scholarly journals A Simplified and High Accuracy Algorithm of RSSI-Based Localization Zoning for Children Tracking In-Out the School Buses Using Bluetooth Low Energy Beacon

Informatics ◽  
2021 ◽  
Vol 8 (4) ◽  
pp. 65
Author(s):  
Siraporn Sakphrom ◽  
Korakot Suwannarat ◽  
Rina Haiges ◽  
Krit Funsian
Keyword(s):  
Tracking System ◽  
Signal Strength ◽  
High Accuracy ◽  
Low Energy ◽  
Lms Algorithm ◽  
Least Mean Square ◽  
School Bus ◽  
Bluetooth Low Energy ◽  
School Buses ◽  
Bus Service

To avoid problems related to a school bus service such as kidnapping, children being left in a bus for hours leading to fatality, etc., it is important to have a reliable transportation service to ensure students’ safety along journeys. This research presents a high accuracy child monitoring system for locating students if they are inside or outside a school bus using the Internet of Things (IoT) via Bluetooth Low Energy (BLE) which is suitable for a signal strength indication (RSSI) algorithm. The in/out-bus child tracking system alerts a driver to determine if there is a child left on the bus or not. Distance between devices is analyzed for decision making to affiliate the zone of the current children’s position. A simplified and high accuracy machine learning of least mean square (LMS) algorithm is used in this research with model-based RSSI localization techniques. The distance is calculated with the grid size of 0.5 m × 0.5 m similar in size to an actual seat of a school bus using two zones (inside or outside a school bus). The averaged signal strength is proposed for this research, rather than using the raw value of the signal strength in typical works, providing a robust position-tracking system with high accuracy while maintaining the simplicity of the classical trilateration method leading to precise classification of each student from each zone. The test was performed to validate the effectiveness of the proposed tracking strategy which precisely shows the positions of each student. The proposed method, therefore, can be applied for future autopilot school buses where students’ home locations can be securely stored in the system used for references to transport each student to their homes without a driver.

scholarly journals Indoor Location Tracking System Based on Android Application Using Bluetooth Low Energy Beacons for Ubiquitous Computing Environment

2020 ◽  
pp. 572-576
Author(s):  
Khamla NonAlinsavath ◽  
◽  
Lukito Edi Nugroho ◽  
Widyawan Widyawan ◽  
Kazuhiko Hamamoto
Keyword(s):  
Radio Frequency Identification ◽  
Tracking System ◽  
Signal Strength ◽  
Smartphone Application ◽  
Indoor Positioning ◽  
Low Energy ◽  
Positioning System ◽  
Location Awareness ◽  
Android Application ◽  
Bluetooth Low Energy

Indoor positioning and tracking systems have become enormous issue in location awareness computing due to its improvement of location detection and positioning identification. The locations are normally detected using position technologies such as Global Positioning System, radio frequency identification, Bluetooth Beacon, Wi-Fi fingerprinting, pedometer and so on. This research presents an indoor positioning system based on Bluetooth low energy 4.0 Beacons; we have implemented Bluetooth signal strength for tracking the specific location and detect the movement of user through Android application platform. Bluetooth low energy was addressed to be an experiment technique to set up into the real environment of interior the building. The signal strength of beacons is evaluated and measured the quality of accuracy as well as the improvement of provide raw data from Beacons to the system to get better performance of the direction map and precise distance from current location to desire’s positioning. A smartphone application detects the location-based Bluetooth signal strength accurately and can be achieved the destination by provided direction map and distance perfectly.

