LAUSR

Unmanned-aerial-vehicle (UAV)-enabled mobile-edge computing (MEC) has emerged as a promising paradigm to extend the coverage of computation service for Internet of Things (IoT) applications, which are usually time sensitive and computation intensive. In this article, a novel design framework is proposed for a multi-UAV-enabled MEC system, where edge servers are equipped on multiple UAVs to provide flexible computation assistance to IoT devices with hard deadlines. The aim is to maximize the number of served IoT devices through jointly optimizing UAV trajectory and service indicator as well as resource allocation and computation offloading, where the chosen IoT devices will complete their computation tasks on time under given energy budgets and co-channel interference is taken into account. We formulate the optimization problem as a mixed integer nonlinear programming (MINLP), which is challenging to solve directly. The problem is first reformulated to a more mathematically tractable form by adding a penalty term to the objective function. We then decouple the problem into two subproblems and develop an iterative algorithm by solving the two subproblems with alternating optimization and successive convex approximation techniques, where the proposed algorithm converges to a Karush–Kuhn–Tucker (KKT) solution. In addition, an efficient initialization scheme is proposed based on multiple traveling salesman problem with time windows (m-TSPTWs) method. Finally, simulation results are provided to demonstrate that the proposed joint design achieves significant performance gains over baseline schemes.