EEMLCR: Energy-Efficient Machine Learning-Based Clustering and Routing for Wireless Sensor Networks
Wireless Sensor Networks (WSNs) offer a powerful technology for sensing and transmitting data across vast geographical regions. However, limitations inherent to WSNs, such as low-power sensor units, communication constraints, and limited processing capabilities, can significantly impact their lifesp...
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| Format: | Article |
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10969782/ |
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| author | Muhammad Akram Sibghat Ullah Bazai Muhammad Imran Ghafoor Saira Akram Qazi Mudassar Ilyas Abid Mehmood Sajid Iqbal Muhammad Asim Rafique |
| author_facet | Muhammad Akram Sibghat Ullah Bazai Muhammad Imran Ghafoor Saira Akram Qazi Mudassar Ilyas Abid Mehmood Sajid Iqbal Muhammad Asim Rafique |
| author_sort | Muhammad Akram |
| collection | DOAJ |
| description | Wireless Sensor Networks (WSNs) offer a powerful technology for sensing and transmitting data across vast geographical regions. However, limitations inherent to WSNs, such as low-power sensor units, communication constraints, and limited processing capabilities, can significantly impact their lifespan. To address these limitations and enhance the energy efficiency of WSNs, it is often necessary to divide sensors into clusters and establish routing to conserve energy. Machine learning algorithms can potentially automate these processes, minimizing energy consumption and extending network lifetime. This research investigates the application of machine learning algorithms, specifically Q-learning and K-means clustering, to propose the Energy-Efficient Machine Learning-based Clustering and Routing (EEMLCR) method for WSNs. This method facilitates cluster formation and routing path selection. The proposed method is compared with the well-established LEACH algorithm and two multi-hop variants, DMHT LEACH and EDMHT LEACH to validate its effectiveness. Our experimental results demonstrate the effectiveness of EEMLCR compared to LEACH and its multi-hop variants (DMHT LEACH and EDMHT LEACH). After 600 rounds in networks comprising 400 nodes, EEMLCR showed significant improvements in key performance metrics. These include increased alive nodes, reduced average energy consumption, higher remaining energy levels, and improved packet reception. Additionally, we compared EEMLCR with recent state-of-the-art algorithms such as EECDA and CMML, where our method demonstrated comparable or superior performance in terms of network lifetime and energy efficiency. By optimizing clustering and routing strategies, WSNs can reduce energy consumption, leading to more efficient utilization of the limited energy resources available to sensor nodes. The primary objective of this research is to contribute to the development of energy-efficient WSNs by leveraging machine learning algorithms for data routing and the cluster-based organization of sensor nodes. |
| format | Article |
| id | doaj-art-22fbbca6a06648d3919f5b06a2f8a31e |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-22fbbca6a06648d3919f5b06a2f8a31e2025-08-20T02:29:34ZengIEEEIEEE Access2169-35362025-01-0113708497087110.1109/ACCESS.2025.356236810969782EEMLCR: Energy-Efficient Machine Learning-Based Clustering and Routing for Wireless Sensor NetworksMuhammad Akram0https://orcid.org/0000-0003-1485-9804Sibghat Ullah Bazai1https://orcid.org/0000-0003-3042-5977Muhammad Imran Ghafoor2https://orcid.org/0000-0002-0809-3163Saira Akram3https://orcid.org/0009-0005-1201-8712Qazi Mudassar Ilyas4https://orcid.org/0000-0003-4238-8093Abid Mehmood5https://orcid.org/0000-0001-9974-9537Sajid Iqbal6https://orcid.org/0000-0002-8464-2275Muhammad Asim Rafique7Department of Software Engineering, Baluchistan University of Information Technology Engineering and Management Sciences (BUITEMS), Quetta, PakistanDepartment of Computer Engineering, Baluchistan University of Information Technology Engineering and Management Sciences (BUITEMS), Quetta, PakistanDepartment of Engineering, Pakistan Television Corporation, Lahore, PakistanDepartment of Computer Engineering, Baluchistan