A Real-Time Smooth Weighted Data Fusion Algorithm for Greenhouse Sensing Based on Wireless Sensor Networks
Wireless sensor networks are widely used to acquire environmental parameters to support agricultural production. However, data variation and noise caused by actuators often produce complex measurement conditions. These factors can lead to nonconformity in reporting samples from different nodes and c...
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doaj-353a4a9bd6b14960b2810730834f6bc02020-11-24T21:18:24ZengMDPI AGSensors1424-82202017-11-011711255510.3390/s17112555s17112555A Real-Time Smooth Weighted Data Fusion Algorithm for Greenhouse Sensing Based on Wireless Sensor NetworksTengyue Zou0Yuanxia Wang1Mengyi Wang2Shouying Lin3College of Mechanical and Electronic Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, ChinaCollege of Mechanical and Electronic Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, ChinaCollege of Mechanical and Electronic Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, ChinaCollege of Mechanical and Electronic Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, ChinaWireless sensor networks are widely used to acquire environmental parameters to support agricultural production. However, data variation and noise caused by actuators often produce complex measurement conditions. These factors can lead to nonconformity in reporting samples from different nodes and cause errors when making a final decision. Data fusion is well suited to reduce the influence of actuator-based noise and improve automation accuracy. A key step is to identify the sensor nodes disturbed by actuator noise and reduce their degree of participation in the data fusion results. A smoothing value is introduced and a searching method based on Prim’s algorithm is designed to help obtain stable sensing data. A voting mechanism with dynamic weights is then proposed to obtain the data fusion result. The dynamic weighting process can sharply reduce the influence of actuator noise in data fusion and gradually condition the data to normal levels over time. To shorten the data fusion time in large networks, an acceleration method with prediction is also presented to reduce the data collection time. A real-time system is implemented on STMicroelectronics STM32F103 and NORDIC nRF24L01 platforms and the experimental results verify the improvement provided by these new algorithms.https://www.mdpi.com/1424-8220/17/11/2555greenhousewireless sensor networkdata fusiondynamic weight |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Tengyue Zou Yuanxia Wang Mengyi Wang Shouying Lin |
spellingShingle |
Tengyue Zou Yuanxia Wang Mengyi Wang Shouying Lin A Real-Time Smooth Weighted Data Fusion Algorithm for Greenhouse Sensing Based on Wireless Sensor Networks Sensors greenhouse wireless sensor network data fusion dynamic weight |
author_facet |
Tengyue Zou Yuanxia Wang Mengyi Wang Shouying Lin |
author_sort |
Tengyue Zou |
title |
A Real-Time Smooth Weighted Data Fusion Algorithm for Greenhouse Sensing Based on Wireless Sensor Networks |
title_short |
A Real-Time Smooth Weighted Data Fusion Algorithm for Greenhouse Sensing Based on Wireless Sensor Networks |
title_full |
A Real-Time Smooth Weighted Data Fusion Algorithm for Greenhouse Sensing Based on Wireless Sensor Networks |
title_fullStr |
A Real-Time Smooth Weighted Data Fusion Algorithm for Greenhouse Sensing Based on Wireless Sensor Networks |
title_full_unstemmed |
A Real-Time Smooth Weighted Data Fusion Algorithm for Greenhouse Sensing Based on Wireless Sensor Networks |
title_sort |
real-time smooth weighted data fusion algorithm for greenhouse sensing based on wireless sensor networks |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2017-11-01 |
description |
Wireless sensor networks are widely used to acquire environmental parameters to support agricultural production. However, data variation and noise caused by actuators often produce complex measurement conditions. These factors can lead to nonconformity in reporting samples from different nodes and cause errors when making a final decision. Data fusion is well suited to reduce the influence of actuator-based noise and improve automation accuracy. A key step is to identify the sensor nodes disturbed by actuator noise and reduce their degree of participation in the data fusion results. A smoothing value is introduced and a searching method based on Prim’s algorithm is designed to help obtain stable sensing data. A voting mechanism with dynamic weights is then proposed to obtain the data fusion result. The dynamic weighting process can sharply reduce the influence of actuator noise in data fusion and gradually condition the data to normal levels over time. To shorten the data fusion time in large networks, an acceleration method with prediction is also presented to reduce the data collection time. A real-time system is implemented on STMicroelectronics STM32F103 and NORDIC nRF24L01 platforms and the experimental results verify the improvement provided by these new algorithms. |
topic |
greenhouse wireless sensor network data fusion dynamic weight |
url |
https://www.mdpi.com/1424-8220/17/11/2555 |
work_keys_str_mv |
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