In disaster scenarios where communication infrastructure is damaged, Unmanned Aerial Vehicle (UAV)-assisted wireless networks can provide temporary connectivity and hence the indispensable mobile edge computing functionality. However, limited resources on UAVs require prioritization of critical data in such scenarios. This research addresses reliable transmission and task offloading by modeling user tasks as layered compositions, where the base layer is essential and enhancement layers are optional. TDMA-based prioritization is employed to ensure reliable decoding of high-priority layers of the computational tasks (i.e., intra-user priority) along with inter-user priority needed for urgent users like rescue teams. Under these reliability constraints, this work formulates a joint communication–computation optimization problem to allocate transmission power and UAV CPU cycles efficiently in order to minimize total weighted offloading latency. The original problem is non-convex; thus, we leverage epigraph and perspective functions to recast the problem into a convex one. We also derive analytically, using the KKT conditions, the optimal water-filling-like solutions for the reformulated problem. The numerical results show that, at a signal-to-noise ratio of 5 dB, the proposed algorithm achieves relative latency reductions vs. the baseline algorithms (39.99% reduction vs. Equal Allocation, 49.99% reduction vs. Enhancement First, and 69.99% reduction vs. No Priority), which reflect considerable latency reduction with priority-aware offloading.
Desher et al. (Sat,) studied this question.