Multi-Hop Relay-Aided Task Offloading for Tactical Edge Computing

Abstract

Recent years have witnessed explosive growth in the deployment of IoT devices over tactical edge networks, which demand flexible task offloading strategies to accommodate limited connectivity and computing resources. While many existing works assume direct access to edge servers, such assumptions often break down in adversarial and dynamic environments. This work introduces a novel edge task offloading framework that enables multi-hop communication and enables task execution not only at edge servers but also at relay nodes and local devices. We formulate the problem of joint task offloading, resource allocation, and time frame/slot assignment, under a time-division multiple access (TDMA) uplink scheme, as a nonlinear integer program to maximize total rewards, subject to various communication and resource constraints. To solve it optimally, we first construct a feasibility-aware directed graph that captures valid communication links based on signal-to-noise ratio (SNR) constraints, and then design a dynamic programming (DP) algorithm that integrates slot-minimizing offloading path selection with the assignment of the largest feasible number of time frames for each user to reduce total time slots consumption without violating task completion deadlines. To improve scalability, efficient heuristics are developed using scaling and rounding techniques. Extensive experiments demonstrate that our proposed DP algorithm achieves optimal task offloading and resource allocation, while our heuristic algorithms offer a computationally efficient alternative with competitive performance.