Research on heterogeneous data fusion and arithmetic optimization in multi-sensor systems
Multi-sensor systems integrate diverse sensor data to achieve comprehensive and accurate environmental perception. However, how to effectively fuse heterogeneous data and realize the efficiency of real-time processing is still a hot and difficult issue in current research. Therefore, focusing on dat...
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| Format: | Article |
| Language: | zho |
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China InfoCom Media Group
2024-12-01
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| Series: | 物联网学报 |
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| Online Access: | http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2024.00449/ |
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| _version_ | 1832586361044992000 |
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| author | DING Kai JIANG Chaoyue TAO Ming XIE Renping |
| author_facet | DING Kai JIANG Chaoyue TAO Ming XIE Renping |
| author_sort | DING Kai |
| collection | DOAJ |
| description | Multi-sensor systems integrate diverse sensor data to achieve comprehensive and accurate environmental perception. However, how to effectively fuse heterogeneous data and realize the efficiency of real-time processing is still a hot and difficult issue in current research. Therefore, focusing on data fusion and arithmetic optimization of multi-source heterogeneous sensors, an innovative solution was proposed. Firstly, a data fusion system based on master-slave architecture was designed to solve the problem of multi-source heterogeneous data processing. Secondly, a three-layer "cloud-edge-end" architecture was implemented, leveraging edge servers to offload computational pressure from cloud servers, optimizing task scheduling strategies, and enabling coordinated management of network and computing resources. Finally, the delay and energy consumption requirements of tasks were modeled, and the optimization problem of minimizing system cost was constructed under resource constraints, which was transformed into Markov decision process (MDP) and solved with deep deterministic policy gradient (DDPG) algorithm. Simulation experiments show that the proposed architecture and scheduling algorithm exhibit excellent performance in reducing both latency and energy consumption, and provide a new idea for efficient data fusion and arithmetic optimization in multi-sensor systems. |
| format | Article |
| id | doaj-art-b117b830315d447da05c411aad416ddc |
| institution | Kabale University |
| issn | 2096-3750 |
| language | zho |
| publishDate | 2024-12-01 |
| publisher | China InfoCom Media Group |
| record_format | Article |
| series | 物联网学报 |
| spelling | doaj-art-b117b830315d447da05c411aad416ddc2025-01-25T19:00:29ZzhoChina InfoCom Media Group物联网学报2096-37502024-12-018233379606431Research on heterogeneous data fusion and arithmetic optimization in multi-sensor systemsDING KaiJIANG ChaoyueTAO MingXIE RenpingMulti-sensor systems integrate diverse sensor data to achieve comprehensive and accurate environmental perception. However, how to effectively fuse heterogeneous data and realize the efficiency of real-time processing is still a hot and difficult issue in current research. Therefore, focusing on data fusion and arithmetic optimization of multi-source heterogeneous sensors, an innovative solution was proposed. Firstly, a data fusion system based on master-slave architecture was designed to solve the problem of multi-source heterogeneous data processing. Secondly, a three-layer "cloud-edge-end" architecture was implemented, leveraging edge servers to offload computational pressure from cloud servers, optimizing task scheduling strategies, and enabling coordinated management of network and computing resources. Finally, the delay and energy consumption requirements of tasks were modeled, and the optimization problem of minimizing system cost was constructed under resource constraints, which was transformed into Markov decision process (MDP) and solved with deep deterministic policy gradient (DDPG) algorithm. Simulation experiments show that the proposed architecture and scheduling algorithm exhibit excellent performance in reducing both latency and energy consumption, and provide a new idea for efficient data fusion and arithmetic optimization in multi-sensor systems.http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2024.00449/multi source heterogeneous datadata fusionsensorarithmetic optimization |
| spellingShingle | DING Kai JIANG Chaoyue TAO Ming XIE Renping Research on heterogeneous data fusion and arithmetic optimization in multi-sensor systems 物联网学报 multi source heterogeneous data data fusion sensor arithmetic optimization |
| title | Research on heterogeneous data fusion and arithmetic optimization in multi-sensor systems |
| title_full | Research on heterogeneous data fusion and arithmetic optimization in multi-sensor systems |
| title_fullStr | Research on heterogeneous data fusion and arithmetic optimization in multi-sensor systems |
| title_full_unstemmed | Research on heterogeneous data fusion and arithmetic optimization in multi-sensor systems |
| title_short | Research on heterogeneous data fusion and arithmetic optimization in multi-sensor systems |
| title_sort | research on heterogeneous data fusion and arithmetic optimization in multi sensor systems |
| topic | multi source heterogeneous data data fusion sensor arithmetic optimization |
| url | http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2024.00449/ |
| work_keys_str_mv | AT dingkai researchonheterogeneousdatafusionandarithmeticoptimizationinmultisensorsystems AT jiangchaoyue researchonheterogeneousdatafusionandarithmeticoptimizationinmultisensorsystems AT taoming researchonheterogeneousdatafusionandarithmeticoptimizationinmultisensorsystems AT xierenping researchonheterogeneousdatafusionandarithmeticoptimizationinmultisensorsystems |