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Terrestrial networks form the fundamental infrastructure of modern communication systems, serving more than 4 billion users globally. However, terrestrial networks are facing a wide range of challenges, from coverage and reliability to interference and congestion. As the demands of the 6G era are expected to be much higher, it is crucial to address these challenges to ensure a robust and efficient communication infrastructure for the future. To address these problems, Non-terrestrial Network (NTN) has emerged to be a promising solution. NTNs are communication networks that leverage airborne (e.g., unmanned aerial vehicles) and spaceborne vehicles (e.g., satellites) to facilitate ultra-reliable communications and connectivity with high data rates and low latency over expansive regions. This article aims to provide a comprehensive survey on the utilization of network slicing, Artificial Intelligence/Machine Learning (AI/ML), and Open Radio Access Network (ORAN) to address diverse challenges of NTNs from the perspectives of both academia and industry. Particularly, we first provide an in-depth tutorial on NTN and the key enabling technologies including network slicing, AI/ML, and ORAN. Then, we provide a comprehensive survey on how network slicing and AI/ML have been leveraged to overcome the challenges that NTNs are facing. Moreover, we present how ORAN can be utilized for NTNs. Finally, we highlight important challenges, open issues, and future research directions of NTN in the 6G era.

The digital revolution has substantially changed our lives in which Internet-of-Things (IoT) plays a prominent role. The rapid development of IoT to most corners of life, however, leads to various emerging cybersecurity threats. Therefore, detecting and preventing potential attacks in IoT networks have recently attracted paramount interest from both academia and industry. Among various attack detection approaches, machine learning-based methods, especially deep learning, have demonstrated great potential thanks to their early detecting capability. However, these machine learning techniques only work well when a huge volume of data from IoT devices with label information can be collected. Nevertheless, the labeling process is usually time consuming and expensive, thus, it may not be able to adapt with quick evolving IoT attacks in reality. In this paper, we propose a novel deep transfer learning (DTL) method that allows to learn from data collected from multiple IoT devices in which not all of them are labeled. Specifically, we develop a DTL model based on two AutoEncoders (AEs). The first AE (AE1) is trained on the source datasets (source domains) in the supervised mode using the label information and the second AE (AE2) is trained on the target datasets (target domains) in an unsupervised manner without label information. The transfer learning process attempts to force the latent representation (the bottleneck layer) of AE2 similarly to the latent representation of AE1. After that, the latent representation of AE2 is used to detect attacks in the incoming samples in the target domain. We carry out intensive experiments on nine recent IoT datasets to evaluate the performance of the proposed model. The experimental results demonstrate that the proposed DTL model significantly improves the accuracy in detecting IoT attacks compared to the baseline deep learning technique and two recent DTL approaches.

Social distancing plays a pivotal role in preventing the spread of viral diseases illnesses such as COVID-19. By minimizing the close physical contact among people, we can reduce the chances of catching the virus and spreading it across the community. This two-part paper aims to provide a comprehensive survey on how emerging technologies, e.g., wireless and networking, artificial intelligence (AI) can enable, encourage, and even enforce social distancing practice. In this Part I, we provide a comprehensive background of social distancing including basic concepts, measurements, models, and propose various practical social distancing scenarios. We then discuss enabling wireless technologies which are especially effect- in social distancing, e.g., symptom prediction, detection and monitoring quarantined people, and contact tracing. The companion paper Part II surveys other emerging and related technologies, such as machine learning, computer vision, thermal, ultrasound, etc., and discusses open issues and challenges (e.g., privacy-preserving, scheduling, and incentive mechanisms) in implementing social distancing in practice.

This two-part paper aims to provide a comprehensive survey on how emerging technologies, e.g., wireless and networking, artificial intelligence (AI) can enable, encourage, and even enforce social distancing practice. In Part I, an extensive background of social distancing is provided, and enabling wireless technologies are thoroughly surveyed. In this Part II, emerging technologies such as machine learning, computer vision, thermal, ultrasound, etc., are introduced. These technologies open many new solutions and directions to deal with problems in social distancing, e.g., symptom prediction, detection and monitoring quarantined people, and contact tracing. Finally, we discuss open issues and challenges (e.g., privacy-preserving, scheduling, and incentive mechanisms) in implementing social distancing in practice. As an example, instead of reacting with ad-hoc responses to COVID-19-like pandemics in the future, smart infrastructures (e.g., next-generation wireless systems like 6G, smart home/building, smart city, intelligent transportation systems) should incorporate a pandemic mode in their standard architectures/designs.

