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.

Recently, with the rapid development of information and communication technologies, the infrastructures, resources, end devices, and applications in communications and networking systems are becoming much more complex and heterogeneous. In addition, the large volume of data and massive end devices may bring serious security, privacy, services provisioning, and network management challenges. In order to achieve decentralized, secure, intelligent, and efficient network operation and management, the joint consideration of blockchain and machine learning (ML) may bring significant benefits and have attracted great interests from both academia and industry. On one hand, blockchain can significantly facilitate training data and ML model sharing, decentralized intelligence, security, privacy, and trusted decision-making of ML. On the other hand, ML will have significant impacts on the development of blockchain in communications and networking systems, including energy and resource efficiency, scalability, security, privacy, and intelligent smart contracts. However, some essential open issues and challenges that remain to be addressed before the widespread deployment of the integration of blockchain and ML, including resource management, data processing, scalable operation, and security issues. In this paper, we present a survey on the existing works for blockchain and ML technologies. We identify several important aspects of integrating blockchain and ML, including overview, benefits, and applications. Then we discuss some open issues, challenges, and broader perspectives that need to be addressed to jointly consider blockchain and ML for communications and networking systems.

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 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.

Fog computing, complementary to cloud computing, has recently emerged as a promising solution that extends the computing infrastructure from the cloud center to the network edge. By offloading computational applications to the network edge, fog computing could support delay-sensitive applications and reliable access to nearby users. However, with the growing number of demands from various applications, fog computing may be overwhelmed and it may result in significant performance degradation. Thus, to process applications efficiently, we propose an integrated fog and cloud computing (FCC) approach, where users can offload a series of applications to nearby fog nodes (FNs) or cloud center cooperatively. Nevertheless, due to the constrained computing, storage, and radio resources, how to perform resource allocation to achieve an optimal and stable performance is an important problem. To address this issue, we focus on multiple resource allocation problem in a general system, which consists of multi-user, multi-FN, and a cloud center. In addition, to reduce offloading transmission latency and release the constraint of limited radio resource, non-orthogonal multiple access, which enables multiple users to transmit data to the same FN for offloading tasks on the same spectrum resource, is introduced into the proposed FCC approach. To this end, we formulate joint offloading decision, user scheduling, and resource allocation problem as an optimization problem that aims at minimizing the total system cost of energy as well as the delay of users. Furthermore, we decouple the original problem and transform it into a convex problem. Finally, we develop alternating direction method of multipliers based algorithms to solve the optimization problem in a distributed and efficient way. Simulation results show that the proposed approach achieves better performance compared with existing schemes.

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.