AoI Energy-Efficient Edge Caching in AAV-Assisted Vehicular Networks

Abstract

Mobile edge caching (MEC) has grown substantially with the rapid development in scale and complexity of data traffic. By exploiting the expansive coverage of autonomous aerial vehicles (AAVs), MEC enables services for massive vehicle users (VUs) simultaneously, which is promising for enhancing network transmission efficiency. Nonetheless, due to challenges arising from the timeliness and freshness of content services caused by AAVs’ limited endurance and airborne capacity, caching strategy considering the real-time of content in large-scale dynamic Internet of Vehicles (IoV) environments remains open. With the above consideration, in this article, the cache refreshing cycle and content placement are jointly optimized in the cache-enabled AAV-assisted vehicular integrated networks (CAVINs) to minimize the content Age of Information (AoI) and energy consumption of the macro AAV. Since the joint optimization problem is variational coupled with nonconvex binary constraints, it is decoupled and solved by a double-iteration method. Specifically, the optimal cache refreshing cycle is derived in semi-closed form with the Karush-Kuhn-Tucker (KKT) conditions. The locally optimal solution of the content placement is obtained through successive convex approximation (SCA). Simulation results corroborate the effectiveness and superiority of the proposed scheme.