Implementing TinyML in Internet of Things devices: A systematic literature review

Abstract

The Internet of Things is at the heart of society and is experiencing rapid expansion. Its integration with Artificial Intelligence and Machine Learning has led to the emergence of Tiny Machine Learning (TinyML), which enables data processing directly on the device, improving efficiency, reducing latency, and increasing data privacy. Despite the growing relevance of TinyML in the Internet of Things, there is a lack of systematic literature reviews providing a holistic understanding of its implementation, advances, and challenges, which hinders a clear understanding of the available empirical evidence and best practices. To bridge this gap, this study presents a systematic literature review, adhering to the PRISMA protocol and employing a multi-database search strategy, identifying 114 primary studies. The review reveals that TinyML is consolidating as a transformative paradigm for the Internet of Things, experiencing significant research growth since 2020. Applications are diverse, with healthcare and environmental monitoring being the most notable examples. Deep learning models, particularly convolutional neural networks, are frequently employed in this context. The main challenges identified include security vulnerabilities, the need to address ethical considerations like algorithmic bias, and hardware limitations related to memory and processing power. Ultimately, this review offers valuable insights into the current state and prospects of TinyML in the Internet of Things, providing a valuable resource for researchers, developers, and decision-makers in this rapidly evolving field.