Nowadays, many disruptive Internet-of-Things (IoT) applications emerge, such as augmented/virtual reality online games, autonomous driving, and smart everything, which are massive in number, data intensive, computation intensive, and delay sensitive. Due to the mismatch between the fifth generation (5G) and the requirements of such massive IoT-enabled applications, there is a need for technological advancements and evolutions for wireless communications and networking toward the sixth-generation (6G) networks. 6G is expected to deliver extended 5G capabilities at a very high level, such as Tbps data rate, sub-ms latency, cm-level localization, and so on, which will play a significant role in supporting massive IoT devices to operate seamlessly with highly diverse service requirements. Motivated by the aforementioned facts, in this article, we present a comprehensive survey on 6G-enabled massive IoT. First, we present the drivers and requirements by summarizing the emerging IoT-enabled applications and the corresponding requirements, along with the limitations of 5G. Second, visions of 6G are provided in terms of core technical requirements, use cases, and trends. Third, a new network architecture provided by 6G to enable massive IoT is introduced, i.e., space-air-ground-underwater/sea networks enhanced by edge computing. Fourth, some breakthrough technologies, such as machine learning and blockchain, in 6G are introduced, where the motivations, applications, and open issues of these technologies for massive IoT are summarized. Finally, a use case of fully autonomous driving is presented to show 6G supports massive IoT.

To tackle the contradiction between the computation intensive applications and the resource-hungry mobile user equipments (UEs), mobile edge computing (MEC) has been provisioned as a promising solution, which enables the UEs to offload the tasks to the MEC servers. Considering the characteristics of small cell networks (SCNs), integrating MEC into SCNs is natural. But in terms of the high interference, multi-access property, and limited resources of small cell base stations (SBSs), an efficient computation offloading scheme is essential. However, there still lack comprehensive studies on this problem in the densely deployed SCNs. In this paper, we study the energy-efficient computation offloading management scheme in the MEC system with SCNs. The aim of this paper is to minimize the energy consumption of all UEs via jointly optimizing computation offloading decision making, spectrum, power, and computation resource allocation. Specially, the UEs need not only to decide whether to offload but also to determine where to offload. First, we present the computation offloading model and formulate this problem as a mix integer non-linear programming problem, which is NP-hard. Taking advantages of genetic algorithm (GA) and particle swarm optimization (PSO), we design a suboptimal algorithm named as hierarchical GA and PSO-based computation algorithm to solve this problem. Finally, the convergence of this algorithm is studied by simulation, and the performance of the proposed algorithm is verified by comparing with the other baseline algorithms.

Mobile edge computing (MEC) has been proposed in recent years to offload computation tasks from user equipments (UEs) to the network edge to break hardware limitations and resource constraints at UEs. Although there have been some existing works on computation offloading in 5G, most of them fail to take into account of the unique property of 5G in their scheme design. In this work, we consider small-cell network architecture for task offloading. In order to achieve energy efficiency, we model the energy consumption of offloading from both task computation and communication aspects. Besides, transmission scheduling are carried over both the fronthaul and backhual links. We first formulate an energy optimization problem of offloading, which aims to minimize the overall energy consumption at all system entities and takes into account of the constraints from both computation capabilities and service delay requirement. We then develop an artificial fish swarm algorithm (AFSA) based scheme to solve the energy optimization problem. Besides, the global convergence property of the our scheme is formally proved. Finally, various simulation results demonstrate the efficiency of our scheme.

In this paper, we present a blockchain-based mobile edge computing (B-MEC) framework for adaptive resource allocation and computation offloading in future wireless networks, where the blockchain works as an overlaid system to provide management and control functions. In this framework, how to reach a consensus between the nodes while simultaneously guaranteeing the performance of both MEC and blockchain systems is a major challenge. Meanwhile, resource allocation, block size, and the number of consecutive blocks produced by each producer are critical to the performance of B-MEC. Therefore, an adaptive resource allocation and block generation scheme is proposed. To improve the throughput of the overlaid blockchain system and the quality of services (QoS) of the users in the underlaid MEC system, spectrum allocation, size of the blocks, and number of producing blocks for each producer are formulated as a joint optimization problem, where the time-varying wireless links and computation capacity of the MEC servers are considered. Since this problem is intractable using traditional methods, we resort to the deep reinforcement learning approach. Simulation results show the effectiveness of the proposed approach by comparing with other baseline methods.

Mobile edge computing is conceived as an appealing technology to enhance cloud computing capability of mobile devices (MDs) at the edge of the networks. Although some researchers use the technology to address the intensive tasks' high computation needs of MDs in small-cell networks (SCNs), most of them ignore considering the interests interaction between small cells and MDs. In this paper, we study a distributed computation offloading strategy for a multi-device and multi-server system based on orthogonal frequency-division multiple access in SCNs. First, to satisfy the interest requirements of different MDs and analyze the interactions among multiple small cells, we formulate a distributed overhead minimization problem, aiming at jointly optimizing energy consumption and latency of each MD. Second, to ensure the individuals of different MDs, we formulate the proposed overhead minimization problem as a strategy game. Then, we prove the strategy game is a potential game by the feat of potential game theory. Moreover, the potential game-based offloading algorithm is proposed to reach a Nash equilibrium. In addition, to guarantee the performance of the designed algorithm, we consider the lower bound of iteration times to derive the worst case performance guarantee. Finally, the simulation results corroborate that the proposed algorithm can effectively minimize the overhead of each MD compared with different other existing algorithms.

Wireless virtual reality (VR) is predicted to become a killer application in 5G and beyond, which provides an immersive experience and revolutionizes the way people communicate. It is well-known that rendering is the key performance bottleneck in wireless VR systems, especially for VR games. However, real-time rendering and data correlation are ignored by most researchers. In this paper, we propose an adaptive VR framework that enables high-quality wireless VR in future mmWave-enabled wireless networks with mobile edge computing (MEC), where real-time VR rendering tasks can be offloaded to MEC servers adaptively and the caching capability of MEC servers enables further performance improvement. First, we formulate the addressed problem to maximize the quality of experience (QoE) of the users, where association, adaptive offloading mode selection, and caching policy are jointly optimized. Considering the high complexity of the addressed problem, we then propose a distributed learning approach consisting of an offline training phase and an online running phase, which maintains scalability and adaptation capability. The offline phase is based on deep reinforcement learning (DRL) while the latter utilizes game theory. At last, simulation results show the superiority of the proposed algorithm over the other baseline algorithms in terms of QoE utility values, latency, and convergence time.