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Recently, Tiny Machine Learning (TinyML) has gained incredible importance due to its capabilities of creating lightweight machine learning (ML) frameworks aiming at low latency, lower energy consumption, lower bandwidth requirement, improved data security and privacy, and other performance necessities. As billions of battery-operated embedded IoT and low power wide area networks (LPWAN) nodes with very low on-board memory and computational capabilities are getting connected to the Internet each year, there is a critical need to have a special computational framework like TinyML.
TinyML for Edge Intelligence in IoT and LPWAN Networks presents the evolution, developments, and advances in TinyML as applied to IoT and LPWANs. It starts by providing the foundations of IoT/LPWANs, low power embedded systems and hardware, the role of artificial intelligence and machine learning in communication networks in general and cloud/edge intelligence. It then presents the concepts, methods, algorithms and tools of TinyML. Practical applications of the use of TinyML are given from health and industrial fields which provide practical guidance on the design of applications and the selection of appropriate technologies.
This chapter is intended to discuss the fundamentals of TinyML and its algorithms. It covers overview of TinyML and benefits, workflow, AI and edge computing, traditional machine learning, deep learning, reinforcement learning and deep reinforcement learning, federated learning, edge computing optimization using AI solutions, computing offloading optimization, energy consumption reduction using non-computation offloading methods, hardware structure optimization, security of edge computing, data privacy, resource allocation optimization.
TinyML enables the deployment of small machine learning models onto tiny edge devices, which face severe resource limitations such as constrained computation, minuscule memory, and low power consumption. Using TinyML, data analysis and interpretation can be performed locally on these devices, allowing real-time actions. TinyML is not an alternative to the fog or cloud paradigms, but it is an auxiliary system that works as an accelerator to the existing paradigms. TinyML offers several benefits, including significant cost savings, energy efficiency, and improved privacy protection.
TinyML for Edge Intelligence in IoT and LPWAN Networks is highly suitable for academic researchers and professional system engineers, architects, designers, testers, deployment engineers seeking to design ultra-lower power and time-critical applications. It would also help in designing the networks for emerging and future applications for resource-constrained nodes.
This book provides one-stop solutions for emerging TinyML for IoT and LPWAN applications.
The principles and methods of TinyML are explained, with a focus on how it can be used for IoT, LPWANs, and 5G applications.
Applications from the healthcare and industrial sectors are presented.