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A new LoRa based positioning algorithm utilizing sequence based deep learning technique

Positioning systems can be utilized both indoors and outdoors, however their precision varies since the environment seems to have a significant influence on localization. There are positioning system for respective environments for example, at outdoor GPS is used whereas for indoor positioning Wi-Fi...

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Main Author: Suseenthiran, Kavetha
Format: Thesis
Language:English
English
Published: 2023
Online Access:http://eprints.utem.edu.my/id/eprint/28326/1/A%20new%20LoRa%20based%20positioning%20algorithm%20utilizing%20sequence%20based%20deep%20learning%20technique.pdf
http://eprints.utem.edu.my/id/eprint/28326/2/A%20new%20LoRa%20based%20positioning%20algorithm%20utilizing%20sequence%20based%20deep%20learning%20technique.pdf
http://eprints.utem.edu.my/id/eprint/28326/
https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=124287
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spelling my.utem.eprints.283262024-12-27T08:51:16Z http://eprints.utem.edu.my/id/eprint/28326/ A new LoRa based positioning algorithm utilizing sequence based deep learning technique Suseenthiran, Kavetha Positioning systems can be utilized both indoors and outdoors, however their precision varies since the environment seems to have a significant influence on localization. There are positioning system for respective environments for example, at outdoor GPS is used whereas for indoor positioning Wi-Fi and BLE are used but there is no positioning system that can be adaptive to different types of environments which leads to huge positioning error. LoRa positioning has good performance in terms of accuracy however the positioning error is high due to the Received Signal Strength Indicator (RSSI) heavy fluctuations and the selection of the parameters depending on different the type of environment. In this research, an adaptive LoRa based Positioning system is developed which consists of LoRa Transmitters and LoRa Receiver. Next, RSSI and Signal-to-Noise Ratio (SNR) that is measured is being classified whether it is LoS or NLoS environment based on the sequence-based Bi-LSTM model. Furthermore, an analysis of classification using different sequence length is done. Then, a new positioning algorithm is developed which incorporates distance estimation, Kalman Filter and trilateration technique according to the classification with different sequence length data and the positioning error is being analysed. It is concluded, having a sequence length of 100 dataset gives 100% accuracy due to the length is shorter, it is faster to be trained. The CDF gives 90% of positioning error less than 2.9m in LoS scenario whereas NLoS scenario is less than 2.41m. Comparing with the traditional trilateration method, the proposed algorithm gives higher positioning accuracy in which the estimated positions are near to the actual position. Proposed method improves the positioning error up to 28.92% for the LoS scenario meanwhile for the NLoS scenario the positioning error is improved by 32.68%. Meanwhile when the user moves from NLoS to LoS environment, the positioning error was improved to 72.16% whereas when it is was from LoS to NLoS environment, the accuracy improved 99.81%. 2023 Thesis NonPeerReviewed text en http://eprints.utem.edu.my/id/eprint/28326/1/A%20new%20LoRa%20based%20positioning%20algorithm%20utilizing%20sequence%20based%20deep%20learning%20technique.pdf text en http://eprints.utem.edu.my/id/eprint/28326/2/A%20new%20LoRa%20based%20positioning%20algorithm%20utilizing%20sequence%20based%20deep%20learning%20technique.pdf Suseenthiran, Kavetha (2023) A new LoRa based positioning algorithm utilizing sequence based deep learning technique. Masters thesis, Universiti Teknikal Malaysia Melaka. https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=124287
institution Universiti Teknikal Malaysia Melaka
building UTEM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknikal Malaysia Melaka
content_source UTEM Institutional Repository
url_provider http://eprints.utem.edu.my/
language English
English
description Positioning systems can be utilized both indoors and outdoors, however their precision varies since the environment seems to have a significant influence on localization. There are positioning system for respective environments for example, at outdoor GPS is used whereas for indoor positioning Wi-Fi and BLE are used but there is no positioning system that can be adaptive to different types of environments which leads to huge positioning error. LoRa positioning has good performance in terms of accuracy however the positioning error is high due to the Received Signal Strength Indicator (RSSI) heavy fluctuations and the selection of the parameters depending on different the type of environment. In this research, an adaptive LoRa based Positioning system is developed which consists of LoRa Transmitters and LoRa Receiver. Next, RSSI and Signal-to-Noise Ratio (SNR) that is measured is being classified whether it is LoS or NLoS environment based on the sequence-based Bi-LSTM model. Furthermore, an analysis of classification using different sequence length is done. Then, a new positioning algorithm is developed which incorporates distance estimation, Kalman Filter and trilateration technique according to the classification with different sequence length data and the positioning error is being analysed. It is concluded, having a sequence length of 100 dataset gives 100% accuracy due to the length is shorter, it is faster to be trained. The CDF gives 90% of positioning error less than 2.9m in LoS scenario whereas NLoS scenario is less than 2.41m. Comparing with the traditional trilateration method, the proposed algorithm gives higher positioning accuracy in which the estimated positions are near to the actual position. Proposed method improves the positioning error up to 28.92% for the LoS scenario meanwhile for the NLoS scenario the positioning error is improved by 32.68%. Meanwhile when the user moves from NLoS to LoS environment, the positioning error was improved to 72.16% whereas when it is was from LoS to NLoS environment, the accuracy improved 99.81%.
format Thesis
author Suseenthiran, Kavetha
spellingShingle Suseenthiran, Kavetha
A new LoRa based positioning algorithm utilizing sequence based deep learning technique
author_facet Suseenthiran, Kavetha
author_sort Suseenthiran, Kavetha
title A new LoRa based positioning algorithm utilizing sequence based deep learning technique
title_short A new LoRa based positioning algorithm utilizing sequence based deep learning technique
title_full A new LoRa based positioning algorithm utilizing sequence based deep learning technique
title_fullStr A new LoRa based positioning algorithm utilizing sequence based deep learning technique
title_full_unstemmed A new LoRa based positioning algorithm utilizing sequence based deep learning technique
title_sort new lora based positioning algorithm utilizing sequence based deep learning technique
publishDate 2023
url http://eprints.utem.edu.my/id/eprint/28326/1/A%20new%20LoRa%20based%20positioning%20algorithm%20utilizing%20sequence%20based%20deep%20learning%20technique.pdf
http://eprints.utem.edu.my/id/eprint/28326/2/A%20new%20LoRa%20based%20positioning%20algorithm%20utilizing%20sequence%20based%20deep%20learning%20technique.pdf
http://eprints.utem.edu.my/id/eprint/28326/
https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=124287
_version_ 1819914744928141312
score 13.223943

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