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

Janssen, Thomas; Weyn, Maarten; Berkvens, Rafael

doi:10.3390/app7090936

Cited by 13 publications

(4 citation statements)

References 18 publications

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“…This approach would demand many GPS nodes, which impedes scalability. Lastly, Janssen et al implemented a WiFi fingerprinting method in a large urban area [15]. A mobile device was carried around in the city of Antwerp, Belgium while sniffing for WiFi BSSIDs.…”

Section: Low Power Wide Area Networkmentioning

confidence: 99%

Sigfox and LoRaWAN Datasets for Fingerprint Localization in Large Urban and Rural Areas

Aernouts

Berkvens

Vlaenderen³

et al. 2018

Data

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Because of the increasing relevance of the Internet of Things and location-based services, researchers are evaluating wireless positioning techniques, such as fingerprinting, on Low Power Wide Area Network (LPWAN) communication. In order to evaluate fingerprinting in large outdoor environments, extensive, time-consuming measurement campaigns need to be conducted to create useful datasets. This paper presents three LPWAN datasets which are collected in large-scale urban and rural areas. The goal is to provide the research community with a tool to evaluate fingerprinting algorithms in large outdoor environments. During a period of three months, numerous mobile devices periodically obtained location data via a GPS receiver which was transmitted via a Sigfox or LoRaWAN message. Together with network information, this location data is stored in the appropriate LPWAN dataset. The first results of our basic fingerprinting implementation, which is also clarified in this paper, indicate a mean location estimation error of 214.58 m for the rural Sigfox dataset, 688.97 m for the urban Sigfox dataset and 398.40 m for the urban LoRaWAN dataset. In the future, we will enlarge our current datasets and use them to evaluate and optimize our fingerprinting methods. Also, we intend to collect additional datasets for Sigfox, LoRaWAN and NB-IoT.

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Section: Low Power Wide Area Networkmentioning

confidence: 99%

Sigfox and LoRaWAN Datasets for Fingerprint Localization in Large Urban and Rural Areas

Aernouts

Berkvens

Vlaenderen³

et al. 2018

Data

Self Cite

127

119

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“…However, their localization capability has not been fully developed. Many of the existing IoT systems still rely on location solutions from the existing localization technologies such as GNSS [17] and Wireless Fidelity (WiFi) [18]. There are two reasons for this phenomenon:…”

Section: A Localization Technologies and Applicationsmentioning

confidence: 99%

Toward Location-Enabled IoT (LE-IoT): IoT Positioning Techniques, Error Sources, and Error Mitigation

Zhuang

et al. 2021

IEEE Internet Things J.

128

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Localization techniques are becoming key to add location context to Internet of Things (IoT) data without human perception and intervention. Meanwhile, the newly-emerged Low-Power Wide-Area Network (LPWAN) and 5G technologies have become strong candidates for mass-market localization applications. However, various error sources have limited localization performance by using such IoT signals. This paper reviews the IoT localization system through the following sequence: IoT localization system review -localization data sourceslocalization algorithms -localization error sources and mitigation -localization performance evaluation. Compared to the related surveys, this paper has a more comprehensive and state-of-theart review on IoT localization methods, an original review on IoT localization error sources and mitigation, an original review on IoT localization performance evaluation, and a more comprehensive review of IoT localization applications, opportunities, and challenges. Thus, this survey provides comprehensive guidance for peers who are interested in enabling localization ability in the existing IoT systems, using IoT systems for localization, or integrating IoT signals with the existing localization sensors.

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“…Different algorithms using RSSI fingerprint localization are available in the literature using various technologies; nevertheless, most of the present works investigated indoor scenarios because of many data needed for the training phase and tedious work in accumulating enough data for a large area. Wi-Fi has been used in fingerprint localization by different researchers [18][19][20][21], whereby a smartphone may be used to record its RSSI and calculate its location using the web. The authors in [22] compared the performance analyses of different wireless technologies based on RSSI localization.…”

Section: Related Workmentioning

confidence: 99%

Outdoor Node Localization Using Random Neural Networks for Large-Scale Urban IoT LoRa Networks

et al. 2021

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Accurate localization for wireless sensor end devices is critical, particularly for Internet of Things (IoT) location-based applications such as remote healthcare, where there is a need for quick response to emergency or maintenance services. Global Positioning Systems (GPS) are widely known for outdoor localization services; however, high-power consumption and hardware cost become a significant hindrance to dense wireless sensor networks in large-scale urban areas. Therefore, wireless technologies such as Long-Range Wide-Area Networks (LoRaWAN) are being investigated in different location-aware IoT applications due to having more advantages with low-cost, long-range, and low-power characteristics. Furthermore, various localization methods, including fingerprint localization techniques, are present in the literature but with different limitations. This study uses LoRaWAN Received Signal Strength Indicator (RSSI) values to predict the unknown X and Y position coordinates on a publicly available LoRaWAN dataset for Antwerp in Belgium using Random Neural Networks (RNN). The proposed localization system achieves an improved high-level accuracy for outdoor dense urban areas and outperforms the present conventional LoRa-based localization systems in other work, with a minimum mean localization error of 0.29 m.

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

Cited by 13 publications

References 18 publications

Sigfox and LoRaWAN Datasets for Fingerprint Localization in Large Urban and Rural Areas

Sigfox and LoRaWAN Datasets for Fingerprint Localization in Large Urban and Rural Areas

Toward Location-Enabled IoT (LE-IoT): IoT Positioning Techniques, Error Sources, and Error Mitigation

Outdoor Node Localization Using Random Neural Networks for Large-Scale Urban IoT LoRa Networks

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