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Title	Lpwan Communication-Based Ml For Iot Networks
ID_Doc	35791
Authors	Pullagura L.; Kumari N.V.; Katta S.K.G.
Year	2024
Published	Low-Power Wide Area Network for Large Scale Internet of Things: Architectures, Communication Protocols and Recent Trends
DOI	http://dx.doi.org/10.1201/9781003426974-2
Abstract	This chapter presents an overview of the combination of Machine Learning (ML) and Low Power Wide Area Networks (LPWAN) for Internet of Things (IoT) applications. LPWAN provides long-range and low-power connectivity, making it ideal for IoT devices that are usually deployed in remote or hard-to-reach locations, while ML algorithms can analyze large volumes of data collected by these devices to detect patterns, identify anomalies, and predict future outcomes. This chapter discusses different types of LPWAN and IoT sensors, as well as various ML techniques that can be used in IoT applications, including supervised learning, unsupervised learning, and reinforcement learning. In addition, this chapter explores various applications of LPWAN-based IoT and ML, such as smart homes, smart cities, and industrial IoT, along with the challenges and future directions of these technologies. Overall, this chapter provides insights into the potential benefits of combining ML and LPWAN-based IoT applications and how they can improve decision-making and optimize processes in various industries. © 2024 selection and editorial matter, Mariyam Ouaissa, Mariya Ouaissa, Inam Ullah Khan, Zakaria Boulouard and Junaid Rashid; individual chapters, the contributors.
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