The Transmission Control Protocol.

The fifth generation (5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability, and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation (6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence (AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears.

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Towards 6G wireless communication networks: vision, enabling technologies, and new paradigm shifts

The past decade has witnessed the rapid evolution in blockchain technologies, which has attracted tremendous interests from both the research communities and industries. The blockchain network was originated from the Internet financial sector as a decentralized, immutable ledger system for transactional data ordering. Nowadays, it is envisioned as a powerful backbone/framework for decentralized data processing and data-driven self-organization in flat, open-access networks. In particular, the plausible characteristics of decentralization, immutability, and self-organization are primarily owing to the unique decentralized consensus mechanisms introduced by blockchain networks. This survey is motivated by the lack of a comprehensive literature review on the development of decentralized consensus mechanisms in blockchain networks. In this paper, we provide a systematic vision of the organization of blockchain networks. By emphasizing the unique characteristics of decentralized consensus in blockchain networks, our in-depth review of the state-of-the-art consensus protocols is focused on both the perspective of distributed consensus system design and the perspective of incentive mechanism design. From a game-theoretic point of view, we also provide a thorough review of the strategy adopted for self-organization by the individual nodes in the blockchain backbone networks. Consequently, we provide a comprehensive survey of the emerging applications of blockchain networks in a broad area of telecommunication. We highlight our special interest in how the consensus mechanisms impact these applications. Finally, we discuss several open issues in the protocol design for blockchain consensus and the related potential research directions.

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A Survey on Consensus Mechanisms and Mining Strategy Management in Blockchain Networks

Space-air-ground integrated network (SAGIN), as an integration of satellite systems, aerial networks, and terrestrial communications, has been becoming an emerging architecture and attracted intensive research interest during the past years. Besides bringing significant benefits for various practical services and applications, SAGIN is also facing many unprecedented challenges due to its specific characteristics, such as heterogeneity, self-organization, and time-variability. Compared to traditional ground or satellite networks, SAGIN is affected by the limited and unbalanced network resources in all three network segments, so that it is difficult to obtain the best performances for traffic delivery. Therefore, the system integration, protocol optimization, resource management, and allocation in SAGIN is of great significance. To the best of our knowledge, we are the first to present the state-of-the-art of the SAGIN since existing survey papers focused on either only one single network segment in space or air, or the integration of space-ground, neglecting the integration of all the three network segments. In light of this, we present in this paper a comprehensive review of recent research works concerning SAGIN from network design and resource allocation to performance analysis and optimization. After discussing several existing network architectures, we also point out some technology challenges and future directions.

Space-Air-Ground Integrated Network: A Survey

Internet of Things (IoT) computing offloading is a challenging issue, especially in remote areas where common edge/cloud infrastructure is unavailable. In this paper, we present a space-air-ground integrated network (SAGIN) edge/cloud computing architecture for offloading the computation-intensive applications considering remote energy and computation constraints, where flying unmanned aerial vehicles (UAVs) provide near-user edge computing and satellites provide access to the cloud computing. First, for UAV edge servers, we propose a joint resource allocation and task scheduling approach to efficiently allocate the computing resources to virtual machines (VMs) and schedule the offloaded tasks. Second, we investigate the computing offloading problem in SAGIN and propose a learning-based approach to learn the optimal offloading policy from the dynamic SAGIN environments. Specifically, we formulate the offloading decision making as a Markov decision process where the system state considers the network dynamics. To cope with the system dynamics and complexity, we propose a deep reinforcement learning-based computing offloading approach to learn the optimal offloading policy on-the-fly, where we adopt the policy gradient method to handle the large action space and actor-critic method to accelerate the learning process. Simulation results show that the proposed edge VM allocation and task scheduling approach can achieve near-optimal performance with very low complexity and the proposed learning-based computing offloading algorithm not only converges fast but also achieves a lower total cost compared with other offloading approaches.

Space/Aerial-Assisted Computing Offloading for IoT Applications: A Learning-Based Approach

Edge computing has great potential to address the challenges in mobile vehicular networks by transferring partial storage and computing functions to network edges. However, it is still a challenge to efficiently utilize heterogeneous edge computing architectures and deploy large-scale IoV systems. In this article, we focus on the collaborations among different edge computing anchors and propose a novel collaborative vehicular edge computing framework, called CVEC. Specifically, CVEC can support more scalable vehicular services and applications by both horizontal and vertical collaborations. Furthermore, we discuss the architecture, principle, mechanisms, special cases, and potential technical enablers to support the CVEC. Finally, we present some research challenges as well as future research directions.

Enabling Collaborative Edge Computing for Software Defined Vehicular Networks

Vehicular networking is promising to improve traffic efficiency and driving safety, as well as travel experience. However, the traditional network employs a highly coupled design, which is quite limited in its ability to satisfy various challenging vehicular demands. Recently, new studies focus on how to design software defined vehicular networks smartly to meet various vehicular demands. In this article, we investigate a new smart identifier networking (SINET) paradigm and propose a SINET customized solution enabling crowd collaborations for software defined vehicular networks (SINET-V). In particular, through crowd sensing, network function slices are well organized with a group of function-similar components. Different function slices are further driven to serve various applications by using crowd collaborations. We clearly illustrate how SINET-V works and also analyze its potential advantages in several special vehicular instances. Experimental results show that SINET-V has great potential to promote powerful vehicular networks.

Enhancing Crowd Collaborations for Software Defined Vehicular Networks

In Named Data Networking (NDN), packet forwarding decisions rely upon lookup operations on variable-length hierarchical names instead of fixed-length host addresses. This pivotal feature introduces new challenges in the deployment of NDN at the Internet scale. In this letter, a novel Name Lookup engine with Adaptive Prefix Bloom filter (NLAPB) is conceived, in which each NDN name/prefix is split into B-prefix followed by T-suffix. B-prefix is matched by Bloom filters whereas T-suffix is processed by the small-scale trie. The length of B-prefixes (and T-suffixes) is dynamically throttled based on their popularity in order to accelerate the lookup. Experimental results show that: (i) NLAPB is able to lower the false positive rate with respect to a lookup entirely based on Bloom filters; (ii) it decreases the memory requirements with respect to a trie-based approach; (iii) it reduces processing time with respect to both them.

Scalable Name Lookup with Adaptive Prefix Bloom Filter for Named Data Networking

The current Internet has many inherent shortcomings, such as poor security, low mobility, and high energy consumption, which all seriously hinder its further development. This article first re-examines the original design of the Internet, and points out the so-called triple bindings, namely resource/location binding, user/network binding, and control/data binding, which are the root causes of the current Internet’s problems. A collaborative Internet architecture named SINET is proposed by removing the restrictions from the triple bindings completely. SINET contains three layers vertically and two domains horizontally. At each layer, there are two domains associated with a pair of xID and xBD. Multiple xAMs among these xIDs and xBDs are established to collaboratively adapt to various services requirements. Experimental tests have verified that SINET improves the performance in many ways and thus has great potential to flexibly satisfy emerging demands.

Wei Quan

Papers

Space/Aerial-Assisted Computing Offloading for IoT Applications: A Learning-Based Approach

Enabling Collaborative Edge Computing for Software Defined Vehicular Networks

Enhancing Crowd Collaborations for Software Defined Vehicular Networks

Scalable Name Lookup with Adaptive Prefix Bloom Filter for Named Data Networking

Smart identifier network: A collaborative architecture for the future internet