Metaverse beyond the hype: Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy

Communication technologies are developing very rapidly and achieving many breakthrough results. The advent of 5th generation mobile communication networks, the so-called 5G, has become one of the most exciting and challenging topics in the wireless study area. The power of 5G enables it to connect to hundreds of billions of devices with extreme-high throughput and extreme-low latency. The 5G realizing a true digital society where everything can be connected via the Internet, well known as the Internet of Things (IoT). IoT is a technology of technologies where humans, devices, software, solutions, and platforms can connect based on the Internet. The formation of IoT technology leads to the birth of a series of applications and solutions serving humanity, such as smart cities, smart agriculture, smart retail, intelligent transportation systems, and IoT ecosystems. Although IoT is considered a revolution in the evolution of the Internet, it still faces a series of challenges such as saving energy, security, performance, and QoS support. In this study, we provide a vision of the Internet of Things that will be the main force driving the comprehensive digital revolution in the future. The communication technologies in the IoT system are discussed comprehensively and in detail. Furthermore, we also indicated indepth challenges of existing common communication technologies in IoT systems and future research directions of IoT. We hope the results of this work can provide a vital guide for future studies on communication technologies for IoT in 5G.

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Wireless Communication Technologies for IoT in 5G: Vision, Applications, and Challenges

The role of artificial intelligence and machine learning in wireless networks security: principle, practice and challenges

The ubiquitous Internet of Things (IoTs) devices spawn growing mobile services of applications with computationally-intensive and latency-sensitive features, which increases the data traffic sharply. Driven by container technology, microservice is emerged with flexibility and scalability by decomposing one service into several independent lightweight parts. To improve the quality of service (QoS) and alleviate the burden of the core network, caching microservices at the edge of networks empowered by the mobile edge computing (MEC) paradigm is envisioned as a promising approach. However, considering the stochastic retrieval requests of IoT devices and time-varying network topology, it brings challenges for IoT devices to decide the caching node selection and microservice replacement independently without complete information of dynamic environments. In light of this, a MEC-enabled di stributed cooperative m icroservice ca ching scheme, named DIMA, is proposed in this paper. Specifically, the microservice caching problem is modeled as a Markov decision process (MDP) to optimize the fetching delay and hit ratio. Moreover, a distributed double dueling deep Q-network (D3QN) based algorithm is proposed, by integrating double DQN and dueling DQN, to solve the formulated MDP, where each IoT device performs actions independently in a decentralized mode. Finally, extensive experimental results are demonstrated that the DIMA is well-performed and more effective than existing baseline schemes.

DIMA: Distributed cooperative microservice caching for internet of things in edge computing by deep reinforcement learning

https://doi.org/10.1016/j.jksuci.2022.08.003

A deeper look into cybersecurity issues in the wake of Covid-19: A survey

In recent times, there have been attempts to leverage artificial intelligence (AI) techniques in a broad range of cyber security applications. Therefore, this paper surveys the existing literature (comprising 54 papers mainly published between 2016 and 2020) on the applications of AI in user access authentication, network situation awareness, dangerous behavior monitoring, and abnormal traffic identification. This paper also identifies a number of limitations and challenges, and based on the findings, a conceptual human-in-the-loop intelligence cyber security model is presented.

Artificial intelligence in cyber security: research advances, challenges, and opportunities

Dataflow Management in the Internet of Things: Sensing, Control, and Security

ZigBee is one of the communication protocols used in the Internet of Things (IoT) applications. In typical deployment scenarios involving low-cost and low-power IoT devices, many communication features are disabled, consequently affecting the security offered by ZigBee. The ZigBee specification assumes that deployment of frame counters is sufficient to mitigate replay attacks in secure ZigBee networks. However, we demonstrate that it is insufficient in this paper (i.e., the network is no longer secure after the coordinator restarts). As a countermeasure, we present a timestamp-based scheme to mitigate replay attacks. Our mitigation strategy does not consume power significantly, and fully powered devices will be responsible for providing power-constrained devices with the current timestamp. The proposed scheme is designed for all ZigBee topologies and different states of ZigBee End Devices (ZEDs). Findings from our evaluation show that the proposed scheme can successfully mitigate replay attacks, with no significant network performance degradation even assuming a worst-case scenario (i.e., many devices are sending data simultaneously).

Timestamp Scheme to Mitigate Replay Attacks in Secure ZigBee Networks

In Human activity recognition (HAR), a large amount of data may have no labels, so it is necessary to realize effective and credible data calibration under Few-shot learning (FSL). This paper proposes a new method for calibrating unlabeled data by using two models to improve the credibility of data labels further. Markov logic network (MLN) is used as the basic model. On the one hand, the construction of knowledge can reduce the dependence on the amount of data. On the other hand, the relationship between the actions can be effectively expressed. Specifically, we pre-train two MLN models using two unmatched training datasets. Then, these models are used to infer the possible labels of unlabeled data simultaneously. When labels are inconsistent, the most likely label will be selected and used to retrain the models. In order to ensure the credibility of the selected label, the selection process adopts a dual-model cross-validation method. This method uses the least square method to determine models’ weights according to the test results. Finally, it gives the possible probability of the labels under the common prediction of the models. Experimental results showed that the dual-model design method is better than the single model method in terms of time efficiency and better than the single model, TextCNN, TextLSTM, Transformer, and other models in terms of label credibility. 

Jiabo Xu

Papers

Artificial intelligence in cyber security: research advances, challenges, and opportunities

Dataflow Management in the Internet of Things: Sensing, Control, and Security

Timestamp Scheme to Mitigate Replay Attacks in Secure ZigBee Networks

Few-shot activity learning by dual Markov logic networks

CDCAT: A multi-language cross-document entity and event coreference annotation tool