Machine Learning for Relaying Topology: Optimization of IoT Networks With Energy Harvesting
In this paper, we examine Internet of Things (IoT) systems related to smart cities, smart factories, connected cars, etc. To support such systems in a wide area with low power consumption, energy harvesting technology utilizing wireless charging infrastructure is necessary for the longevity of netwo...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10108996/ |
| _version_ | 1797833200338206720 |
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| author | Kiseop Chung Jin-Taek Lim |
| author_facet | Kiseop Chung Jin-Taek Lim |
| author_sort | Kiseop Chung |
| collection | DOAJ |
| description | In this paper, we examine Internet of Things (IoT) systems related to smart cities, smart factories, connected cars, etc. To support such systems in a wide area with low power consumption, energy harvesting technology utilizing wireless charging infrastructure is necessary for the longevity of networks. Considering that the position and amount of energy charged for each device could be unbalanced according to the distribution of nodes and energy sources, maximizing the minimum throughput among all nodes has become an NP-hard challenging issue. To overcome this challenge, we propose a machine learning based relaying topology algorithm with a novel backward-pass rate assessment method to present proper learning direction and an iterative balancing time slot allocation algorithm which can utilize a node with sufficient energy as the relay. To validate our proposed scheme, we conducted simulations on our established system model; thus, we confirm that the proposed scheme is stable and superior to conventional schemes. |
| first_indexed | 2024-04-09T14:20:44Z |
| format | Article |
| id | doaj.art-8145b5517dd44f1abb79e8b341d016ea |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-09T14:20:44Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-8145b5517dd44f1abb79e8b341d016ea2023-05-04T23:00:17ZengIEEEIEEE Access2169-35362023-01-0111418274183910.1109/ACCESS.2023.327063110108996Machine Learning for Relaying Topology: Optimization of IoT Networks With Energy HarvestingKiseop Chung0https://orcid.org/0000-0002-4718-3946Jin-Taek Lim1https://orcid.org/0000-0002-9649-0459Agency for Defense Development, Daejeon, Republic of KoreaAgency for Defense Development, Daejeon, Republic of KoreaIn this paper, we examine Internet of Things (IoT) systems related to smart cities, smart factories, connected cars, etc. To support such systems in a wide area with low power consumption, energy harvesting technology utilizing wireless charging infrastructure is necessary for the longevity of networks. Considering that the position and amount of energy charged for each device could be unbalanced according to the distribution of nodes and energy sources, maximizing the minimum throughput among all nodes has become an NP-hard challenging issue. To overcome this challenge, we propose a machine learning based relaying topology algorithm with a novel backward-pass rate assessment method to present proper learning direction and an iterative balancing time slot allocation algorithm which can utilize a node with sufficient energy as the relay. To validate our proposed scheme, we conducted simulations on our established system model; thus, we confirm that the proposed scheme is stable and superior to conventional schemes.https://ieeexplore.ieee.org/document/10108996/Unsupervised learningvariational autoencoderIoT networkTDMA systemenergy harvestingrelay |
| spellingShingle | Kiseop Chung Jin-Taek Lim Machine Learning for Relaying Topology: Optimization of IoT Networks With Energy Harvesting IEEE Access Unsupervised learning variational autoencoder IoT network TDMA system energy harvesting relay |
| title | Machine Learning for Relaying Topology: Optimization of IoT Networks With Energy Harvesting |
| title_full | Machine Learning for Relaying Topology: Optimization of IoT Networks With Energy Harvesting |
| title_fullStr | Machine Learning for Relaying Topology: Optimization of IoT Networks With Energy Harvesting |
| title_full_unstemmed | Machine Learning for Relaying Topology: Optimization of IoT Networks With Energy Harvesting |
| title_short | Machine Learning for Relaying Topology: Optimization of IoT Networks With Energy Harvesting |
| title_sort | machine learning for relaying topology optimization of iot networks with energy harvesting |
| topic | Unsupervised learning variational autoencoder IoT network TDMA system energy harvesting relay |
| url | https://ieeexplore.ieee.org/document/10108996/ |
| work_keys_str_mv | AT kiseopchung machinelearningforrelayingtopologyoptimizationofiotnetworkswithenergyharvesting AT jintaeklim machinelearningforrelayingtopologyoptimizationofiotnetworkswithenergyharvesting |