An improved three-point localization method based on RSS for transceiver separation RFID systems

Zhu, Cui; Shu, Zhao; Xia, Yuanqing; Li, Li

doi:10.1016/j.measurement.2021.110283

Cited by 7 publications

(2 citation statements)

References 28 publications

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“…This approach is less accurate than a range-based method. On the other hand, range-based techniques are more precise and effective than range-free localization methods [13]. The angles and distances between nodes in Wireless Sensor Network (WSN) are calculated using Time of Arrival (ToA) range-based algorithms [14], Angle of Arrival (AoA) [15], Time Difference of Arrival (TDoA) [16], acoustic energy [17], and RSSI [18].…”

Section: Introductionmentioning

confidence: 99%

Children Tracking System Based on ZigBee Wireless Network and Neural Network

Ahmed

Gharghan

Mutlag

et al. 2023

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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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Section: Introductionmentioning

confidence: 99%

Children Tracking System Based on ZigBee Wireless Network and Neural Network

Ahmed

Gharghan

Mutlag

et al. 2023

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“…Studying the moving track and staying time of the target at the key points in the protection area in the security system plays an important role in mining the behavior characteristics of the target and evaluating its threat degree. e moving track of the target in the protection area can be obtained through RFID positioning technology [2,3]. It is helpful to analyze the target behavior pattern by analyzing the target trajectory and mining the abnormal trajectory [4,5].…”

Section: Introductionmentioning

confidence: 99%

Semantic Trajectory Frequent Pattern Mining Method with Fuzzy Stay Time Constraint

Gao

et al. 2022

Scientific Programming

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In the security system, transforming a large number of collected target trajectories into semantic trajectories with a less volume and high quality and mining their frequent patterns are helpful to analyze the target behavior patterns, identify hazard sources, and enhance the internal prevention, and control of the security system. Aiming at the limitation of semantic trace frequent pattern mining method defined by precise stay time in practical application scenarios, a fuzzy semantic trace frequent pattern mining method is proposed. Firstly, the membership function of fuzzy stay time is defined, so the stay time of the target at the stay point is fuzzified, and the fuzzy semantic trajectory is obtained. Then, a fuzzy semantic trajectory frequent pattern mining algorithm FST-FPM (fuzzy semantic trajectory frequent pattern mining) is proposed. The FST-FPM algorithm is experimentally verified on the Geolife public dataset and the self-collected RFID positioning dataset. The experimental results show that FST-FPM algorithm can mine frequent patterns of fuzzy semantic trajectories on Geolife dataset and RFID positioning dataset, and the running time is reduced by more than 10% compared with classical PrefixSpan algorithm, PrefixSpan-x algorithm, and LFFT2 algorithm.

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eDeepRFID-IPS: Enhanced RFID Indoor Positioning with Deep Learning for Internet of Things

Alsinglawi

Rabie

2023

Lecture Notes in Networks and Systems

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An improved three-point localization method based on RSS for transceiver separation RFID systems

Cited by 7 publications

References 28 publications

Children Tracking System Based on ZigBee Wireless Network and Neural Network

Children Tracking System Based on ZigBee Wireless Network and Neural Network

Semantic Trajectory Frequent Pattern Mining Method with Fuzzy Stay Time Constraint

eDeepRFID-IPS: Enhanced RFID Indoor Positioning with Deep Learning for Internet of Things

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