MUTAA: An online trajectory optimization and task scheduling for UAV-aided edge computing

Abstract

A novel UAV-aided edge computing system is proposed in this work, where UAV-aided edge nodes are dispatched to provide communication and computation assistance for completing tasks generated by ground clients (GCs). We formulate a trajectory design and task allocation problem (TDTAP), aiming at maximizing the sum of completed tasks of GCs by optimizing the proper trajectory for each UAV and scheduling tasks from each GC. It is impossible to solve the TDTAP problem directly in polynomial time since UAVs lack all GCs’ information, e.g., position and amount of tasks. To this end, we put forward an online iterative algorithm named Maximum UAV trajectory and Task Allocation Algorithm (MUTAA) to solve the TDTAP problem by jointly optimizing UAVs’ trajectory and GCs’ task scheduling. Unlike existing algorithms, MUTAA can make real-time decisions for each UAV without acquiring information from all GCs in advance. During each iteration, MUTAA consists of two sub-algorithms: (1) trajectory design algorithm TDA and (2) task allocation strategy TAS. Specifically, the preschedule step is used in TDA to find the proper trajectory for UAVs, and a competitive online algorithm, TAS, is proposed to schedule GCs’ tasks. Theoretical analysis proves that TAS is e/(e−1)-competitive, that is, it processes (e−1)/e (approximately 63%) tasks when compared with the optimal offline solution. Experimental results demonstrate that MUTAA completes 83% data on an average of the optimal offline solution, illustrating that the proposed algorithm MUTAA can be widely used in time-sensitive scenarios.