LoRa-based localization systems for noisy outdoor environment

Lam, Ka-Ho; Cheung, Chun Shun; Lee, Wah-Ching

doi:10.1109/wimob.2017.8115843

Cited by 46 publications

(26 citation statements)

References 15 publications

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“…Islam et al experimentally measured the RSSI signal intensity over the distance in an indoor environment and investigated the relationship between the distribution and distance of RSSI values in non-line-of-sight and line-of-sight conditions using a path loss model [8]. Lam et al studied gateway selection strategies for a LoRa-signal-based path loss model in an outdoor environment [7]. They found that positioning errors rapidly increased because of noise from gateways.…”

Section: Rssi Proximity and Path-loss-model-based Positioningmentioning

confidence: 99%

“…The LoRaWAN RSSI proximity method determines whether a LoRa gateway is located nearby by examining whether a signal can be received from it. The LoRa signal covers a wide area and complies with the log-distance path loss model [7]. The position can be calculated more precisely by measuring the decrease in signal intensity [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

“…The LoRa signal covers a wide area and complies with the log-distance path loss model [7]. The position can be calculated more precisely by measuring the decrease in signal intensity [6][7][8]. However, the LoRaWAN RSSI proximity method has a large error of approximately 1000-2000 m compared to that of the LoRaWAN TDoA method [6]; the error of the LoRaWAN TDoA method is more than 300 m because of the time resolution [9].…”

Section: Introductionmentioning

confidence: 99%

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Low-Power LoRa Signal-Based Outdoor Positioning Using Fingerprint Algorithm

Choi

Chang

Jung

et al. 2018

IJGI

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Positioning is an essential element in most Internet of Things (IoT) applications. Global Positioning System (GPS) chips have high cost and power consumption, making it unsuitable for long-range (LoRa) and low-power IoT devices. Alternatively, low-power wide-area (LPWA) signals can be used for simultaneous positioning and communication. We summarize previous studies related to LoRa signal-based positioning systems, including those addressing proximity, a path loss model, time difference of arrival (TDoA), and fingerprint positioning methods. We propose a LoRa signal-based positioning method that uses a fingerprint algorithm instead of a received signal strength indicator (RSSI) proximity or TDoA method. The main objective of this study was to evaluate the accuracy and usability of the fingerprint algorithm for large areas in the real world. We estimated the locations using probabilistic means based on three different algorithms that use interpolated fingerprint RSSI maps. The average accuracy of the three proposed algorithms in our experiments was 28.8 m. Our method also reduced the battery consumption significantly compared with that of existing GPS-based positioning methods.

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Section: Rssi Proximity and Path-loss-model-based Positioningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Low-Power LoRa Signal-Based Outdoor Positioning Using Fingerprint Algorithm

Choi

Chang

Jung

et al. 2018

IJGI

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“…Among the different systems for communications in low power wide area networks (WANs), LoRa is gaining attention. LoRa is a patented technology based on chirp spread spectrum (CSS) modulation, which is able to achieve long distances with limited energy consumption.…”

Section: Introductionmentioning

confidence: 99%

“…Preliminary experiments have been carried out to show that LoRa technology can be used to develop localization systems , but extensive experimental data sets are still missing. Some works explored LoRa communication for time of arrival (ToA), time difference of arrival (TDOA) or angle of arrival (AoA) based localization solutions. However, AoA‐based methods require an array of antennas, while ToA/TDoA techniques rely on precise clocks, and TDoA also needs accurate synchronization among anchors.…”

Section: Introductionmentioning

confidence: 99%

Experimental data set analysis of RSSI‐based indoor and outdoor localization in LoRa networks

Goldoni

Prando

Vizziello

et al. 2018

Internet Technology Letters

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Positioning capability represents one of the basic features of modern Internet of Things (IoT) applications. Although this objective may be pursued by using Global Navigation Satellite Systems, cheaper and simpler techniques are more suitable for low‐power networks. In this letter, we present a complete experimental data set of received signal strength indicator (RSSI) measurements collected in different indoor and outdoor environments using LoRa radios. Moreover, we apply simple and power efficient localization algorithms on the obtained data set. The main goal of this work is to share both the experimental data set and the preliminary results on localization among the community.

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LoRa RSSI Based Outdoor Localization in an Urban Area Using Random Neural Networks

Ingabire

Larijani

Gibson

2021

Lecture Notes in Networks and Systems

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The concept of the Internet of Things (IoT) has led to the interconnection of a significant number of devices and has impacted several applications in smart cities' development. Localization is widely done using Global Positioning System (GPS). However, with large scale wireless sensor networks, GPS is limited by its high-power consumption and more hardware cost required. An energy-efficient localization system of wireless sensor nodes, especially in outdoor urban environments, is a research challenge with limited investigation. In this paper, an energyefficient end device localization model based on LoRa Received Signal Strength Indicator (RSSI) is developed using Random Neural Networks (RNN). Various RNN architectures are used to evaluate the proposed model's performance by applying different learning rates on real RSSI LoRa measurements collected in the urban area of Glasgow City. The proposed model is used to predict the 2D cartesian position coordinates with a minimum mean localization error of 0.39 m.

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LoRa-based localization systems for noisy outdoor environment

Cited by 46 publications

References 15 publications

Low-Power LoRa Signal-Based Outdoor Positioning Using Fingerprint Algorithm

Low-Power LoRa Signal-Based Outdoor Positioning Using Fingerprint Algorithm

Experimental data set analysis of RSSI‐based indoor and outdoor localization in LoRa networks

LoRa RSSI Based Outdoor Localization in an Urban Area Using Random Neural Networks

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