Indoor Positioning System for IoT Device based on BLE Technology and MQTT Protocol

Mekki, Kais; Bajic, Eddy; Meyer, Fernand

doi:10.1109/wf-iot.2019.8767287

Cited by 20 publications

(26 citation statements)

References 8 publications

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“…The three things proposed are frequency diversity, Kalman filtering, and trilateration, this method is called "weighted trilateration". Presenting a system that can track mobile devices and help find their location within building boundaries [32]. With the help of BLE beacons that can be used in different locations.…”

Section: Trilateration Methodsmentioning

confidence: 99%

Indoor Positioning System Using Combination of Trilateration and Fingerprinting Methods

Saputra¹

2020

IJATCSE

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Positioning technology continues to develop. Positioning which is widely used and can work very well outdoors is to use GPS (Global Positioning System). However, GPS cannot run well indoors because of many obstacles in a building. The study was conducted with the topic "indoor positioning system". This research using BLE (Bluetooth Low Energy) technology because it consumes the least energy and can last a very long time. The focus of this research is to use a combination of trilateration and fingerprinting methods to improve accuracy performance. The first stage determines the reference point and forms a radio map. The second stage consists of estimation using the trilateration method. The log distance path loss model/Log-normal shadowing model was used for the propagation model. The evaluation of the proposed method is in a room with a size of 21m x 12 m, 100 sample data for each reference point, and 10 sample data for each test point. Error performance is evaluated using average error, min error, max error, median and 90th percentile. The results of this study if compared with results that only use the trilateration method show that the proposed hybrid method has better accuracy performance and fewer errors rate.

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Section: Trilateration Methodsmentioning

confidence: 99%

Indoor Positioning System Using Combination of Trilateration and Fingerprinting Methods

Saputra¹

2020

IJATCSE

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“…GPS and compass are also widely used to help with positioning. These tools allow us to create a 3D view and model of the world, while the robot is measuring its position in the 3D world with sufficient accuracy [14,15].…”

Section: Technical Background Of Developed Agv Platformmentioning

confidence: 99%

Neural Network Driven Automated Guided Vehicle Platform Development for Industry 4.0 Environment

et al. 2021

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“…Mekki et al, [ 24 ] implemented an Internet of Things (IoT)-based solution. The authors gave some path loss exponents for different environments, such as free space and obstructions in buildings and factories, since in some environments signal strength loss is higher than the others.…”

Section: Related Workmentioning

confidence: 99%

Obstruction-Aware Signal-Loss-Tolerant Indoor Positioning Using Bluetooth Low Energy

Taşkan

Alemdar

2021

Sensors

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Indoor positioning is getting increased attention due to the availability of larger and more sophisticated indoor environments. Wireless technologies like Bluetooth Low Energy (BLE) may provide inexpensive solutions. In this paper, we propose obstruction-aware signal-loss-tolerant indoor positioning (OASLTIP), a cost-effective BLE-based indoor positioning algorithm. OASLTIP uses a combination of techniques together to provide optimum tracking performance by taking into account the obstructions in the environment, and also, it can handle a loss of signal. We use running average filtering to smooth the received signal data, multilateration to find the measured position of the tag, and particle filtering to track the tag for better performance. We also propose an optional receiver placement method and provide the option to use fingerprinting together with OASLTIP. Moreover, we give insights about BLE signal strengths in different conditions to help with understanding the effects of some environmental conditions on BLE signals. We performed extensive experiments for evaluation of the OASLTool we developed. Additionally, we evaluated the performance of the system both in a simulated environment and in real-world conditions. In a highly crowded and occluded office environment, our system achieved 2.29 m average error, with three receivers. When simulated in OASLTool, the same setup yielded an error of 2.58 m.

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Indoor Positioning System for IoT Device based on BLE Technology and MQTT Protocol

Cited by 20 publications

References 8 publications

Indoor Positioning System Using Combination of Trilateration and Fingerprinting Methods

Indoor Positioning System Using Combination of Trilateration and Fingerprinting Methods

Neural Network Driven Automated Guided Vehicle Platform Development for Industry 4.0 Environment

Obstruction-Aware Signal-Loss-Tolerant Indoor Positioning Using Bluetooth Low Energy

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