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Review

AI-Inspired Non-Terrestrial Networks for IIoT: Review on Enabling Technologies and Applications

1
Department of Electrical and Electronics Engineering, Faculty of Engineering, University of West Attica, Ancient Olive Grove Campus, 250 Thivon & P. Ralli, 12244 Aigaleo, Athens, Greece
2
Department of Digital Systems, School of Information and Communication Technologies, University of Piraeus, 80 Karaoli & Dimitriou St., 18534 Piraeus, Greece
*
Author to whom correspondence should be addressed.
IoT 2020, 1(1), 21-48; https://doi.org/10.3390/iot1010003
Received: 27 June 2020 / Revised: 17 July 2020 / Accepted: 30 July 2020 / Published: 31 July 2020
During the last few years, various Industrial Internet of Things (IIoT) applications have emerged with numerous network elements interconnected using wired and wireless communication technologies and equipped with strategically placed sensors and actuators. This paper justifies why non-terrestrial networks (NTNs) will bring the IIoT vision closer to reality by providing improved data acquisition and massive connectivity to sensor fields in large and remote areas. NTNs are engineered to utilize satellites, airships, and aircrafts, which can be employed to extend the radio coverage and provide remote monitoring and sensing services. Additionally, this paper describes indicative delay-tolerant massive IIoT and delay-sensitive mission-critical IIoT applications spanning a large number of vertical markets with diverse and stringent requirements. As the heterogeneous nature of NTNs and the complex and dynamic communications scenarios lead to uncertainty and a high degree of variability, conventional wireless communication technologies cannot sufficiently support ultra-reliable and low-latency communications (URLLC) and offer ubiquitous and uninterrupted interconnectivity. In this regard, this paper sheds light on the potential role of artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL), in the provision of challenging NTN-based IIoT services and provides a thorough review of the relevant research works. By adding intelligence and facilitating the decision-making and prediction procedures, the NTNs can effectively adapt to their surrounding environment, thus enhancing the performance of various metrics with significantly lower complexity compared to typical optimization methods. View Full-Text
Keywords: deep learning (DL); high-altitude platforms (HAPs); industrial internet of things (IIoT); machine learning (ML); satellite networks; unmanned aerial vehicles (UAVs) deep learning (DL); high-altitude platforms (HAPs); industrial internet of things (IIoT); machine learning (ML); satellite networks; unmanned aerial vehicles (UAVs)
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MDPI and ACS Style

Michailidis, E.T.; Potirakis, S.M.; Kanatas, A.G. AI-Inspired Non-Terrestrial Networks for IIoT: Review on Enabling Technologies and Applications. IoT 2020, 1, 21-48. https://doi.org/10.3390/iot1010003

AMA Style

Michailidis ET, Potirakis SM, Kanatas AG. AI-Inspired Non-Terrestrial Networks for IIoT: Review on Enabling Technologies and Applications. IoT. 2020; 1(1):21-48. https://doi.org/10.3390/iot1010003

Chicago/Turabian Style

Michailidis, Emmanouel T., Stelios M. Potirakis, and Athanasios G. Kanatas. 2020. "AI-Inspired Non-Terrestrial Networks for IIoT: Review on Enabling Technologies and Applications" IoT 1, no. 1: 21-48. https://doi.org/10.3390/iot1010003

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