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. 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 constitutes the thoroughly refereed proceedings of the 7th International Conference on e-Infrastructure and e-Services for Developing Countries, AFRICOMM 2015, held in Cotonou, Benin, in December 2015. The 25 papers were carefully selected from 51 submissions and cover topics such as communication infrastructure, access to information, green IT applications and security, health.

Low power wide area network (LPWAN) is a promising solution for long range and low power Internet of Things (IoT) and machine to machine (M2M) communication applications. The LPWANs are resource-constrained networks and have critical requirements for long battery life, extended coverage, high scalability, and low device and deployment costs. There are several design and deployment challenges such as media access control, spectrum management, link optimization and adaptability, energy harvesting, duty cycle restrictions, coexistence and interference, interoperability and heterogeneity, security and privacy, and others. LPWAN Technologies for IoT and M2M Applications is intended to provide a one-stop solution for study of LPWAN technologies as it covers a broad range of topics and multidisciplinary aspects of LPWAN and IoT. Primarily, the book focuses on design requirements and constraints, channel access, spectrum management, coexistence and interference issues, energy efficiency, technology candidates, use cases of different applications in smart city, healthcare, and transportation systems, security issues, hardware/software platforms, challenges, and future directions.

This book is a practical resource for designing Internet of Things (IoT) networks and implementing IoT applications from the localization perspective. With the emergence of IoT, machine to machine communication, Industrial IoT, and other societal applications, many applications require knowledge of the exact location of mobile IoT nodes in real-time. As the IoT nodes have computational and energy limitations, it is a crucial research challenge to optimize the network's performance with the highest localization accuracy. Many researchers are working towards such localization problems. However, there is no single book available for the detailed study on IoT node localization. This book provides one-stop multidisciplinary solutions for IoT node localization, design requirements, challenges, constraints, available techniques, comparison, related applications, and future directions. Special features included are theory supported by algorithmic development, treatment of optimization techniques, and applications.