Blockchain-Based Privacy Preservation for 5G-Enabled Drone Communications

Wu, Yulei; Dai, Hong-Ning; Wang, Hao; Choo, Kim‐Kwang Raymond

doi:10.1109/mnet.011.2000166

Cited by 74 publications

(33 citation statements)

References 14 publications

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“…In recent years, with the fast development and deployment of 5G and IoT/IIoT [132]- [134], traditional factory and manufacturing environment is carrying out its digital transformation. While such a transformation brings benefits for economics, the security of the industry systems is being challenged [135]- [137]. This is largely due to the weakness and the lack of security considerations of traditional industry…”

Section: Iiot-enabled Smart Factorymentioning

confidence: 99%

Graph Neural Networks for Anomaly Detection in Industrial Internet of Things

Dai

Tang

2022

IEEE Internet Things J.

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114

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The Industrial Internet of Things (IIoT) plays an important role in digital transformation of traditional industries towards Industry 4.0. By connecting sensors, instruments and other industry devices to the Internet, IIoT facilitates the data collection, data analysis, and automated control, thereby improving the productivity and efficiency of the business as well as the resulting economic benefits. Due to the complex IIoT infrastructure, anomaly detection becomes an important tool to ensure the success of IIoT. Due to the nature of IIoT, graph-level anomaly detection has been a promising means to detect and predict anomalies in many different domains such as transportation, energy and factory, as well as for dynamically evolving networks. This paper provides a useful investigation on graph neural networks (GNN) for anomaly detection in IIoTenabled smart transportation, smart energy and smart factory. In addition to the GNN-empowered anomaly detection solutions on point, contextual, and collective types of anomalies, useful datasets, challenges and open issues for each type of anomalies in the three identified industry sectors (i.e., smart transportation, smart energy and smart factory) are also provided and discussed, which will be useful for future research in this area. To demonstrate the use of GNN in concrete scenarios, we show three case studies in smart transportation, smart energy, and smart factory, respectively.

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Section: Iiot-enabled Smart Factorymentioning

confidence: 99%

Graph Neural Networks for Anomaly Detection in Industrial Internet of Things

Dai

Tang

2022

IEEE Internet Things J.

Self Cite

114

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“…Consequently, the blockchain can ensure a certain security of IoMT. The employment of the authentication [42] , homomorphic obfuscations, and group signature [43] to blockchains can further protect the data privacy of IoMT. Moreover, the overlaid P2P networks in blockchain systems can connect different sectors in IoMT together so as to improve the interoperability across the entire IoMT.…”

Section: Blockchain-enabled Iomtmentioning

confidence: 99%

Is blockchain for Internet of Medical Things a panacea for COVID-19 pandemic?

Tao

Dai³

et al. 2021

Pervasive and Mobile Computing

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The outbreak of the COVID-19 pandemic has deeply influenced the lifestyle of the general public and the healthcare system of the society. As a promising approach to address the emerging challenges caused by the epidemic of infectious diseases like COVID-19, Internet of Medical Things (IoMT) deployed in hospitals, clinics, and healthcare centers can save the diagnosis time and improve the efficiency of medical resources though privacy and security concerns of IoMT stall the wide adoption. In order to tackle the privacy, security, and interoperability issues of IoMT, we propose a framework of blockchain-enabled IoMT by introducing blockchain to incumbent IoMT systems. In this paper, we review the benefits of this architecture and illustrate the opportunities brought by blockchain-enabled IoMT. We also provide use cases of blockchain-enabled IoMT on fighting against the COVID-19 pandemic, including the prevention of infectious diseases, location sharing and contact tracing, and the supply chain of injectable medicines. We also outline future work in this area.

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“…It presented a fog computing architecture that included homogeneous physical resources, a fog abstraction layer, and a fog service orchestration layer. Heterogeneous physical resources include segments, instance, servers, set-up boxes, and end-gadgets with various capacity and memory abilities to help extra functionalities [15].…”

Section: B Fog Computingmentioning

confidence: 99%

Internet of Things-Based Energy Efficiency Optimization Model in Fog Smart Cities

Shafik

Matinkhah

Sanda

et al. 2021

JOIV : Int. J. Inform. Visualization

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In recent years, the IoT) Internet of Things (IoT) allows devices to connect to the Internet that has become a promising research area mainly due to the constant emerging of the dynamic improvement of technologies and their associated challenges. In an approach to solve these challenges, fog computing came to play since it closely manages IoT connectivity. Fog-Enabled Smart Cities (IoT-ESC) portrays equitable energy consumption of a 7% reduction from 18.2% renewable energy contribution, which extends resource computation as a great advantage. The initialization of IoT-Enabled Smart Grids including (FESC) like fog nodes in fog computing, reduced workload in Terminal Nodes services (TNs) that are the sensors and actuators of the Internet of Things (IoT) set up. This paper proposes an integrated energy-efficiency model computation about the response time and delays service minimization delay in FESC. The FESC gives an impression of an auspicious computing model for location, time, and delay-sensitive applications supporting vertically -isolated, service delay, sensitive solicitations by providing abundant, ascendable, and scattered figuring stowage and system associativity. We first reviewed the persisting challenges in the proposed state-of-the models and based on them. We introduce a new model to address mainly energy efficiency about response time and the service delays in IoT-ESC. The iFogsim simulated results demonstrated that the proposed model minimized service delay and reduced energy consumption during computation. We employed IoT-ESC to decide autonomously or semi-autonomously whether the computation is to be made on Fog nodes or its transfer to the cloud.

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Blockchain-Based Privacy Preservation for 5G-Enabled Drone Communications

Cited by 74 publications

References 14 publications

Graph Neural Networks for Anomaly Detection in Industrial Internet of Things

Graph Neural Networks for Anomaly Detection in Industrial Internet of Things

Is blockchain for Internet of Medical Things a panacea for COVID-19 pandemic?

Internet of Things-Based Energy Efficiency Optimization Model in Fog Smart Cities

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