Edge computing provides a promising paradigm to support the implementation of industrial Internet of Things (IIoT) by offloading computational-intensive tasks from resource-limited machine-type devices (MTDs) to powerful edge servers. However, the performance gain of edge computing may be severely compromised due to limited spectrum resources, capacity-constrained batteries, and context unawareness. In this paper, we consider the optimization of channel selection which is critical for efficient and reliable task delivery. We aim at maximizing the long-term throughput subject to long-term constraints of energy budget and service reliability. We propose a learning-based channel selection framework with service reliability awareness, energy awareness, backlog awareness, and conflict awareness, by leveraging the combined power of machine learning, Lyapunov optimization, and matching theory. We provide rigorous theoretical analysis, and prove that the proposed framework can achieve guaranteed performance with a bounded deviation from the optimal performance with global state information (GSI) based on only local and causal information. Finally, simulations are conducted under both single-MTD and multi-MTD scenarios to verify the effectiveness and reliability of the proposed framework.

Machine-to-machine (M2M) communication with autonomous data acquisition and exchange plays a key role in realizing the “control”-oriented tactile Internet (TI) applications such as industrial automation. In this paper, we develop a twostage access control and resource allocation algorithm. In the first stage, we propose a contract-based incentive mechanism to motivate some delay-tolerant machine-type communication (MTC) devices to postpone their access demands in exchange for higher access opportunities. In the second stage, a long-term cross-layer online resource allocation approach is proposed based on Lyapunov optimization, which jointly optimizes rate control, power allocation, and channel selection without prior knowledge of channel states. Particularly, the joint power allocation and channel selection problem is formulated as a two-dimensional matching problem, and solved by a pricing-based stable matching approach. Finally, the performance of the proposed algorithm is verified under various simulation scenarios.

Space-air-ground-integrated power Internet of Things (SAG-PIoT) can provide ubiquitous communication and computing services for PIoT devices deployed in remote areas. In SAG-PIoT, the tasks can be either processed locally by PIoT devices, offloaded to edge servers through unmanned aerial vehicles (UAVs), or offloaded to cloud servers through satellites. However, the joint optimization of task offloading and computational resource allocation faces several challenges, such as incomplete information, dimensionality curse, and coupling between long-term constraints of queuing delay and short-term decision making. In this article, we propose a learning-based queue-aware task offloading and resource allocation algorithm (QUARTER). Specifically, the joint optimization problem is decomposed into three deterministic subproblems: 1) device-side task splitting and resource allocation; 2) task offloading; and 3) server-side resource allocation. The first subproblem is solved by the Lagrange dual decomposition. For the second subproblem, we propose a queue-aware actor-critic-based task offloading algorithm to cope with dimensionality curse. A greedy-based low-complexity algorithm is developed to solve the third subproblem. Compared with existing algorithms, simulation results demonstrate that QUARTER has superior performances in energy consumption, queuing delay, and convergence.

Vehicular fog computing (VFC) has emerged as a cost-efficient solution for task processing in vehicular networks. However, how to realize effective server recruitment and reliable task offloading under information asymmetry and uncertainty remains a critical challenge. In this paper, we adopt a two-stage task offloading framework to address this challenge. Firstly, we propose a convex-concave procedure (CCP)-based contract optimization algorithm for server recruitment, which aims to maximize the expected utility of the operator with asymmetric information. Then, a low-complexity and stable task offloading mechanism is proposed to minimize the total network delay based on the pricing-based matching. Furthermore, we extend the work to the scenario of information uncertainty, and develop a matching-learning-based task offloading mechanism, which takes both occurrence awareness and conflict awareness into consideration. Simulation results demonstrate that the proposed algorithm can effectively motivate resource sharing and guarantee bounded deviation from the optimal performance without the global information.

Service caching refers to caching the necessary programs or/and the related databases for performing computational tasks at edge servers, which has been considered to save both computation and communication resources in mobile edge computing (MEC) systems. In this paper, we investigate computation service caching in a multi-unmanned aerial vehicle (UAV) enabled MEC system, where each UAV equipped with an edge server acts as an aerial computing platform to serve the ground devices. Furthermore, the UAVs can serve the devices cooperatively through the provided computing and caching resources. Aiming at minimizing the maximum task completion latency among all devices, we formulate a joint service caching, task offloading, communication and computation resource allocation, as well as UAV placement optimization problem, while guaranteeing the energy budget of all devices and UAVs. The problem is a mixed integer non-linear programming problem, and we decompose it into four sub-problems, and then propose an iterative algorithm based on block coordinate descent (BCD) and successive convex approximation (SCA) techniques to obtain near-optimal solution. Numerical results show that our proposed algorithm can achieve lower task completion latency than other baselines while guaranteeing better fairness among all devices.

Mobile edge computing (MEC) has been regarded as a promising technology to improve the quality of service (QoS) of users by offloading their computation-intensive and delay-sensitive tasks to the wireless network edge. Furthermore, applying non-orthogonal multiple access (NOMA) technology in heterogeneous networks (HetNets) has become a trend to improve system throughput and spectrum efficiency. Exploiting these benefits, we investigate the joint task offloading and resource allocation problem for MEC in NOMA-based HetNets. To minimize the energy consumption of all users, we jointly consider task offloading decision, local CPU frequency scheduling, power control, computation resource and subchannel resource allocation. The optimization problem is challenging due to the combinatorial nature of the offloading decision and its strong coupling with the resource allocation. We thus decouple the problem into two sub-problems of offloading decision and resource allocation, and propose an efficient approach to find the joint solution by solving these two sub-problems iteratively. Simulation results show that the proposed approach can achieve a near optimal performance, and outperform other benchmark approaches in terms of saving energy consumption of all users with an acceptable complexity.