Deep Reinforcement Learning for Social-Aware Edge Computing and Caching in Urban Informatics

Empowered with urban informatics, transportation industry has witnessed a paradigm shift. These developments lead to the need of content processing and sharing between vehicles under strict delay constraints. Mobile edge services can help meet these demands through computation offloading and edge caching empowered transmission, while cache-enabled smart vehicles may also work as carriers for content dispatch. However, diverse capacities of edge servers and smart vehicles as well as unpredictable vehicle routes make efficient and time content distribution a challenge. To cope with this challenge, we exploit the relations between vehicles and road side units in content dispatch, and develop a social-aware mobile edge computing and caching mechanism. By leveraging deep reinforcement learning approach, we propose optimal content processing and caching schemes that maximize the dispatch utility in an urban environment with diverse vehicular social characteristics. Numerical results based on real urban traffic datasets demonstrate the efficiency of our proposed schemes.