AIoT as a Service – Framework, Opportunities, and Challenges
Victor C.M. Leung, Distinguished Professor, Fellow of RSC, FCAE, FEIC, Life Fellow of IEEE
College of Computer Science and Software Engineering, Shenzhen University, and Department of Electrical and Computer Engineering, The University of British Columbia
Victor C. M. Leung is a Distinguished Professor of Computer Science and Software Engineering at Shenzhen University. He was a Professor of Electrical and Computer Engineering and inaugural holder of the TELUS Mobility Research Chair at the University of British Columbia (UBC) before he became an Emeritus Professor at UBC in 2019. His research is in the broad areas of wireless networks and mobile systems, in which he has co-authored more than 1400 refereed journal/conference papers and book chapters. Dr. Leung is serving on the editorial boards of the IEEE Transactions on Green Communications and Networking, IEEE Transactions on Cloud Computing, IEEE Transactions on Computational Social Systems, IEEE Network, IEEE Access, and several other journals. He received the IEEE Vancouver Section Centennial Award, the 2011 UBC Killam Research Prize, the 2017 Canadian Award for Telecommunications Research, the 2018 IEEE TCGCC Distinguished Technical Achievement Recognition Award, and the 2018 MSWiM Reginald Fessenden Award. He co-authored papers that won the 2017 IEEE ComSoc Fred W. Ellersick Prize, the 2017 IEEE Systems Journal Best Paper Award, the 2018 IEEE CSIM Best Journal Paper Award, and the 2019 IEEE TCGCC Best Journal Paper Award. His name is included in the current Clarivate Analytics list of “Highly Cited Researchers”. He is a Life Fellow of IEEE, the Royal Society of Canada, the Canadian Academy of Engineering and the Engineering Institute of Canada.
Abstract: With the increasing adoption of Internet of Things (IoT) and rapid advancements of Artificial Intelligence (AI) applications, the integration of AI and IoT, widely referred as AIoT, is gaining momentum. Realizing AIoT will require massive investments in infrastructure, and lessons learned from network deployments point to the use of shared hardware and software resources that can be virtualized to support various services on demand, resulting in AIoT-as-a-Service (AIoTaaS). In this talk, we start with a review of the idea of Anything-as-a-Service (XaaS), leading to IoTaaS and AIaaS, which form the basis of AIoTaaS. We present a framework for AIoTaaS, which incorporates edge and cloud computing platforms to support the AI functionalities required for different services. Enabled by cloud and edge computing, AIoTaaS can effectively implement machine learning (ML) training and inference functions on cloud and edge devices with much less complexity to enable efficient and effective AI decision making in IoT and data analytics, especially in the area of streaming data and real-time analytics associated with edge computing networks. To implement AIoTaaS, interoperability between components at the device level, software level and platform level should be extended while optimizing system and network operations as well as extracting value from data. In the future, AIoTaaS integrated with 5G and blockchain will be leveraged to achieve more efficient IoT operations, improve human-machine interactions, and enhance data management and analytics, creating a foundation for pervasive AI services and applications.