Collaboration of IoT devices in smart home scenarios: algorithm research based on graph neural networks and federated learning
Abstract Traditional IoT device collaboration is usually static and cannot adjust the collaboration mode between devices according to various changes, which limits work efficiency. To this end, an IoT device collaboration optimization algorithm based on graph neural network and federated learning is...
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| Format: | Article |
| Language: | English |
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Springer
2025-01-01
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| Series: | Discover Internet of Things |
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| Online Access: | https://doi.org/10.1007/s43926-025-00096-7 |
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| _version_ | 1841544346019561472 |
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| author | Yuanquan Zhong |
| author_facet | Yuanquan Zhong |
| author_sort | Yuanquan Zhong |
| collection | DOAJ |
| description | Abstract Traditional IoT device collaboration is usually static and cannot adjust the collaboration mode between devices according to various changes, which limits work efficiency. To this end, an IoT device collaboration optimization algorithm based on graph neural network and federated learning is studied. This method abstracts various IoT device nodes and their communication relationships into graph structured data for storage, and then uses federated learning to train the graph convolutional network with graph structured data. The obtained model can be used to optimize the collaboration mode of IoT devices. During the training process, the total average MSE (mean square error) between the output and the label of the graph convolutional network model based on federated learning is 0.968; the total standard deviation of MSE is 0.0353; the total time from training to model convergence is 435.82 s, of which data transmission time accounts for 27.1% and model training time accounts for 72.9%. In a 2-h practical experiment, the graph convolutional network model based on federated learning was used to optimize the collaboration mode of smart homes, achieving a target environment residence time of 87 min and a total power consumption reduction of 0.69 kW·h. The results show that this method can effectively optimize the collaboration efficiency of IoT devices, reduce training time and network overhead, but it fails to improve the prediction accuracy of the model and may also lead to a decrease in stability. |
| format | Article |
| id | doaj-art-33c7ecf1865f479b8dfcf20c01ea54d0 |
| institution | Kabale University |
| issn | 2730-7239 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Internet of Things |
| spelling | doaj-art-33c7ecf1865f479b8dfcf20c01ea54d02025-01-12T12:36:01ZengSpringerDiscover Internet of Things2730-72392025-01-015111910.1007/s43926-025-00096-7Collaboration of IoT devices in smart home scenarios: algorithm research based on graph neural networks and federated learningYuanquan Zhong0Academic Affairs Office, Anhui Wenda University of Information EngineeringAbstract Traditional IoT device collaboration is usually static and cannot adjust the collaboration mode between devices according to various changes, which limits work efficiency. To this end, an IoT device collaboration optimization algorithm based on graph neural network and federated learning is studied. This method abstracts various IoT device nodes and their communication relationships into graph structured data for storage, and then uses federated learning to train the graph convolutional network with graph structured data. The obtained model can be used to optimize the collaboration mode of IoT devices. During the training process, the total average MSE (mean square error) between the output and the label of the graph convolutional network model based on federated learning is 0.968; the total standard deviation of MSE is 0.0353; the total time from training to model convergence is 435.82 s, of which data transmission time accounts for 27.1% and model training time accounts for 72.9%. In a 2-h practical experiment, the graph convolutional network model based on federated learning was used to optimize the collaboration mode of smart homes, achieving a target environment residence time of 87 min and a total power consumption reduction of 0.69 kW·h. The results show that this method can effectively optimize the collaboration efficiency of IoT devices, reduce training time and network overhead, but it fails to improve the prediction accuracy of the model and may also lead to a decrease in stability.https://doi.org/10.1007/s43926-025-00096-7IoT device collaborationGraph neural networkFederated learningGraph structureSmart home |
| spellingShingle | Yuanquan Zhong Collaboration of IoT devices in smart home scenarios: algorithm research based on graph neural networks and federated learning Discover Internet of Things IoT device collaboration Graph neural network Federated learning Graph structure Smart home |
| title | Collaboration of IoT devices in smart home scenarios: algorithm research based on graph neural networks and federated learning |
| title_full | Collaboration of IoT devices in smart home scenarios: algorithm research based on graph neural networks and federated learning |
| title_fullStr | Collaboration of IoT devices in smart home scenarios: algorithm research based on graph neural networks and federated learning |
| title_full_unstemmed | Collaboration of IoT devices in smart home scenarios: algorithm research based on graph neural networks and federated learning |
| title_short | Collaboration of IoT devices in smart home scenarios: algorithm research based on graph neural networks and federated learning |
| title_sort | collaboration of iot devices in smart home scenarios algorithm research based on graph neural networks and federated learning |
| topic | IoT device collaboration Graph neural network Federated learning Graph structure Smart home |
| url | https://doi.org/10.1007/s43926-025-00096-7 |
| work_keys_str_mv | AT yuanquanzhong collaborationofiotdevicesinsmarthomescenariosalgorithmresearchbasedongraphneuralnetworksandfederatedlearning |