TinyML: Tools, Applications, Challenges, and Future Research Directions

In recent years, Artificial Intelligence (AI) and Machine learning (ML) have gained significant interest from both, industry and academia. Notably, conventional ML techniques require enormous amounts of power to meet the desired accuracy, which has limited their use mainly to high-capability devices such as network nodes. However, with many advancements in technologies such as the Internet of Things (IoT) and edge computing, it is desirable to incorporate ML techniques into resource-constrained embedded devices for distributed and ubiquitous intelligence. This has motivated the emergence of the TinyML paradigm which is an embedded ML technique that enables ML applications on multiple cheap, resource- and power-constrained devices. However, during this transition towards appropriate implementation of the TinyML technology, multiple challenges such as processing capacity optimization, improved reliability, and maintenance of learning models' accuracy require timely solutions. In this article, various avenues available for TinyML implementation are reviewed. Firstly, a background of TinyML is provided, followed by detailed discussions on various tools supporting TinyML. Then, state-of-art applications of TinyML using advanced technologies are detailed. Lastly, various research challenges and future directions are identified.

Survey on Internet of Things enabled by 6G Wireless Networks

The 6G wireless technology is visualized to revolutionize multiple customer services with the Internet of Things (IoT), thereby contributing to a ubiquitous intelligent society comprising autonomous systems. In this chapter, we conduct a detailed survey on the IoT networks with 6G wireless networks and investigate the trending possibilities provided by the 6G technology within the IoT networks and the related utilization; Firstly, we detail the breakthrough IoT technologies and the technological drivers which are anticipated to strengthen IoT networks in future. Next, we present the relevant use cases detailing the discussion on the role of the 6G technology within a broad spectrum of IoT potential applications. Lastly, we highlight the several research scope and challenges and list the potential research needs and encourage further research within the thrust area of IoT enabled by 6G networks.