An Exploration of State of Art Approaches on Machine Learning Based Energy-Efficient Routing Approaches for Wireless Sensor Networks

DOI

10.2991/978-94-6463-252-1_45How to use a DOI?

Keywords

Clustering; Energy Efficient; Machine Learning; Routing Algorithms; Wireless Sensor Networks

Abstract

Wireless Sensor Networks (WSN) have increased reputation since its widespread applications and potential improvements in various fields. They have increased the enabling of IoT applications, due to easy accessibility and less cost. Data sensing is done through nodes in a wireless sensor network and processed the data, then sent to base station. The main design challenge in WSN is the efficient use of the limited energy and expansion of the network lifetime. It is attained by using an appropriate routing technique. Various routing protocols have been evolved for this regard. Also, the continuous improvements of IoT systems have leads to numerous novel protocols designed for wireless sensor networks, where energy saving is the highest importance. At another hand, the routing protocols have gained the greatest significance, because protocols may change depending on the application and design of networks. So, with the introduction of Machine Learning algorithms in WSN, they can become a self-oriented network. Machine Learning is an idea that machines can learn from the input data and provide correct and innovative decisions. This article reviews current WSNs routing protocols and proposes action plans for future approaches.

Copyright

© 2023 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  - Gummarekula Sattibabu
AU  - Nagarajan Ganesan
AU  - Vankayala Chethan Prakash
PY  - 2023
DA  - 2023/11/09
TI  - An Exploration of State of Art Approaches on Machine Learning Based Energy-Efficient Routing Approaches for Wireless Sensor Networks
BT  - Proceedings of the Second International Conference on Emerging Trends in Engineering (ICETE 2023)
PB  - Atlantis Press
SP  - 416
EP  - 429
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-252-1_45
DO  - 10.2991/978-94-6463-252-1_45
ID  - Sattibabu2023
ER  -