Artificial Intelligence empowered content caching for energy optimization in vehicular networks

Indeed, the development of future autonomous and intelligent transportation systems depends heavily on vehicular networks. They allow vehicles to connect with one another, with roadside infrastructure, and with centralized control systems. RSUs can store regularly accessed data including map updates, traffic data, and software updates. Data caching is one of the main responsibilities of Road side Units (RSUs) in Vehicular application as sending the same content repeatedly wastes a lot of system resources. Utilizing caching resources effectively can lower the link load imposed by content transmission, thus enhancing the quality of experience (QoE) for users. However, inefficient collaboration among network routers may lead to substantial energy wastage. To address the aforementioned problem and reduce the requested content energy consumption, this paper proposes artificial intelligence empowered collaborative content caching scheme utilizing the concept of both on path caching and off path caching. Performance analyses show that the proposed technique outperforms other current caching strategies.