An Overview of Machine Learning-Based Energy-Efficient Routing Algorithms in Wireless Sensor Networks
Machine learning (ML) technology has shown its unique advantages in many fields and has excellent performance in many applications, such as image recognition, speech recognition, recommendation systems, and natural language processing. Recently, the applicability of ML in wireless sensor networks (W...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/10/13/1539 |
| _version_ | 1797528844178030592 |
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| author | Qianao Ding Rongbo Zhu Hao Liu Maode Ma |
| author_facet | Qianao Ding Rongbo Zhu Hao Liu Maode Ma |
| author_sort | Qianao Ding |
| collection | DOAJ |
| description | Machine learning (ML) technology has shown its unique advantages in many fields and has excellent performance in many applications, such as image recognition, speech recognition, recommendation systems, and natural language processing. Recently, the applicability of ML in wireless sensor networks (WSNs) has attracted much attention. As resources are limited in WSNs, identifying how to improve resource utilization and achieve power-efficient load balancing is becoming a critical issue in WSNs. Traditional green routing algorithms aim to achieve this by reducing energy consumption and prolonging network lifetime through optimized routing schemes in WSNs. However, there are usually problems such as poor flexibility, a single consideration factor, and a reliance on accurate mathematical models. ML techniques can quickly adapt to environmental changes and integrate multiple factors for routing decisions, which provides new ideas for intelligent energy-efficient routing algorithms in WSNs. In this paper, we survey and propose a theoretical hypothetic model formulation of ML as an effective method for creating a power-efficient green routing model that can overcome the limitations of traditional green routing methods. In addition, the study also provides an overview of past, present, and future progress in green routing schemes in WSNs. The contents of this paper will appeal to a wide range of audiences interested in ML-based WSNs. |
| first_indexed | 2024-03-10T10:05:07Z |
| format | Article |
| id | doaj.art-faddebf21bc24446afb29e0356c2c0e4 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T10:05:07Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-faddebf21bc24446afb29e0356c2c0e42023-11-22T01:40:17ZengMDPI AGElectronics2079-92922021-06-011013153910.3390/electronics10131539An Overview of Machine Learning-Based Energy-Efficient Routing Algorithms in Wireless Sensor NetworksQianao Ding0Rongbo Zhu1Hao Liu2Maode Ma3College of Computer Science, South-Central University for Nationalities, Wuhan 430074, ChinaCollege of Informatics, Huazhong Agricultural University, Wuhan 430070, ChinaCollege of Computer Science, South-Central University for Nationalities, Wuhan 430074, ChinaSchool of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, SingaporeMachine learning (ML) technology has shown its unique advantages in many fields and has excellent performance in many applications, such as image recognition, speech recognition, recommendation systems, and natural language processing. Recently, the applicability of ML in wireless sensor networks (WSNs) has attracted much attention. As resources are limited in WSNs, identifying how to improve resource utilization and achieve power-efficient load balancing is becoming a critical issue in WSNs. Traditional green routing algorithms aim to achieve this by reducing energy consumption and prolonging network lifetime through optimized routing schemes in WSNs. However, there are usually problems such as poor flexibility, a single consideration factor, and a reliance on accurate mathematical models. ML techniques can quickly adapt to environmental changes and integrate multiple factors for routing decisions, which provides new ideas for intelligent energy-efficient routing algorithms in WSNs. In this paper, we survey and propose a theoretical hypothetic model formulation of ML as an effective method for creating a power-efficient green routing model that can overcome the limitations of traditional green routing methods. In addition, the study also provides an overview of past, present, and future progress in green routing schemes in WSNs. The contents of this paper will appeal to a wide range of audiences interested in ML-based WSNs.https://www.mdpi.com/2079-9292/10/13/1539machine learningrouting algorithmsenergy efficientwireless sensor networks |
| spellingShingle | Qianao Ding Rongbo Zhu Hao Liu Maode Ma An Overview of Machine Learning-Based Energy-Efficient Routing Algorithms in Wireless Sensor Networks Electronics machine learning routing algorithms energy efficient wireless sensor networks |
| title | An Overview of Machine Learning-Based Energy-Efficient Routing Algorithms in Wireless Sensor Networks |
| title_full | An Overview of Machine Learning-Based Energy-Efficient Routing Algorithms in Wireless Sensor Networks |
| title_fullStr | An Overview of Machine Learning-Based Energy-Efficient Routing Algorithms in Wireless Sensor Networks |
| title_full_unstemmed | An Overview of Machine Learning-Based Energy-Efficient Routing Algorithms in Wireless Sensor Networks |
| title_short | An Overview of Machine Learning-Based Energy-Efficient Routing Algorithms in Wireless Sensor Networks |
| title_sort | overview of machine learning based energy efficient routing algorithms in wireless sensor networks |
| topic | machine learning routing algorithms energy efficient wireless sensor networks |
| url | https://www.mdpi.com/2079-9292/10/13/1539 |
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