Computing Offloading and Resource Allocation of NOMA-Based UAV Emergency Communication in Marine Internet of Things

Unmanned aerial vehicle (UAV) communications have become a prominent technology for emergency communications to enhance network services. This article investigates computing offloading and resource allocation in nonorthogonal multiple access (NOMA)-based UAV emergency communication scenarios. To minimize the computational overhead of the terminal device, a joint task offloading and resource allocation problem is investigated, where the computation overhead of the marine Internet of Things (IoT) device is measured as a weighting of the task completion time and the energy consumption of the device. The optimization of the transmission of IoT devices, the allocation of computing resources to UAVs, task offloading, and carrier allocation are formulated in the considered problem, which is an NP-hard mixed integer nonlinear programming problem. To reduce the complexity, we decompose it into two parts from the property of the problem: 1) the resource optimization problem and 2) the task offloading problem. To solve the resource allocation problem, we first decouple the problem and then use the proposed quasi-convex and convex optimization methods. Meanwhile, a low-complexity task offloading algorithm is designed to achieve a Nash-stable solution by introducing a coalition game approach based on this. Numerical results verify the algorithm’s effectiveness and are compared with other schemes in the literature.

I. Introduction

With the increasing human need for ocean awareness and situational awareness, more and more marine equipment is being deployed in the oceans [1], [2]. The number of marine applications has also increased, for example, marine environment monitoring [3] and maritime navigation [4]. At the same time, emergency rescue operations at sea are also critical. The rapid growth in demand for these applications, as well as the development of the Internet of Things (IoT), leads to the emergence of more and more computing-intensive applications, greatly contributing to the huge demand for wireless communications in the marine environment [5], [6]. For example, real-time voice and video are required for maritime rescue activities. For this reason, computational offloading in marine networks is of great importance for the growing number of marine activities. Multiaccess edge computing (MEC) in marine environments is an effective way to improve the computing efficiency of marine equipment. Based on MEC, marine IoT devices offload tasks to marine edge nodes, which can effectively improve computational efficiency and reduce decision latency in emergency scenarios. At the same time, computing offloading can provide more information to people during the emergency decision-making process, making the final developed solution more in line with human intentions.