Mixed H2/H∞-Based Fusion Estimation for Energy-Limited Multi-Sensors in Wearable Body Networks
In wireless sensor networks, sensor nodes collect plenty of data for each time period. If all of data are transmitted to a Fusion Center (FC), the power of sensor node would run out rapidly. On the other hand, the data also needs a filter to remove the noise. Therefore, an efficient fusion estimatio...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2017-12-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/18/1/56 |
| _version_ | 1811187365589286912 |
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| author | Chao Li Zhenjiang Zhang Han-Chieh Chao |
| author_facet | Chao Li Zhenjiang Zhang Han-Chieh Chao |
| author_sort | Chao Li |
| collection | DOAJ |
| description | In wireless sensor networks, sensor nodes collect plenty of data for each time period. If all of data are transmitted to a Fusion Center (FC), the power of sensor node would run out rapidly. On the other hand, the data also needs a filter to remove the noise. Therefore, an efficient fusion estimation model, which can save the energy of the sensor nodes while maintaining higher accuracy, is needed. This paper proposes a novel mixed H2/H∞-based energy-efficient fusion estimation model (MHEEFE) for energy-limited Wearable Body Networks. In the proposed model, the communication cost is firstly reduced efficiently while keeping the estimation accuracy. Then, the parameters in quantization method are discussed, and we confirm them by an optimization method with some prior knowledge. Besides, some calculation methods of important parameters are researched which make the final estimates more stable. Finally, an iteration-based weight calculation algorithm is presented, which can improve the fault tolerance of the final estimate. In the simulation, the impacts of some pivotal parameters are discussed. Meanwhile, compared with the other related models, the MHEEFE shows a better performance in accuracy, energy-efficiency and fault tolerance. |
| first_indexed | 2024-04-11T14:02:08Z |
| format | Article |
| id | doaj.art-025e43fc936646ebaabbaff07cc95fe9 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T14:02:08Z |
| publishDate | 2017-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-025e43fc936646ebaabbaff07cc95fe92022-12-22T04:20:04ZengMDPI AGSensors1424-82202017-12-011815610.3390/s18010056s18010056Mixed H2/H∞-Based Fusion Estimation for Energy-Limited Multi-Sensors in Wearable Body NetworksChao Li0Zhenjiang Zhang1Han-Chieh Chao2Key Laboratory of Communication and Information Systems, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Software Engineering, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Information Science and Engineering, Fujian University of Technology, Fuzhou 350118, ChinaIn wireless sensor networks, sensor nodes collect plenty of data for each time period. If all of data are transmitted to a Fusion Center (FC), the power of sensor node would run out rapidly. On the other hand, the data also needs a filter to remove the noise. Therefore, an efficient fusion estimation model, which can save the energy of the sensor nodes while maintaining higher accuracy, is needed. This paper proposes a novel mixed H2/H∞-based energy-efficient fusion estimation model (MHEEFE) for energy-limited Wearable Body Networks. In the proposed model, the communication cost is firstly reduced efficiently while keeping the estimation accuracy. Then, the parameters in quantization method are discussed, and we confirm them by an optimization method with some prior knowledge. Besides, some calculation methods of important parameters are researched which make the final estimates more stable. Finally, an iteration-based weight calculation algorithm is presented, which can improve the fault tolerance of the final estimate. In the simulation, the impacts of some pivotal parameters are discussed. Meanwhile, compared with the other related models, the MHEEFE shows a better performance in accuracy, energy-efficiency and fault tolerance.https://www.mdpi.com/1424-8220/18/1/56fusion estimationwearable sensorsenergy-efficiencyaccuracy |
| spellingShingle | Chao Li Zhenjiang Zhang Han-Chieh Chao Mixed H2/H∞-Based Fusion Estimation for Energy-Limited Multi-Sensors in Wearable Body Networks Sensors fusion estimation wearable sensors energy-efficiency accuracy |
| title | Mixed H2/H∞-Based Fusion Estimation for Energy-Limited Multi-Sensors in Wearable Body Networks |
| title_full | Mixed H2/H∞-Based Fusion Estimation for Energy-Limited Multi-Sensors in Wearable Body Networks |
| title_fullStr | Mixed H2/H∞-Based Fusion Estimation for Energy-Limited Multi-Sensors in Wearable Body Networks |
| title_full_unstemmed | Mixed H2/H∞-Based Fusion Estimation for Energy-Limited Multi-Sensors in Wearable Body Networks |
| title_short | Mixed H2/H∞-Based Fusion Estimation for Energy-Limited Multi-Sensors in Wearable Body Networks |
| title_sort | mixed h2 h∞ based fusion estimation for energy limited multi sensors in wearable body networks |
| topic | fusion estimation wearable sensors energy-efficiency accuracy |
| url | https://www.mdpi.com/1424-8220/18/1/56 |
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