Dependency Tasks Offloading and Communication Resource Allocation in Collaborative UAVs Networks: A Meta-Heuristic Approach

TL;DR

The paper addresses latency in MEC-enabled UAV networks where tasks are DAGs with inter-subtask dependencies and UAVs have limited compute and energy. It proposes a two-step solution: a discrete whale optimization algorithm (D-WOA) to decide sub-task offloading across multiple UAVs and a convex optimization (via CVXPY/SCS) for bandwidth allocation, with a penalty-based constraint handling to respect energy limits. The approach is substantiated by simulations showing significant latency reductions compared with benchmarks like associated UAV and exhaustive search, demonstrating the benefits of collaboration and topology-aware offloading. The work offers a computationally tractable framework for joint offloading and resource allocation that scales with task complexity and UAV network size, supporting practical deployment of collaborative UAV MEC systems.

Abstract

In recent years, unmanned aerial vehicles (UAVs) assisted mobile edge computing systems have been exploited by researchers as a promising solution for providing computation services to mobile users outside of terrestrial infrastructure coverage. However, it remains challenging for the standalone MEC-enabled UAVs in order to meet the computation requirement of numerous mobile users due to the limited computation capacity of their onboard servers and battery lives. Therefore, we propose a collaborative scheme among UAVs so that UAVs can share the workload with idle UAVs. Moreover, current task offloading strategies frequently overlook task topology, which may result in poor performance or even system failure. To address the problem, we consider offloading tasks consisting of a set of sub-tasks, and each sub-task has dependencies on other sub-tasks, which is practical in the real world. Sub-tasks with dependencies need to wait for the resulting signal from preceding sub-tasks before being executed. This mechanism has serious effects on the offloading strategy. Then, we formulate an optimization problem to minimize the average latency experienced by users by jointly controlling the offloading decision for dependent tasks and allocating the communication resources of UAVs. The formulated problem appears to be NP-hard and cannot be solved in polynomial time. Therefore, we divide the problem into two sub-problems: the offloading decision problem and the communication resource allocation problem. Then a meta-heuristic method is proposed to find the sub-optimal solution of the task offloading problem, while the communication resource allocation problem is solved by using convex optimization. Finally, we perform substantial simulation experiments, and the result shows that the proposed offloading technique effectively minimizes the average latency of users, compared with other benchmark schemes.

Figures (9)

• Figure 1: System model.
• Figure 2: Convergence of the proposed Whale optimization with different numbers of searching agents.
• Figure 3: Performance comparison under different algorithms.
• Figure 4: Energy consumption by each UAV.
• Figure 5: Average data rate under different numbers of user.