As a powerful tool, the vehicular network has been built to connect human communication and transportation around the world for many years to come. However, with the rapid growth of vehicles, the vehicular network becomes heterogeneous, dynamic, and large scaled, which makes it difficult to meet the strict requirements, such as ultralow latency, high reliability, high security, and massive connections of the next-generation (6G) network. Recently, machine learning (ML) has emerged as a powerful artificial intelligence (AI) technique to make both the vehicle and wireless communication highly efficient and adaptable. Naturally, employing ML into vehicular communication and network becomes a hot topic and is being widely studied in both academia and industry, paving the way for the future intelligentization in 6G vehicular networks. In this article, we provide a survey on various ML techniques applied to communication, networking, and security parts in vehicular networks and envision the ways of enabling AI toward a future 6G vehicular network, including the evolution of intelligent radio (IR), network intelligentization, and self-learning with proactive exploration.

With the widespread adoption of the Internet of Things (IoT), the number of connected devices is growing at an exponential rate, which is contributing to ever-increasing, massive data volumes. Real-time analytics on the massive IoT data, referred to as the “real-time IoT analytics” in this paper, is becoming the mainstream with an aim to provide an immediate or non-immediate actionable insights and business intelligence. However, the analytics network of the existing IoT systems does not adequately consider the requirements of the real-time IoT analytics. In fact, most researchers overlooked an appropriate design of the IoT analytics network while focusing much on the sensing and delivery networks of the IoT system. Since much of the IoT analytics network has often been taken as granted, the survey, in this paper, we aim to review the state-of-theart of the analytics network methodologies, which are suitable for real-time IoT analytics. In this vein, we first describe the basics of the real-time IoT analytics, use cases, and software platforms, and then explain the shortcomings of the network methodologies to support them. To address those shortcomings, we then discuss the relevant network methodologies which may support the real-time IoT analytics. Also, we present a number of prospective research problems and future research directions focusing on the network methodologies for the real-time IoT analytics.

Green communications have always been a target for the information industry to alleviate energy overhead and reduce fossil fuel usage. In the current 5G and future 6G eras, there is no doubt that the volume of network infrastructure and the number of connected terminals will keep exponentially increasing, which results in the surging energy cost. It becomes growing important and urgent to drive the development of green communications. However, there is no doubt that 6G will have increasingly stringent and diversified requirements for Quality of Service (QoS), security, flexibility, and intelligence, all of which challenge the improvement of energy efficiency. Moreover, the dynamic energy harvesting process, which will be widely adopted in 6G, further complicates the power control and network management. To address these challenges and reduce human intervention, Artificial Intelligence (AI) has been extensively recognized and acknowledged as the only solution. Academia and industry have conducted extensive research to alleviate energy demand, improve energy efficiency, and manage energy harvesting in various communication scenarios. In this paper, we present main considerations for green communications and survey related research on AI-based green communications. We focus on how AI techniques are adopted to manage networks and improve energy efficiency towards the green era. We analyze how Machine Learning (ML) techniques including state-of-the-art Deep Learning (DL) can cooperate with conventional AI methods and mathematical models to reduce the algorithm complexity and improve the accuracy rate in 6G. Finally, we discuss the existing problems and envision the open research issues of AI models towards green 6G.

The data privacy and confidentiality in Internet of Things (IoT) networks have been one of the most concerned problems due to increasing threats. The commonly utilized IoT chips adopt a fixed authentication and encryption scheme in the link layer even though multiple options are usually supported. As different authentication and encryption operations mean dissimilar protections and various energy consumption, the fixed security strategy neglects the remaining energy, dynamic threats, and diverse service requirements, leading to low energy efficiency. Moreover, fixed high-level security protections consume too much energy even though the security requirement may be low, which results in short working time. To address this problem, we propose an Artificial Intelligence (AI) based adaptive security specification method for 6G IoT networks where the IoT devices are connected to cellular networks via different frequency bands including Terahertz (THz) and millimeter wave (mmWave). And the IoT sensing devices are assumed to support the energy harvesting technique which is expected to be widely adopted in 6G. In our proposal, the Extended Kalman Filtering (EKF) method is firstly adopted to predict future harvesting power. Then, in each energy aware cycle, we design a mathematical model to calculate the required energy of different security strategies and choose the supported highest-level protection which can meet service requirement and avoid energy exhaustion. Simulation results illustrate that the proposal cannot only provide satisfied security protection for different services, but also adjust the security protection to avoid the energy exhaustion, leading to significant improvement of throughput and working time.

In LTE-Advanced networks, besides the overall coverage provided by traditional macrocells, various classes of low-power nodes (e.g., pico eNBs, femto eNBs, and relays) can be distributed throughout the macrocells as a more targeted underlay to further enhance the area's spectral efficiency, alleviate traffic hot zones, and thus improve the end-user experience. Considering the limited backhaul connections within lowpower nodes and the imbalanced traffic distribution among different cells, it is highly possible that some cells are severely congested while adjacent cells are very lightly loaded. Therefore, it is of critical importance to achieve efficient load balancing among multi-tier cells in LTE-Advanced networks. However, available techniques such as smart cell and biasing, although able to alleviate congestion or distribute traffic to some extent, cannot respond or adapt flexibly to the real-time traffic distributions among multi-tier cells. Toward this end, we propose in this article a device-to-device communication-based load balancing algorithm, which utilizes D2D communications as bridges to flexibly offload traffic among different tier cells and achieve efficient load balancing according to their real-time traffic distributions. Besides identifying the research issues that deserve further study, we also present numerical results to show the performance gains that can be achieved by the proposed algorithm.

The end-to-end quality of service (QoS) and quality of experience (QoE) guarantee is quite important for network optimization. The current 5G and conceived 6G network in the future with ultra high density, bandwidth, mobility and large scale brings urgent requirement of high efficient end-to-end optimization methods. The conventional network optimization methods without learning and intelligent decision ability are hard to handle the high complexity and dynamic scenarios of 6G. Recently, machine learning based QoS and QoE aware network optimization algorithms emerge as a hot research area and attract much attention, which is widely acknowledged as the potential solution for end-to-end optimization in 6G. However, there are still many critical issues of employing machine learning in networks, especially in 6G. In this paper, we give a comprehensive survey on the recent machine learning based network optimization methods to guarantee the end-to-end QoS and QoE. To easy to follow, we introduce the investigated works following the end-to-end transmission flow from network access, routing to network congestion control and adaptive steaming control. Then we discuss some open issues and potential future research directions.