Joint Task Offloading and Resource Allocation for Cooperative Mobile-Edge Computing Under Sequential Task Dependency

The emergence of mobile-edge computing (MEC) makes it possible to run intelligent applications on Internet of Things (IoT) devices. However, due to blockage or deep fading, one IoT device may not have direct link with the edge server. In this case, many surrounding wireless devices can serve as a cooperative node. In this article, we study a cooperative MEC system running sequential task, which is composed of a series of subtasks and can support many intelligent applications. To minimize the energy consumption of the IoT device and cooperative node, a task offloading policy together with the allocation of communication and computation resources is designed jointly. The cases when the cooperative node has no/has private task to complete are investigated, which are denoted as cases I and II, respectively. Although both cases involve the optimization of integer variables, their optimal solutions are achieved. For the first case, the associated problem is simplified equivalently and then decomposed into two levels, with the upper level dealing with integer variables and the lower level handling continuous variables. Bisection search is employed to reach optimality in the lower level and the searching space is compressed in the upper level. For the second case, the associated problem is subdivided into three subproblems. To solve every subproblem optimally, a similar operation like case I is followed, with a semiclosed form solution derived in the lower level. Numerical results verify the effectiveness of our proposed methods compared with benchmark methods and our effort on reducing computation complexity.

I. Introduction

The Internet of Things (IoT) enables many widely distributed devices to sense their surrounding states and transmit sensed data back to the central server, which make it especially useful for environmental monitoring, coal mine inspection, etc., [1]. With these advantages, we have witnessed a rapid growth of IoT in recent years [2], [3]. On the other hand, thanks to the emerging deep neural network (DNN) technique in recent years, the application of artificial intelligence comes into reality and becomes popular in many areas nowadays [4]. With the empowering of DNN technique, an IoT network can not only complete its traditional tasks, e.g., environmental monitoring or coal mining inspection, in a better way, but also accomplish many unprecedented jobs, such as intrusion detection or facial detection through video streams, etc. It is worth mentioning that a DNN-based application generally involves heavy computation burden [5]. However, it is challenging for an IoT device with limited computation capability to complete such a heavy task in time. In order to address this issue, mobile-edge computing (MEC) can serve as a promising solution [6], [7]. In an MEC system, an edge server rich in computation capability is deployed in the vicinity of many IoT devices. Every IoT device can offload its computation task to the nearby edge server, which can promise the accomplishment of the computation task with not only low delay but also low energy consumption [8], [9].