A Method of Indoor Localization Based on BLE4.0 Protocol of Using Beacons

2015 ◽  
Vol 740 ◽  
pp. 765-768
Author(s):  
He Shan Bian ◽  
Zhao Hui Li ◽  
Fang Zhao
Keyword(s):  
Kalman Filter ◽  
Power Consumption ◽  
Indoor Localization ◽  
Signal Strength ◽  
Mobile Terminal ◽  
High Accuracy ◽  
Low Energy ◽  
Energy Technology ◽  
Bluetooth Low Energy ◽  
Distance Model

In this paper we discuss our attempt to solve the problem of HAIP(High Accuracy Indoor Position) by using BLE4.0(Bluetooth Low Energy). According to previous research, Wi-Fi Positioning has mainly faced some big challenges. Accuracy is deteriorated by directional handset antennas, which affect the relative AP signal strength; Practical maximum reachable accuracy is 3-10 meters depending on environment; Wi-Fi activities is a big consumption of battery on Mobile Terminal; Now, The Bluetooth Low Energy technology is getting mature. In this paper, we use Bluetooth low energy on iOS device to solve the problem of high accuracy indoor position. In the data-preprocessing step, we use Kalman filter to process the RSSI. In the transition step of RSSI to Distance, we propose a novelty method to adjust the parameters of Log-Distance model dynamically and adaptively according to diagonal beacons’ measurement. We implement our technique and algorithm on iOS device with iOS7.0 SDK. The result shows that error reduced to 0.5m-1.2m range depending on the distance, achieved smaller power consumption.

scholarly journals Position Tracking Techniques Using Multiple Receivers for Anti-Drone Systems

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 35
Author(s):  
Jae-Min Shin ◽  
Yu-Sin Kim ◽  
Tae-Won Ban ◽  
Suna Choi ◽  
Kyu-Min Kang ◽  
...  
Keyword(s):  
Traffic Control ◽  
Tracking System ◽  
Estimation Method ◽  
Signal Strength ◽  
Low Energy ◽  
Position Tracking ◽  
Memory Process ◽  
Multiple Receivers ◽  
Received Signal Strength Indication ◽  
Control Management

The need for drone traffic control management has emerged as the demand for drones increased. Particularly, in order to control unauthorized drones, the systems to detect and track drones have to be developed. In this paper, we propose the drone position tracking system using multiple Bluetooth low energy (BLE) receivers. The proposed system first estimates the target’s location, which consists of the distance and angle, while using the received signal strength indication (RSSI) signals at four BLE receivers and gradually tracks the target based on the estimated distance and angle. We propose two tracking algorithms, depending on the estimation method and also apply the memory process, improving the tracking performance by using stored previous movement information. We evaluate the proposed system’s performance in terms of the average number of movements that are required to track and the tracking success rate.

scholarly journals Multi-Slot BLE Raw Database for Accurate Positioning in Mixed Indoor/Outdoor Environments

Data ◽  
2020 ◽  
Vol 5 (3) ◽  
pp. 67 ◽  
Author(s):  
Fernando J. Aranda ◽  
Felipe Parralejo ◽  
Fernando J. Álvarez ◽  
Joaquín Torres-Sospedra
Keyword(s):  
Signal Strength ◽  
Received Signal Strength ◽  
Low Energy ◽  
Location Based Services ◽  
Positioning System ◽  
Positioning Accuracy ◽  
Bluetooth Low Energy ◽  
Outdoor Environments ◽  
The University

The technologies and sensors embedded in smartphones have contributed to the spread of disruptive applications built on top of Location Based Services (LBSs). Among them, Bluetooth Low Energy (BLE) has been widely adopted for proximity and localization, as it is a simple but efficient positioning technology. This article presents a database of received signal strength measurements (RSSIs) on BLE signals in a real positioning system. The system was deployed on two buildings belonging to the campus of the University of Extremadura in Badajoz. the database is divided into three different deployments, changing in each of them the number of measurement points and the configuration of the BLE beacons. the beacons used in this work can broadcast up to six emission slots simultaneously. Fingerprinting positioning experiments are presented in this work using multiple slots, improving positioning accuracy when compared with the traditional single slot approach.

scholarly journals In-vehicle localization based on multi-channel Bluetooth Low Energy received signal strength indicator

2020 ◽  
Vol 16 (1) ◽  
pp. 155014771990009
Author(s):  
Gao Yuan ◽  
Zhao Ze ◽  
Huang Changcheng ◽  
Han Chuanqi ◽  
Cui Li
Keyword(s):  
Standard Deviation ◽  
High Precision ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Low Energy ◽  
Support Vector ◽  
Time Sharing ◽  
Received Signal Strength Indicator ◽  
Bluetooth Low Energy ◽  
Vehicle Localization

