Recent Advances in Artificial Intelligence for Wireless Internet of Things and Cyber-Physical Systems: A Comprehensive Survey

• Published in: IEEE internet of things journal 2022-08, Vol.9 (15), p.12916-12930
• Main Authors: Salau, Babajide A., Rawal, Atul, Rawat, Danda B.
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial intelligence; Artificial intelligence (AI); Communication system security; Cyber-physical systems; cyber–physical systems (CPSs); Deep learning; distributed learning; Electronic devices; federated learning; Internet of Things; Internet of Things (IoT); Machine learning; machine learning (ML); Military applications; Military communications; Military engineering; Network latency; transfer learning (TL); Wireless communication; Wireless networks; Wireless sensor networks
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2022.3170449

Advances in artificial intelligence (AI) and wireless technology are driving forward the large deployment of interconnected smart technologies that constitute cyber-physical systems (CPSs) and Internet of Things (IoT) for many commercial and military applications. CPS is characterized by communication, computing, and control engineering based on a large volume of data originating from various devices, plants, sensors, etc. Wireless technologies have enabled the ease of networking and communications for both CPS and IoT, by providing massive and critical connectivity and control mechanisms. However, they are prone to challenges, such as low latency, throughput, and scheduling. Recent research trends focus on how to intelligently use data from CPS units to enhance wireless connectivity in CPS. AI tools, particularly AI systems and machine learning (ML) algorithms, have been widely applied in the literature to develop efficient schemes for wireless CPS/IoT. This article presents a review on the role of AI in wireless networking for CPS and IoT. In particular, we focus on ML paradigms, such as transfer learning (TL), distributed learning, and federated learning, that have evolved as building blocks for the utilization of large data for learning, adaptation, and predictions in CPS and IoT systems that leverage wireless networking. Furthermore, we also highlight challenges faced by current and future wireless networks pertaining to CPS/IoT, which are yet to be addressed.