Minimizing the Carbon Footprint in LoRa-Based IoT Networks: A Machine Learning Perspective on Gateway Positioning
The Internet of Things (IoT) is gaining significant attention for its ability to digitally transform various sectors by enabling seamless connectivity and data exchange. However, deploying these networks is challenging due to the need to tailor configurations to diverse application requirements. To...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Open Journal of the Computer Society |
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| Online Access: | https://ieeexplore.ieee.org/document/10959076/ |
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| author | Francisco-Jose Alvarado-Alcon Rafael Asorey-Cacheda Antonio-Javier Garcia-Sanchez Joan Garcia-Haro |
| author_facet | Francisco-Jose Alvarado-Alcon Rafael Asorey-Cacheda Antonio-Javier Garcia-Sanchez Joan Garcia-Haro |
| author_sort | Francisco-Jose Alvarado-Alcon |
| collection | DOAJ |
| description | The Internet of Things (IoT) is gaining significant attention for its ability to digitally transform various sectors by enabling seamless connectivity and data exchange. However, deploying these networks is challenging due to the need to tailor configurations to diverse application requirements. To date, there has been limited focus on examining and enhancing the carbon footprint (CF) associated with these network deployments. In this study, we present an optimization framework leveraging machine learning techniques to minimize the CF associated with IoT multi-hop network deployments by varying the placement of the required gateways. Additionally, we establish a direct comparison between our proposed machine learning method and the integer linear program (ILP) approach. Our findings reveal that placing gateways using neural networks can achieve a 14% reduction in the CF for simple networks compared to those not using optimization for gateway placement. The ILP method could reduce the CF by 16.6% for identical networks, although it incurs a computational cost more than 250 times higher, which has its own environmental impact. Furthermore, we highlight the superior scalability of machine learning techniques, particularly advantageous for larger networks, as discussed in our concluding remarks. |
| format | Article |
| id | doaj-art-00982035a4a54dbaa0c7f7b9c5c88f84 |
| institution | Kabale University |
| issn | 2644-1268 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of the Computer Society |
| spelling | doaj-art-00982035a4a54dbaa0c7f7b9c5c88f842025-08-20T03:51:58ZengIEEEIEEE Open Journal of the Computer Society2644-12682025-01-01653154210.1109/OJCS.2025.355933110959076Minimizing the Carbon Footprint in LoRa-Based IoT Networks: A Machine Learning Perspective on Gateway PositioningFrancisco-Jose Alvarado-Alcon0https://orcid.org/0000-0002-3416-9547Rafael Asorey-Cacheda1https://orcid.org/0000-0003-0722-4181Antonio-Javier Garcia-Sanchez2https://orcid.org/0000-0001-5095-3035Joan Garcia-Haro3https://orcid.org/0000-0003-0741-7530Department of Information and Communication Technologies, Universidad Politécnica de Cartagena, Cartagena, SpainDepartment of Information and Communication Technologies, Universidad Politécnica de Cartagena, Cartagena, SpainDepartment of Information and Communication Technologies, Universidad Politécnica de Cartagena, Cartagena, SpainDepartment of Information and Communication Technologies, Universidad Politécnica de Cartagena, Cartagena, SpainThe Internet of Things (IoT) is gaining significant attention for its ability to digitally transform various sectors by enabling seamless connectivity and data exchange. However, deploying these networks is challenging due to the need to tailor configurations to diverse application requirements. To date, there has been limited focus on examining and enhancing the carbon footprint (CF) associated with these network deployments. In this study, we present an optimization framework leveraging machine learning techniques to minimize the CF associated with IoT multi-hop network deployments by varying the placement of the required gateways. Additionally, we establish a direct comparison between our proposed machine learning method and the integer linear program (ILP) approach. Our findings reveal that placing gateways using neural networks can achieve a 14% reduction in the CF for simple networks compared to those not using optimization for gateway placement. The ILP method could reduce the CF by 16.6% for identical networks, although it incurs a computational cost more than 250 times higher, which has its own environmental impact. Furthermore, we highlight the superior scalability of machine learning techniques, particularly advantageous for larger networks, as discussed in our concluding remarks.https://ieeexplore.ieee.org/document/10959076/Carbon footprint (CF)Internet of Things (IoT) networksLPWANmultilayer perceptronsneural networks |
| spellingShingle | Francisco-Jose Alvarado-Alcon Rafael Asorey-Cacheda Antonio-Javier Garcia-Sanchez Joan Garcia-Haro Minimizing the Carbon Footprint in LoRa-Based IoT Networks: A Machine Learning Perspective on Gateway Positioning IEEE Open Journal of the Computer Society Carbon footprint (CF) Internet of Things (IoT) networks LPWAN multilayer perceptrons neural networks |
| title | Minimizing the Carbon Footprint in LoRa-Based IoT Networks: A Machine Learning Perspective on Gateway Positioning |
| title_full | Minimizing the Carbon Footprint in LoRa-Based IoT Networks: A Machine Learning Perspective on Gateway Positioning |
| title_fullStr | Minimizing the Carbon Footprint in LoRa-Based IoT Networks: A Machine Learning Perspective on Gateway Positioning |
| title_full_unstemmed | Minimizing the Carbon Footprint in LoRa-Based IoT Networks: A Machine Learning Perspective on Gateway Positioning |
| title_short | Minimizing the Carbon Footprint in LoRa-Based IoT Networks: A Machine Learning Perspective on Gateway Positioning |
| title_sort | minimizing the carbon footprint in lora based iot networks a machine learning perspective on gateway positioning |
| topic | Carbon footprint (CF) Internet of Things (IoT) networks LPWAN multilayer perceptrons neural networks |
| url | https://ieeexplore.ieee.org/document/10959076/ |
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