Joint service caching, computation offloading and resource allocation for dual-layer aerial Internet of Things

Abstract

The exponential growth of Internet of Things devices has triggered an unprecedented surge in mobile data traffic, posing significant challenges for latency-sensitive services. Mobile Edge Computing (MEC) has emerged as a promising solution by decentralizing computation and caching resources to the network edge. However, traditional terrestrial MEC systems struggle with limited coverage and flexibility. To overcome these issues, this paper proposes a novel dual-layer aerial MEC architecture, where multiple Unmanned Aerial Vehicles (UAVs) provide computation and caching support for resource-constrained terminal devices, and a high-altitude platform serves as a central hub for long-term service storage and retrieval. The system aims to minimize total latency by jointly optimizing service caching, task offloading, resource allocation, and 3D UAV deployment, formulated as a mixed-integer nonlinear programming problem and efficiently solved using an iterative algorithm based on linear relaxation and successive convex approximation. Simulation results demonstrate that the proposed scheme converges quickly across different scales and outperforms all baselines with minimal runtime increase, reducing total latency by 42.86% compared to the random UAV deployment.