The experiment was conducted under glasshouse conditions to evaluate nitrogen use efficiency and drought tolerant ability of the five different sugarcane varieties (including NiF3, Ni9, Ni17, Ni21 and Ni22) under early growth stage from 60 to 120 days after transplanting. The results showed drought stress reduced the photosynthetic rate, growth parameters including plant height, leaf area; partial and total dry weights; and nitrogen use efficiency (NUE) traits including photosynthetic NUE, nitrogen utilization efficiency and biomass NUE of measured sugarcane varieties. The significant differences were found among varieties in growth parameters, dry weights, NUE traits and drought tolerant index (DTI). The significant positive correlations among NUE traits and DTI suggested higher NUEs could support better a tolerant ability to drought stress at the early growth stage. Because of larger contributions, DTIs for aboveground and stalk dry weight could be used as the important DTIs to evaluate drought tolerant ability in sugarcane varieties. Abbreviations Amax: potential photosynthetic rate; DAT: days after transplanting; DTI: drought tolerant index; NL: specific leaf nitrogen content; NUE: nitrogen use efficiency; NUEb: biomass nitrogen use efficiency; NUEt: nitrogen utilization efficiency; PNUE: photosynthetic nitrogen use efficiency; TN: total nitrogen content; TNU: total nitrogen uptake; WW: well-watered; DS: water stress.

The rapid development of blockchain technology and their numerous emerging applications has received huge attention in recent years. The distributed consensus mechanism is the backbone of a blockchain network. It plays a key role in ensuring the network's security, integrity, and performance. Most current blockchain networks have been deploying the proof-of-work consensus mechanisms, in which the consensus is reached through intensive mining processes. However, this mechanism has several limitations, e.g., energy inefficiency, delay, and vulnerable to security threats. To overcome these problems, a new consensus mechanism has been developed recently, namely proof of stake, which enables to achieve the consensus via proving the stake ownership. This mechanism is expected to become a cutting-edge technology for future blockchain networks. This paper is dedicated to investigating proof-of-stake mechanisms, from fundamental knowledge to advanced proof-of-stake-based protocols along with performance analysis, e.g., energy consumption, delay, and security, as well as their promising applications, particularly in the field of Internet of Vehicles. The formation of stake pools and their effects on the network stake distribution are also analyzed and simulated. The results show that the ratio between the block reward and the total network stake has a significant impact on the decentralization of the network. Technical challenges and potential solutions are also discussed.

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.

In addition to being environment friendly, vehicle-to-grid (V2G) systems can help the plug-in electric vehicle (PEV) users in reducing their energy costs and can also help stabilizing energy demand in the power grid. In V2G systems, since the PEV users need to obtain system information (e.g., locations of charging/discharging stations, current load, and supply of the power grid) to achieve the best charging and discharging performance, data communication plays a crucial role. However, since the PEV users are highly mobile, information from V2G systems is not always available for many reasons, e.g., wireless link failures and cyber attacks. Therefore, in this paper, we introduce a novel concept using cyber insurance to “transfer” cyber risks, e.g., unavailable information, of a PEV user to a third party, e.g., a cyber-insurance company. Under the insurance coverage, even without information about V2G systems, a PEV user is always guaranteed the best price for charging/discharging. In particular, we formulate the optimal energy cost problem for the PEV user by adopting a Markov decision process framework. We then propose a learning algorithm to help the PEV user make optimal decisions, e.g., to charge or discharge and to buy or not to buy insurance, in an online fashion. Through simulations, we show that cyber insurance is an efficient solution not only in dealing with cyber risks, but also in maximizing revenue for the PEV user.