A Cluster-Based Optimal Computation Offloading Decision Mechanism Using RL in the IIoT Field

In the Industrial Internet of Things (IIoT), various tasks are created dynamically because of the small quantity batch production. Hence, it is difficult to execute tasks only with devices that have limited battery lives and computation capabilities. To solve this problem, we adopted the mobile edge computing (MEC) paradigm. However, if there are numerous tasks to be processed on the MEC server (MECS), it may not be suitable to deal with all tasks in the server within a delay constraint owing to the limited computational capability and high network overhead. Therefore, among cooperative computing techniques, we focus on task offloading to nearby devices using device-to-device (D2D) communication. Consequently, we propose a method that determines the optimal offloading strategy in an MEC environment with D2D communication. We aim to minimize the energy consumption of the devices and task execution delay under certain delay constraints. To solve this problem, we adopt a Q-learning algorithm that is part of reinforcement learning (RL). However, if one learning agent determines whether to offload tasks from all devices, the computing complexity of that agent increases tremendously. Thus, we cluster the nearby devices that comprise the job shop, where each cluster’s head determines the optimal offloading strategy for the tasks that occur within its cluster. Simulation results show that the proposed algorithm outperforms the compared methods in terms of device energy consumption, task completion rate, task blocking rate, and throughput.

Robust Edge Computing in UAV Systems via Scalable Computing and Cooperative Computing

<div>Unmanned aerial vehicle (UAV) systems are of increasing interest to academia and industry due to their mobility, flexibility and maneuverability, and are an effective alternative to various uses such as surveillance and mobile edge computing (MEC). However, due to their limited computational and communications resources, it is difficult to serve all computation tasks simultaneously. This article tackles this problem by first proposing a scalable aerial computing solution, which is applicable for computation tasks of multiple quality levels, corresponding to different computation workloads and computation results of distinct performances. It opens up the possibility to maximally improve the overall computing performance with limited computational and communications resources. To meet the demands for timely video analysis that exceed the computing power of a UAV, we propose an aerial video streaming enabled cooperative computing solution namely, UAVideo, which streams videos from a UAV to ground servers. As a complement to scalable aerial computing, UAVideo minimizes the video streaming time under the constraints on UAV trajectory, video features, and communications resources. Simulation results reveal the substantial advantages of the proposed solutions. Besides, we highlight relevant directions for future research.</div>

Robust Edge Computing in UAV Systems via Scalable Computing and Cooperative Computing

<div>Unmanned aerial vehicle (UAV) systems are of increasing interest to academia and industry due to their mobility, flexibility and maneuverability, and are an effective alternative to various uses such as surveillance and mobile edge computing (MEC). However, due to their limited computational and communications resources, it is difficult to serve all computation tasks simultaneously. This article tackles this problem by first proposing a scalable aerial computing solution, which is applicable for computation tasks of multiple quality levels, corresponding to different computation workloads and computation results of distinct performances. It opens up the possibility to maximally improve the overall computing performance with limited computational and communications resources. To meet the demands for timely video analysis that exceed the computing power of a UAV, we propose an aerial video streaming enabled cooperative computing solution namely, UAVideo, which streams videos from a UAV to ground servers. As a complement to scalable aerial computing, UAVideo minimizes the video streaming time under the constraints on UAV trajectory, video features, and communications resources. Simulation results reveal the substantial advantages of the proposed solutions. Besides, we highlight relevant directions for future research.</div>

Computation offloading to edge cloud and dynamically resource-sharing collaborators in Internet of Things

With the diversity of the communication technology and the heterogeneity of the computation resources at network edge, both the edge cloud and peer devices (collaborators) can be scavenged to provide computation resources for the resource-limited Internet-of-Things (IoT) devices. In this paper, a novel cooperative computing paradigm is proposed, in which the computation resources of IoT device, opportunistically idle collaborators and dedicated edge cloud are fully exploited. Computation/offloading assistance is provided by collaborators at idle/busy states, respectively. Considering the channel randomness and opportunistic computation resource share of collaborators, we study the stochastic offloading control for an IoT device, regarding how much computation load is processed locally, offloaded to the edge cloud and a collaborator. The problem is formulated into a finite horizon Markov decision problem with the objective of minimizing the expected total energy consumption of the IoT device and the collaborator, subject to satisfying the hard computation deadline constraint. Optimal offloading policy is derived based on the stochastic optimization theory, which demonstrates that the energy consumption can be reduced by a proportional factor through the cooperative computing. More energy saving is achieved with better wireless channel condition or higher computation energy efficiency of collaborators. Simulation results validate the optimality of the proposed policy and the efficiency of the cooperative computing between end devices and edge cloud, compared to several other offloading schemes.