Localization in Low Power Wide Area Networks using Wi-Fi Fingerprints

Janssen, Thomas; Weyn, Maarten; Berkvens, Rafael

doi:10.20944/preprints201707.0034.v1

Cited by 8 publications

(13 citation statements)

References 11 publications

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“…Compared with the reference paper [16], the localization error of Janssen's study [6] is reduced from 689 meters to 340 meters, which is equivalent to double the localization accuracy. It can be seen that the algorithm of machine learning combined with fingerprint can achieve a good localization performance [17].…”

Section: Sigfox-based Localizationmentioning

confidence: 90%

“…Proximity localization involves finding the target's closest gateway and locating the target at the gateway [6]. The localization accuracy of this method depends on the density of the gateway.…”

Section: Localization Techniquesmentioning

confidence: 99%

“…When the distance between categories is far away, the location classification accuracy is as high as 100%, which means that the larger the category gap, the better the segmentation, but in this case, it does not meet the actual application situation. Janssen et al [6] designed a localization method that combines open source Wi-Fi BSSID databases with Sigfox. The researchers carried mobile devices and moved in Antwerp, Belgium, to get the latest Wi-Fi BSSID.…”

Section: Sigfox-based Localizationmentioning

confidence: 99%

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Intelligent LoRa-Based Positioning System

2022

KSII TIIS

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The Location-Based Service (LBS) is one of the most well-known services on the Internet. Positioning is the primary association with LBS services. This study proposes an intelligent LoRa-based positioning system, called AI@LBS, to provide accurate location data. The fingerprint mechanism with the clustering algorithm in unsupervised learning filters out signal noise and improves computing stability and accuracy. In this study, data noise is filtered using the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm, increasing the positioning accuracy from 95.37% to 97.38%. The problem of data imbalance is addressed using the SMOTE (Synthetic Minority Over-sampling Technique) technique, increasing the positioning accuracy from 97.38% to 99.17%. A field test in the NTUST campus (www.ntust.edu.tw) revealed that AI@LBS system can reduce average distance error to 0.48m.

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Section: Sigfox-based Localizationmentioning

confidence: 90%

Section: Localization Techniquesmentioning

confidence: 99%

Section: Sigfox-based Localizationmentioning

confidence: 99%

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Intelligent LoRa-Based Positioning System

2022

KSII TIIS

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“…Furthermore, limited studies are available for localization analysis in outdoor environments, mainly due to the hard work involved in collecting data. WiFi has been used widely in fingerprint localization [9], [10], [11], and [12] whereby a smartphone can be used to record its RSSI and estimate its location using the web. The authors make a comparative performance analysis of different wireless technologies based on RSSI localization in [13].…”

Section: Overview Of Rssi Fingerprint Localization Methodsmentioning

confidence: 99%

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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“…Although mobile anchors introduce swift and flexible localization solutions, they typically serve limited areas [34]. A method that uses information about nearby Wi-Fi access points to localize Sigfox nodes is proposed in [35]. This approach achieved location estimation with a median error of 39 m. However, it requires dedicated Sigfox messages, leading to inefficient use of the limited Sigfox transmissions.…”

Section: A Related Workmentioning

confidence: 99%

Localization in Ultra Narrow Band IoT Networks: Design Guidelines and Tradeoffs

Sallouha

Chiumento

Rajendran

et al. 2019

IEEE Internet Things J.

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Localization in long-range Internet of Things networks is a challenging task, mainly due to the long distances and low bandwidth used. Moreover, the cost, power, and size limitations restrict the integration of a GPS receiver in each device. In this work, we introduce a novel received signal strength indicator (RSSI) based localization solution for ultra narrow band (UNB) long-range IoT networks such as Sigfox. The essence of our approach is to leverage the existence of a few GPSenabled sensors (GSNs) in the network to split the wide coverage into classes, enabling RSSI based fingerprinting of other sensors (SNs). By using machine learning algorithms at the network backed-end, the proposed approach does not impose extra power, payload, or hardware requirements. To comprehensively validate the performance of the proposed method, a measurement-based dataset that has been collected in the city of Antwerp is used. We show that a location classification accuracy of 80% is achieved by virtually splitting a city with a radius of 2.5 km into seven classes. Moreover, separating classes, by increasing the spacing between them, brings the classification accuracy up-to 92% based on our measurements. Furthermore, when the density of GSN nodes is high enough to enable device-to-device communication, using multilateration, we improve the probability of localizing SNs with an error lower than 20 m by 40% in our measurement scenario.

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Localization in Low Power Wide Area Networks using Wi-Fi Fingerprints

Cited by 8 publications

References 11 publications

Intelligent LoRa-Based Positioning System

Intelligent LoRa-Based Positioning System

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

Localization in Ultra Narrow Band IoT Networks: Design Guidelines and Tradeoffs

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