Blockchain-Based AR Offloading in UAV-Enabled MEC Networks: A Trade-Off Between Energy Consumption and Rendering Latency

Abstract

Unmanned aerial vehicles (UAVs) have recognized as a pivotal technology for advancing wireless augmented reality (AR) applications. However, the considerable energy requirements during the rendering process present a formidable challenge, demanding a precise balance between energy efficiency and latency. Additionally, UAV-enabled systems may face significant security risks in untrusted environments. To solve these issues, we present a secure optimization framework for AR applications, where the blockchain is integrated into the system to provide distributed management and control functions. The critical information during the AR rendering process can be recorded in blockchain promptly to enhance security and privacy. In the proposed framework, a joint optimization problem is formulated to achieve the optimal trade-off between energy consumption and content delivery latency, where the rendering decision, resource allocation, and UAV placement are jointly optimized. Due to the tight coupling variables, the optimization problem is non-convex and difficult to be tackled by adopting the traditional method. To this end, we decouple the formulated problem and design a block coordinate descent (BCD)-based optimization algorithm. In the proposed algorithm, we innovatively combine the Lagrangian multiplier iterative (LMI) method and the deep reinforcement learning (DRL) approach to enhance the solving efficiency by implanting the LMI method into the learning environment of DRL. Simulation results demonstrate that the proposed method can perform well for AR applications compared to other baseline solutions and traditional DRL approaches.