High-precision in-vehicle localization is the basis for both in-vehicle location-based service and the analysis of the driver or passengers’ behaviors. However, interferences like effects of multipath and reflection of the signals significantly raise great challenges to the positioning accuracy at in-vehicle environment. This article presents a novel high-precision in-vehicle localization method, namely, the LOC-in-a-Car, based on functional exploration and full use of multi-channel received signal strength indicator of Bluetooth Low Energy. To achieve higher positioning precision, a hierarchical computation algorithm based on Adaboost and support vector machine is proposed in our method. In particular, we also proposed a device calibration method to deal with the heterogeneity of different smartphone terminals. We developed an Android app as a component in which the channel time-sharing acquisition method is fulfilled, enabling smartphones to distinguish data from multi-channels. The system performance is verified via intensive experiments, of which the results show that our method can distinguish the locations of driver or passengers with an accuracy ranging from 86.80% to 92.02% for each seat on Nexus phone, and the overall accuracy is 89.86%, with standard deviation of 2.64%. On Huawei phone, the accuracy ranges from 85.43% to 93.33% with overall accuracy of 89.75% and standard deviation of 3.07%. Both outperform the existing methods.

scholarly journals Iot based Live Student Tracking System

2020 ◽  
Vol 9 (1) ◽  
pp. 747-749
Keyword(s):  
Mobile Application ◽  
Tracking System ◽  
Low Energy ◽  
Technical Solution ◽  
End User ◽  
Energy Device ◽  
Bluetooth Low Energy ◽  
Exact Location ◽  
Smart Watch ◽  
Teachers And Parents

The IOT based live student tracking system is a mobile application ensuring the safety and security of the students. The main objective of the application is to build a smart watch for a school students.Now a days teachers and parents are worry about children because of the large amount students are bunking their classes.On certain cases,the teachers did not know about where the student is currently available and our proposed system gives a technical solution for the above problem.The system consists of school unit and bus unit which interact with the user.The school unit consists of a Bluetooth Low Energy device which is like a watch,that is used to send information once the student is out of Bluetooth range and also if the watch is removed by the student and the bus unit consists of mobile module where the app is placed.The parents acts like a end user,they can get a student exact location in their mobile phones.With this help of mobile application the parents and teachers can lively track the students location.

scholarly journals Penerapan Kalman Filter Pada Metode Trilaterasi Untuk Peningkatan Akurasi Estimasi Perhitungan Jarak Di Dalam Ruangan

2018 ◽  
Vol 9 (02) ◽  
pp. 96-102
Author(s):  
Fahrudin Wibowo ◽  
Aulia Burhanudin
Keyword(s):  
Kalman Filter ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Low Energy ◽  
Bluetooth Low Energy ◽  
Received Signal Strength Indication

Penelitian tentang posisi maupun jarak suatu obyek di dalam ruangan telah banyak dilakukan. Metode trilaterasi adalah salah satu metode yang dapat dipergunakan untuk menghitung nilai estimasi jarak atau posisi suatu obyek di dalam ruangan, berdasarkan nilai RSSI (Received Signal Strength Indication) yang diterima suatu receiver. Namun, nilai RSSI yang diterima tidak dapat stabil dikarenakan sinyal yang diterima oleh receiver sangat dipengaruhi kondisi lingkungan pada ruangan yang pada umumnya memiliki nilai noise yang cukup tinggi. Sehingga dapat berakibat pada nilai estimasi jarak yang diperoleh menjadi kurang akurat. Sehubungan dengan hal tersebut maka setelah dilakukan perhitungan dengan trilaterasi, dilanjutkan dengan menambahkan metode Kalman Filter untuk meningkatkan nilai akurasi. Penelitian ini menggunakan BLE (Bluetooth Low Energy) sebagai transmitter, sedangkan receiver menggunakan smartphone yang sudah ter-install aplikasi untuk menerima nilai RSSI. Setelah menggunakan Kalman Filter diperoleh peningkatan nilai akurasi sebesar 0, 1 meter dari nilai perhitungan trilaterasi

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