Machine Learning Approach on Efficient Routing Efficient Techniques in Wireless Sensor Network
Nawkhare, Rahul; Singh, Daljeet (2023-04-03)
IEEE
03.04.2023
R. Nawkhare and D. Singh, "Machine Learning Approach on Efficient Routing Efficient Techniques in Wireless Sensor Network," 2022 IEEE International Conference on Current Development in Engineering and Technology (CCET), Bhopal, India, 2022, pp. 1-6, doi: 10.1109/CCET56606.2022.10080050
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© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists,or reuse of any copyrighted component of this work in other works.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202402121688
https://urn.fi/URN:NBN:fi:oulu-202402121688
Tiivistelmä
Abstract:
Network lifetime optimization is a tough and important problem in Wireless Sensor Networks (WSNs). The mainstream current works outline a variety of methods for increasing network lifetime, such as reducing energy consumption, lowering latency, load balancing, clustering, effective data aggregation, and lowering data transmission delays. The use of Machine Learning (ML) in WSNs has recently received much interest. Because WSN s have limited resources, figuring out methods for increasing usage of resources and accomplish power-effective load balancing is a crucial problem. Traditional routing procedures seek to do this by lowering energy ingestion and extending system lifetime with WSN-optimized routing strategies. However, there are frequently issues like limited flexibility, a reliance on precise mathematical models, and a single consideration element. The key benefits of fast environmental adaptation and ability to combine various parameters can be utilized in WSN s to improve efficiency of routing protocols in terms of power consumption. This study reviews current methods and ML-based routing rules for WSN s. Additionally, suggest future prospects for ML methods to improve WSN routing.
Network lifetime optimization is a tough and important problem in Wireless Sensor Networks (WSNs). The mainstream current works outline a variety of methods for increasing network lifetime, such as reducing energy consumption, lowering latency, load balancing, clustering, effective data aggregation, and lowering data transmission delays. The use of Machine Learning (ML) in WSNs has recently received much interest. Because WSN s have limited resources, figuring out methods for increasing usage of resources and accomplish power-effective load balancing is a crucial problem. Traditional routing procedures seek to do this by lowering energy ingestion and extending system lifetime with WSN-optimized routing strategies. However, there are frequently issues like limited flexibility, a reliance on precise mathematical models, and a single consideration element. The key benefits of fast environmental adaptation and ability to combine various parameters can be utilized in WSN s to improve efficiency of routing protocols in terms of power consumption. This study reviews current methods and ML-based routing rules for WSN s. Additionally, suggest future prospects for ML methods to improve WSN routing.
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