Dynamic Sensor-Based Data Management Optimization Strategy of Edge Artificial Intelligence Model for Intelligent Transportation System
In the intelligent transportation field, object recognition, detection, and location applications face significant real-time challenges. To address these issues, we propose an automatic sensor-based data loading and unloading optimization strategy for algorithm models. This strategy is designed for...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/7/2089 |
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| _version_ | 1850213170361139200 |
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| author | Nu Wen Ying Zhou Yang Wang Ye Zheng Yong Fan Yang Liu Yankun Wang Minmin Li |
| author_facet | Nu Wen Ying Zhou Yang Wang Ye Zheng Yong Fan Yang Liu Yankun Wang Minmin Li |
| author_sort | Nu Wen |
| collection | DOAJ |
| description | In the intelligent transportation field, object recognition, detection, and location applications face significant real-time challenges. To address these issues, we propose an automatic sensor-based data loading and unloading optimization strategy for algorithm models. This strategy is designed for artificial intelligence (AI) application systems that leverage edge computing. It aims to solve resource allocation optimization and improve operational efficiency in edge computing environments. By doing so, it meets the real-time computing requirements of intelligent transportation business applications. By adopting node and sensor management mechanisms as well as efficient communication protocols, dynamic sensor-based data management of AI algorithm models was achieved, such as pedestrian object recognition, vehicle object detection, and ship object positioning. Experimental results show that while maintaining the same recall rate, the inference time is reduced to one tenth or even one twentieth of the original time. And this strategy can enhance privacy protection of sensor-based data. In the future research, we may consider integrating distributed computing under high load conditions to further optimize the response time of model loading and unloading for multi-service interaction, and enhance the balance and scalability of the system. |
| format | Article |
| id | doaj-art-e6a5379a731549e39607482c2074ed4a |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-e6a5379a731549e39607482c2074ed4a2025-08-20T02:09:11ZengMDPI AGSensors1424-82202025-03-01257208910.3390/s25072089Dynamic Sensor-Based Data Management Optimization Strategy of Edge Artificial Intelligence Model for Intelligent Transportation SystemNu Wen0Ying Zhou1Yang Wang2Ye Zheng3Yong Fan4Yang Liu5Yankun Wang6Minmin Li7Internet of Things Research Institute, Shenzhen Polytechnic University, Shenzhen 518055, ChinaSchool of Information Science and Engineering, Shandong Agricultural University, Tai’an 271018, ChinaInternet of Things Research Institute, Shenzhen Polytechnic University, Shenzhen 518055, ChinaFaculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, ChinaInternet of Things Research Institute, Shenzhen Polytechnic University, Shenzhen 518055, ChinaHefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, ChinaInternet of Things Research Institute, Shenzhen Polytechnic University, Shenzhen 518055, ChinaKey Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen 518055, ChinaIn the intelligent transportation field, object recognition, detection, and location applications face significant real-time challenges. To address these issues, we propose an automatic sensor-based data loading and unloading optimization strategy for algorithm models. This strategy is designed for artificial intelligence (AI) application systems that leverage edge computing. It aims to solve resource allocation optimization and improve operational efficiency in edge computing environments. By doing so, it meets the real-time computing requirements of intelligent transportation business applications. By adopting node and sensor management mechanisms as well as efficient communication protocols, dynamic sensor-based data management of AI algorithm models was achieved, such as pedestrian object recognition, vehicle object detection, and ship object positioning. Experimental results show that while maintaining the same recall rate, the inference time is reduced to one tenth or even one twentieth of the original time. And this strategy can enhance privacy protection of sensor-based data. In the future research, we may consider integrating distributed computing under high load conditions to further optimize the response time of model loading and unloading for multi-service interaction, and enhance the balance and scalability of the system.https://www.mdpi.com/1424-8220/25/7/2089intelligent transportationartificial intelligencesensor-based data |
| spellingShingle | Nu Wen Ying Zhou Yang Wang Ye Zheng Yong Fan Yang Liu Yankun Wang Minmin Li Dynamic Sensor-Based Data Management Optimization Strategy of Edge Artificial Intelligence Model for Intelligent Transportation System Sensors intelligent transportation artificial intelligence sensor-based data |
| title | Dynamic Sensor-Based Data Management Optimization Strategy of Edge Artificial Intelligence Model for Intelligent Transportation System |
| title_full | Dynamic Sensor-Based Data Management Optimization Strategy of Edge Artificial Intelligence Model for Intelligent Transportation System |
| title_fullStr | Dynamic Sensor-Based Data Management Optimization Strategy of Edge Artificial Intelligence Model for Intelligent Transportation System |
| title_full_unstemmed | Dynamic Sensor-Based Data Management Optimization Strategy of Edge Artificial Intelligence Model for Intelligent Transportation System |
| title_short | Dynamic Sensor-Based Data Management Optimization Strategy of Edge Artificial Intelligence Model for Intelligent Transportation System |
| title_sort | dynamic sensor based data management optimization strategy of edge artificial intelligence model for intelligent transportation system |
| topic | intelligent transportation artificial intelligence sensor-based data |
| url | https://www.mdpi.com/1424-8220/25/7/2089 |
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