The Role of Machine Learning in Advancing IoT and WSNs

DOI

10.2991/978-94-6463-787-8_25How to use a DOI?

Keywords

IoT; WSN-IoT; Sensor nodes; AI; Routing; ML; Unsupervised learning; Reinforcement learning; Supervised learning; smart city

Abstract

Artificial intelligence (AI) and machine learning (ML) approaches can effectively manage the automated operations of IoT nodes in intelligent cities. Key IoT applications in smart cities include intellectual buildings, waste management, traffic monitoring, and healthcare. However, the design of WSN-based IoT (WSN-IoT) faces several challenges, including Web range and connectivity problems, power consumption, bandwidth requirements, maximizing network lifetime, transmission protocols, and advanced infrastructure development. One of the most significant challenges for wireless sensor networks (WSNs) is extending their lifespan. Over the past few years, considerable research has been focused on improving network longevity and quality of service (QoS). A WSN’s sensor nodes are independent, distributed devices that use wireless connections to collect and route data to a central hub, also known as a “Base Station,” without the need for a central supervisor. These networks’ range and battery life are constrained by their lower processor, memory, power supply, and other capabilities. To overcome such situations, machine learning (ML) methods can be applied to react correctly. The practice of functioning without human intervention or reprogramming to learn from experiences is known as machine learning. The proliferation of Internet of Things (IoT) based Wireless Sensor Networks (WSNs) has triggered a paradigm shift in the business, necessitating the use of dependable and efficient routing techniques. Preliminaries in Machine Learning for WSN-IoT, IOT protocols, and several ML-based methods are reviewed in this study.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Navneet Kumar
AU  - Subhash Chandra Pandey
PY  - 2025
DA  - 2025/07/17
TI  - The Role of Machine Learning in Advancing IoT and WSNs
BT  - Proceedings of the Recent Advances in Artificial Intelligence for Sustainable Development (RAISD 2025)
PB  - Atlantis Press
SP  - 301
EP  - 317
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-787-8_25
DO  - 10.2991/978-94-6463-787-8_25
ID  - Kumar2025
ER  -