Children Tracking System Based on ZigBee Wireless Network and Neural Network
The safety of children is one of the fundamental concerns of parents. Recently, child kidnapping has increased by a large percentage, some children have been found, and some children have not found yet. This paper proposes an indoor localization system based on ZigBee wireless sensor network (WSN)...
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| Language: | English |
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middle technical university
2023-03-01
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| Series: | Journal of Techniques |
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| Online Access: | https://journal.mtu.edu.iq/index.php/MTU/article/view/838 |
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| author | Nadia Ahmed Sadik Kamel Gharghan Ammar Hussein Mutlag M. G. M. Abdolrasol |
| author_facet | Nadia Ahmed Sadik Kamel Gharghan Ammar Hussein Mutlag M. G. M. Abdolrasol |
| author_sort | Nadia Ahmed |
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The safety of children is one of the fundamental concerns of parents. Recently, child kidnapping has increased by a large percentage, some children have been found, and some children have not found yet. This paper proposes an indoor localization system based on ZigBee wireless sensor network (WSN) and Backpropagation Artificial Neural Network (BP-ANN) to locate the child in an indoor environment. Several ANN topologies were investigated to select the best one with minimum tracking or localization error. The Received Signal Strength Indicator (RSSI) was collected from four ZigBee XBee S2C anchor nodes by the mobile node carried by the child in an indoor area of 32m × 32m. The RSSI was collected from 127 test points inside the tested area. The measured RSSI was used to train, test, and validate the performance of BP-ANN to determine the two dimensions (2D) of the target child’s location. Different topologies of ANN have been examined for training, testing, and validation which are 5-5, 10-10, 15-15, and 20-20 neurons in the hidden layer. The findings indicate that the 20-20 ANN topology can achieve higher accuracy than other topologies. Additionally, 20-20 topology localization errors were 1.0, 1.157, and 1.356 m for training, testing, and validating ANN performance.
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| format | Article |
| id | doaj-art-7096f9bc0dcd4e1096b31ed1f62e2f4d |
| institution | Kabale University |
| issn | 1818-653X 2708-8383 |
| language | English |
| publishDate | 2023-03-01 |
| publisher | middle technical university |
| record_format | Article |
| series | Journal of Techniques |
| spelling | doaj-art-7096f9bc0dcd4e1096b31ed1f62e2f4d2025-01-19T11:01:59Zengmiddle technical universityJournal of Techniques1818-653X2708-83832023-03-015110.51173/jt.v5i1.838Children Tracking System Based on ZigBee Wireless Network and Neural NetworkNadia Ahmed0Sadik Kamel Gharghan1Ammar Hussein Mutlag2M. G. M. Abdolrasol3Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq.Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq.Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq.University Kebangsaan Malaysia, UKM Bangi 43600, Selangor, Malaysia The safety of children is one of the fundamental concerns of parents. Recently, child kidnapping has increased by a large percentage, some children have been found, and some children have not found yet. This paper proposes an indoor localization system based on ZigBee wireless sensor network (WSN) and Backpropagation Artificial Neural Network (BP-ANN) to locate the child in an indoor environment. Several ANN topologies were investigated to select the best one with minimum tracking or localization error. The Received Signal Strength Indicator (RSSI) was collected from four ZigBee XBee S2C anchor nodes by the mobile node carried by the child in an indoor area of 32m × 32m. The RSSI was collected from 127 test points inside the tested area. The measured RSSI was used to train, test, and validate the performance of BP-ANN to determine the two dimensions (2D) of the target child’s location. Different topologies of ANN have been examined for training, testing, and validation which are 5-5, 10-10, 15-15, and 20-20 neurons in the hidden layer. The findings indicate that the 20-20 ANN topology can achieve higher accuracy than other topologies. Additionally, 20-20 topology localization errors were 1.0, 1.157, and 1.356 m for training, testing, and validating ANN performance. https://journal.mtu.edu.iq/index.php/MTU/article/view/838Indoor EnvironmentNeural NetworkRSSITrackingZigBee |
| spellingShingle | Nadia Ahmed Sadik Kamel Gharghan Ammar Hussein Mutlag M. G. M. Abdolrasol Children Tracking System Based on ZigBee Wireless Network and Neural Network Journal of Techniques Indoor Environment Neural Network RSSI Tracking ZigBee |
| title | Children Tracking System Based on ZigBee Wireless Network and Neural Network |
| title_full | Children Tracking System Based on ZigBee Wireless Network and Neural Network |
| title_fullStr | Children Tracking System Based on ZigBee Wireless Network and Neural Network |
| title_full_unstemmed | Children Tracking System Based on ZigBee Wireless Network and Neural Network |
| title_short | Children Tracking System Based on ZigBee Wireless Network and Neural Network |
| title_sort | children tracking system based on zigbee wireless network and neural network |
| topic | Indoor Environment Neural Network RSSI Tracking ZigBee |
| url | https://journal.mtu.edu.iq/index.php/MTU/article/view/838 |
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