Roadside units (RSUs) play an important role in the request fulfillment of vehicles. In rural areas, RSUs are powered by renewable energy sources like solar energy. Hence, the request fulfillment of vehicles must be done in such a way that the energy consumption across RSUs is minimized. The requests are categorized into traditional application (low computation) requests and smart application (high computation) requests. To avoid excessive computation at RSUs, low computation requests are scheduled across RSUs while high computation requests are scheduled across fog servers for processing. In this paper, we propose an online energy-efficient Inter-RSU Scheduling Algorithm (ee-IRSA) and a distributed Ant Colony Optimization-based Load balancing technique (d-ACOL) for optimizing the request fulfillment of traditional and smart application requests, respectively. ee-IRSA ensures minimum and uniform energy consumption across RSUs while d-ACOL ensures minimum queue waiting time of requests across fog servers. In addition, a second-price auction game-based relay vehicle selection technique is proposed which further minimizes the energy consumption of RSUs. Simulation results show that ee-IRSA with relay vehicle selection reduces the energy consumption by \(33\%\), and d-ACOL reduces the queue waiting time by an average of \(48\%\) as compared to other load balancing techniques.

路边单元 (RSU) 在满足车辆要求方面发挥着重要作用。在农村地区，RSU 由太阳能等可再生能源供电。因此，必须以最小化跨 RSU 的能源消耗的方式完成车辆的请求。请求分为传统应用（低计算）请求和智能应用（高计算）请求。为了避免 RSU 上的过度计算，低计算请求跨 RSU 调度，而高计算请求跨雾服务器调度以进行处理。在本文中，我们提出了一种在线高能效交互R SU S调度A算法 ( ee -IRSA) 和基于分布式蚁群优化的负载平衡技术 ( d - ACOL ) 分别用于优化传统和智能应用程序请求的请求实现。ee-IRSA 确保跨 RSU 的能耗最小且均匀，而 d-ACOL 确保跨雾服务器的请求的队列等待时间最短。此外，提出了一种基于二次价格拍卖博弈的接力车辆选择技术，进一步降低了 RSU 的能耗。仿真结果表明，带有中继车辆选择的ee-IRSA将能耗降低了\(33\%\)，d-ACOL将排队等待时间平均降低了\(48\%\)与其他负载平衡技术相比。