University of Information Technology Engineering and Management Sciences (BUITEMS), Quetta, PakistanDepartment of Information Systems, College of Computer Sciences and Information Technology, King Faisal University, Al Hofuf, Saudi ArabiaDepartment of Management Information Systems, College of Business Administration, King Faisal University, Al Hofuf, Saudi ArabiaDepartment of Information Systems, College of Computer Sciences and Information Technology, King Faisal University, Al Hofuf, Saudi ArabiaDepartment of Information Systems, College of Computer Sciences and Information Technology, King Faisal University, Al Hofuf, Saudi ArabiaWireless Sensor Networks (WSNs) offer a powerful technology for sensing and transmitting data across vast geographical regions. However, limitations inherent to WSNs, such as low-power sensor units, communication constraints, and limited processing capabilities, can significantly impact their lifespan. To address these limitations and enhance the energy efficiency of WSNs, it is often necessary to divide sensors into clusters and establish routing to conserve energy. Machine learning algorithms can potentially automate these processes, minimizing energy consumption and extending network lifetime. This research investigates the application of machine learning algorithms, specifically Q-learning and K-means clustering, to propose the Energy-Efficient Machine Learning-based Clustering and Routing (EEMLCR) method for WSNs. This method facilitates cluster formation and routing path selection. The proposed method is compared with the well-established LEACH algorithm and two multi-hop variants, DMHT LEACH and EDMHT LEACH to validate its effectiveness. Our experimental results demonstrate the effectiveness of EEMLCR compared to LEACH and its multi-hop variants (DMHT LEACH and EDMHT LEACH). After 600 rounds in networks comprising 400 nodes, EEMLCR showed significant improvements in key performance metrics. These include increased alive nodes, reduced average energy consumption, higher remaining energy levels, and improved packet reception. Additionally, we compared EEMLCR with recent state-of-the-art algorithms such as EECDA and CMML, where our method demonstrated comparable or superior performance in terms of network lifetime and energy efficiency. By optimizing clustering and routing strategies, WSNs can reduce energy consumption, leading to more efficient utilization of the limited energy resources available to sensor nodes. The primary objective of this research is to contribute to the development of energy-efficient WSNs by leveraging machine learning algorithms for data routing and the cluster-based organization of sensor nodes.https://ieeexplore.ieee.org/document/10969782/Clustering algorithmsenergy efficiencymachine learningmulti-hop communicationrouting protocolswireless sensor networks |
| spellingShingle | Muhammad Akram Sibghat Ullah Bazai Muhammad Imran Ghafoor Saira Akram Qazi Mudassar Ilyas Abid Mehmood Sajid Iqbal Muhammad Asim Rafique EEMLCR: Energy-Efficient Machine Learning-Based Clustering and Routing for Wireless Sensor Networks IEEE Access Clustering algorithms energy efficiency machine learning multi-hop communication routing protocols wireless sensor networks |
| title | EEMLCR: Energy-Efficient Machine Learning-Based Clustering and Routing for Wireless Sensor Networks |
| title_full | EEMLCR: Energy-Efficient Machine Learning-Based Clustering and Routing for Wireless Sensor Networks |
| title_fullStr | EEMLCR: Energy-Efficient Machine Learning-Based Clustering and Routing for Wireless Sensor Networks |
| title_full_unstemmed | EEMLCR: Energy-Efficient Machine Learning-Based Clustering and Routing for Wireless Sensor Networks |
| title_short | EEMLCR: Energy-Efficient Machine Learning-Based Clustering and Routing for Wireless Sensor Networks |
| title_sort | eemlcr energy efficient machine learning based clustering and routing for wireless sensor networks |
| topic | Clustering algorithms energy efficiency machine learning multi-hop communication routing protocols wireless sensor networks |
| url | https://ieeexplore.ieee.org/document/10969782/ |
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