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- Publisher Website: 10.1109/IOTM.001.2400125
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Article: Generative AI for Energy Harvesting Internet of Things Network: Fundamental, Applications, and Opportunities
| Title | Generative AI for Energy Harvesting Internet of Things Network: Fundamental, Applications, and Opportunities |
|---|---|
| Authors | |
| Issue Date | 1-Jan-2025 |
| Citation | IEEE Internet of Things Magazine, 2025, v. 8, n. 3, p. 72-80 How to Cite? |
| Abstract | Internet of Things (IoT) devices are typically powered by small-sized batteries with limited energy storage capacity, requiring regular replacement or recharging. To reduce costs and maintain connectivity in IoT networks, energy harvesting technologies are regarded as a promising solution. Notably, due to its robust analytical and generative capabilities, generative artificial intelligence (GenAl) has demonstrated significant potential in optimizing energy harvesting networks. Therefore, we discuss key applications of GenAl in improving energy harvesting IoT networks in this article. Specifically, we first review the key technologies of GenAI and the architecture of energy harvesting IoT networks. Then, we show how GenAI can address different problems to improve the performance of the energy harvesting IoT networks. Subsequently, we present a case study of unmanned aerial vehicle (UAV)-enabled data col-lection and energy transfer. The case study shows distinctively the necessity of energy harvesting technology and verify the effectiveness of GenAI-based methods. Finally, we discuss some import-ant open directions. |
| Persistent Identifier | http://hdl.handle.net/10722/362168 |
| ISSN |
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Xie, Wenwen | - |
| dc.contributor.author | Sun, Geng | - |
| dc.contributor.author | Li, Jiahui | - |
| dc.contributor.author | Wang, Jiacheng | - |
| dc.contributor.author | Du, Hongyang | - |
| dc.contributor.author | Niyato, Dusit | - |
| dc.contributor.author | Dobre, Octavia A. | - |
| dc.date.accessioned | 2025-09-19T00:33:26Z | - |
| dc.date.available | 2025-09-19T00:33:26Z | - |
| dc.date.issued | 2025-01-01 | - |
| dc.identifier.citation | IEEE Internet of Things Magazine, 2025, v. 8, n. 3, p. 72-80 | - |
| dc.identifier.issn | 2576-3180 | - |
| dc.identifier.uri | http://hdl.handle.net/10722/362168 | - |
| dc.description.abstract | Internet of Things (IoT) devices are typically powered by small-sized batteries with limited energy storage capacity, requiring regular replacement or recharging. To reduce costs and maintain connectivity in IoT networks, energy harvesting technologies are regarded as a promising solution. Notably, due to its robust analytical and generative capabilities, generative artificial intelligence (GenAl) has demonstrated significant potential in optimizing energy harvesting networks. Therefore, we discuss key applications of GenAl in improving energy harvesting IoT networks in this article. Specifically, we first review the key technologies of GenAI and the architecture of energy harvesting IoT networks. Then, we show how GenAI can address different problems to improve the performance of the energy harvesting IoT networks. Subsequently, we present a case study of unmanned aerial vehicle (UAV)-enabled data col-lection and energy transfer. The case study shows distinctively the necessity of energy harvesting technology and verify the effectiveness of GenAI-based methods. Finally, we discuss some import-ant open directions. | - |
| dc.language | eng | - |
| dc.relation.ispartof | IEEE Internet of Things Magazine | - |
| dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
| dc.title | Generative AI for Energy Harvesting Internet of Things Network: Fundamental, Applications, and Opportunities | - |
| dc.type | Article | - |
| dc.identifier.doi | 10.1109/IOTM.001.2400125 | - |
| dc.identifier.scopus | eid_2-s2.0-105004210706 | - |
| dc.identifier.volume | 8 | - |
| dc.identifier.issue | 3 | - |
| dc.identifier.spage | 72 | - |
| dc.identifier.epage | 80 | - |
| dc.identifier.eissn | 2576-3199 | - |
| dc.identifier.issnl | 2576-3180 | - |
