Distributed Target Tracking in Sensor Networks by Consistency Algorithm and Semantic Moving Computing of Internet of Things
The target tracking algorithm of mobile wireless sensor networks involves target motion trend prediction and subsequent node guidance. This study aims to solve the problems of global consistency of node information and significant errors in forecasting fast-moving targets’ trajectories th...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9762985/ |
| _version_ | 1811335701801730048 |
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| author | Yun Wang Han Liu Jun Zhou Yi Wan |
| author_facet | Yun Wang Han Liu Jun Zhou Yi Wan |
| author_sort | Yun Wang |
| collection | DOAJ |
| description | The target tracking algorithm of mobile wireless sensor networks involves target motion trend prediction and subsequent node guidance. This study aims to solve the problems of global consistency of node information and significant errors in forecasting fast-moving targets’ trajectories through traditional distributed tracking methods in sensor networks. Initially, the average consistency algorithm is used to average the local measurements of each node to achieve global consistency. Then, semantic moving computing of the Internet of Things calculates and analyzes the node movement to support the subsequent movement guidance of nodes and target movement prediction. Finally, the simulation experiment is carried out to evaluate the commonly used target trajectory prediction model. The simulation results show that the node movement algorithm by average consistency can effectively improve the positioning accuracy of the network for moving targets. Besides, the positioning error decreases with the increase of the sensing radius R, the number of moving nodes nm, and the total number of nodes ns deployed in a particular range in a two-dimensional (2D) space. The positioning error after node movement in 2D space is about 20%–30%R lower than that in a static state. After node movement in a three-dimensional (3D) space, the positioning error is about 40%–50%R lower than in a dormant state. When the target moves at a speed greater than 7m/s, the consistency-based moving computing algorithm’s target loss rate and tracking errors are about 0~10% and 1.5%~2% lower than the target tracking algorithm via Kalman Filter. Therefore, the algorithm reported here can precisely track the high-speed moving target. The existing research on point target tracking has problems of insufficient accuracy and robustness. The algorithm proposed here has stronger robustness, reduced data error in multi-node, and more flexible node movements, providing a reference for the subsequent research on distributed point target tracking. |
| first_indexed | 2024-04-13T17:28:09Z |
| format | Article |
| id | doaj.art-c8c13e1168cc4b74b201d60a22c8098d |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-13T17:28:09Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-c8c13e1168cc4b74b201d60a22c8098d2022-12-22T02:37:41ZengIEEEIEEE Access2169-35362022-01-0110516345164210.1109/ACCESS.2022.31704779762985Distributed Target Tracking in Sensor Networks by Consistency Algorithm and Semantic Moving Computing of Internet of ThingsYun Wang0Han Liu1Jun Zhou2Yi Wan3https://orcid.org/0000-0001-5317-7748Department of Computer Engineering, Shanxi Polytechnic College, Taiyuan, ChinaCollege of Quality and Technical Supervision, Hebei University, Baoding, ChinaSchool of Artificial Intelligence, Chongqing Business Vocational College, Chongqing, ChinaSchool of Information Engineering, Southwest University of Science and Technology, Mianyang, ChinaThe target tracking algorithm of mobile wireless sensor networks involves target motion trend prediction and subsequent node guidance. This study aims to solve the problems of global consistency of node information and significant errors in forecasting fast-moving targets’ trajectories through traditional distributed tracking methods in sensor networks. Initially, the average consistency algorithm is used to average the local measurements of each node to achieve global consistency. Then, semantic moving computing of the Internet of Things calculates and analyzes the node movement to support the subsequent movement guidance of nodes and target movement prediction. Finally, the simulation experiment is carried out to evaluate the commonly used target trajectory prediction model. The simulation results show that the node movement algorithm by average consistency can effectively improve the positioning accuracy of the network for moving targets. Besides, the positioning error decreases with the increase of the sensing radius R, the number of moving nodes nm, and the total number of nodes ns deployed in a particular range in a two-dimensional (2D) space. The positioning error after node movement in 2D space is about 20%–30%R lower than that in a static state. After node movement in a three-dimensional (3D) space, the positioning error is about 40%–50%R lower than in a dormant state. When the target moves at a speed greater than 7m/s, the consistency-based moving computing algorithm’s target loss rate and tracking errors are about 0~10% and 1.5%~2% lower than the target tracking algorithm via Kalman Filter. Therefore, the algorithm reported here can precisely track the high-speed moving target. The existing research on point target tracking has problems of insufficient accuracy and robustness. The algorithm proposed here has stronger robustness, reduced data error in multi-node, and more flexible node movements, providing a reference for the subsequent research on distributed point target tracking.https://ieeexplore.ieee.org/document/9762985/Consistency algorithmmoving computing algorithmmobile wireless sensor networkdistributed target tracking |
| spellingShingle | Yun Wang Han Liu Jun Zhou Yi Wan Distributed Target Tracking in Sensor Networks by Consistency Algorithm and Semantic Moving Computing of Internet of Things IEEE Access Consistency algorithm moving computing algorithm mobile wireless sensor network distributed target tracking |
| title | Distributed Target Tracking in Sensor Networks by Consistency Algorithm and Semantic Moving Computing of Internet of Things |
| title_full | Distributed Target Tracking in Sensor Networks by Consistency Algorithm and Semantic Moving Computing of Internet of Things |
| title_fullStr | Distributed Target Tracking in Sensor Networks by Consistency Algorithm and Semantic Moving Computing of Internet of Things |
| title_full_unstemmed | Distributed Target Tracking in Sensor Networks by Consistency Algorithm and Semantic Moving Computing of Internet of Things |
| title_short | Distributed Target Tracking in Sensor Networks by Consistency Algorithm and Semantic Moving Computing of Internet of Things |
| title_sort | distributed target tracking in sensor networks by consistency algorithm and semantic moving computing of internet of things |
| topic | Consistency algorithm moving computing algorithm mobile wireless sensor network distributed target tracking |
| url | https://ieeexplore.ieee.org/document/9762985/ |
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