity of resources, exacerbate congestion, and cause delays, particularly in densely populated areas like urban highways. Various methods and techniques are used to address overloading issues in vehicular networks, including cooperative and non-cooperative approaches. Cooperative methods involve adding nodes as temporary roadside units (RSU) and edge servers, but they face challenges in sharing processing power with other vehicles. Non-cooperative methods include caching, data scheduling, and edge node overload control mechanisms. Edge computing, which provides low-latency services and additional computational power, can help address this issue by equipping VANET with Vehicular Edge Computing (VEC) servers with greater computation, storage, and net- working capabilities. However, VEC deployment in urban areas and extreme traffic congestion presents challenges, as overwhelming data limits computational, storage, and memory capacities. The RSU Resource Capacity Handling (RRCH) mechanism is proposed as an integrated solution combining these three mechanisms and is tested and evaluated in a simulated urban highway’s standing vehicular traffic congestion scenario. The study evaluates the performance of RRCH in comparison to other approaches namely DQN-ATMOO, DIVINE, BAFOS, AVCIL, and MobTORU in a stationary traffic gridlock scenario, revealing comparative analysis of potential resource capacity depletion, longer delays, decreased throughput, and poorer PDR. Nonetheless, RRCH mechanism reservation of 25% of resource capacity for computation efficiency, resulting in a reduction of delay of up to 36% lower, and higher of 15% in throughput and packet delivery ratio, with an additional 11 to 16% increase in fair service provisioning. The study highlights the importance of efficient storage management in optimizing the operational efficiency with significant improvements in performance compared to other relevant approaches.