Offloading Association, Routing, and 3D Deployment Optimization for PAoI Minimization in Drone-Assisted Smart Last-Mile Logistics Networks

Abstract

In smart last-mile logistics networks, real-time status telemetry from ground logistics nodes, such as automated parcel lockers, cold-chain sensors, and unmanned delivery vehicles, is crucial for safe dispatching and precise delivery tracking. However, during peak delivery hours in urban areas, heavy workloads and traffic may lead to severe congestion at base stations (BSs). To address this, multiple unmanned aerial vehicle (UAV) relays are introduced to offload critical logistics monitoring data to neighboring light-load BSs. To quantify the freshness of the received status updates, we adopt the Peak Age of Information (PAoI) metric. We formulate a multi-UAV-assisted multi-cell logistics communication framework that jointly optimizes logistics data offloading association, routing, and 3D UAV deployment to minimize the average PAoI. To solve the resulting mixed-integer non-convex problem, an alternating optimization algorithm based on Block Coordinate Descent (BCD) is developed. Specifically, the offloading association and routing subproblem is reformulated as an integral single-commodity minimum-cost flow problem, while the horizontal UAV positions and altitudes are optimized using Successive Convex Approximation (SCA). Over five random realizations, the proposed method reduces the average PAoI by approximately 0.6% and the transmission delay by up to 4.2% compared with the benchmark methods under the default setting. The delay reduction reaches 6.6% at K=60 and exceeds 28% when the benchmark altitude is fixed at 100m, demonstrating its effectiveness under high network loads and the importance of UAV altitude optimization.