Energy Consumption Minimization for Integrated Sensing, Communication, Computing, and Caching in Multilayer Aerial Internet of Things

With the rapid advancement of Internet of Things applications, the demand for integrated sensing, communication, computing, and caching (ISC3) functions has surged. However, existing systems optimize these functions independently, leading to suboptimal resource utilization and performance bottlenecks. In this paper, we propose a multi-layer aerial ISC3 architecture where a versatile unmanned aerial vehicle (UAV) provides edge computing and caching services to ground wireless devices (WDs) alongside its radar sensing capabilities. A high-altitude platform maintains the complete service library, delivering required services to the UAV when cache misses occur. Partial data compression is employed to reduce uplink communication overhead, where WDs partially compress their offloaded task data before transmitting to the UAV. The objective is to minimize total system energy consumption by jointly optimizing time scheduling ratios, task offloading ratios, compression selection ratios, service caching decisions, and UAV trajectory, subject to task latency, sensing quality, energy budgets, and cache capacity constraints. An efficient iterative algorithm utilizing specialized optimization techniques such as Lagrangian duality and successive convex approximation is developed to solve the resulting mixed-integer nonlinear programming problem. Extensive simulations demonstrate fast convergence under diverse network configurations, with the proposed scheme consistently outperforming all baselines by 22.5%-67.0% in total energy consumption.