Localization in long-range ultra narrow band IoT networks using RSSI

Sallouha, Hazem; Chiumento, Alessandro; Pollin, Sofie

doi:10.1109/icc.2017.7997195

Cited by 61 publications

(49 citation statements)

References 12 publications

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“…The localization problem in long-range UNB IoT networks using RSSI fingerprinting has been addressed in [1,2,36]. In [2], the authors introduced datasets collected in large-scale outdoor environments.…”

Section: A Related Workmentioning

confidence: 99%

“…In fact, this requires either a massive number of GPSenabled nodes to collect tanning data, which is expensive, or alternatively, spend time and effort in building a radio map that is highly vulnerable to become outdated. In [1] a novel measurement based localization approach that leverages the existence of a few GPS nodes (e.g., 1% of the nodes) is presented. Furthermore, enhancement of the localization accuracy using device-to-device short-range communication is introduced.…”

Section: A Related Workmentioning

confidence: 99%

“…The Internet of Things (IoT) is becoming ubiquitous, enabling everyday objects to be equipped with computation and communication capabilities [1]- [5]. To realize IoT, currently deployed networks are using long-range, low power, and low throughput communications such as Sigfox [6], Weightless-P [7], LoRa [8], and narrowband (NB)-IoT [9].…”

Section: Introductionmentioning

confidence: 99%

“…Particularly, by leveraging the advances in machine learning (ML) algorithms, fingerprinting methods have expanded to include outdoor scenarios [1,2].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: A Related Workmentioning

confidence: 99%

Section: A Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Particularly, by leveraging the advances in machine learning (ML) algorithms, fingerprinting methods have expanded to include outdoor scenarios [1,2].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Sallouha

Chiumento

Rajendran

et al. 2019

IEEE Internet Things J.

Self Cite

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“…They also indicate that the CRLB for estimating that range through the received signal strength is proportional to the distance between the mobile unit and the base station; since Sigfox is a wide area network, we can assume generally that the CRLB will be rather high. Sallouha et al [19] demonstrate a classification or fingerprinting solution to Sigfox localization, employing GPS enabled devices to create the fingerprints. They achieve a classification accuracy of 78% in fingerprints with a radius of 150 m, separated by 2.5 times this distance from other fingerprints.…”

Section: Introductionmentioning

confidence: 99%

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

2017

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Featured Application: Localization of a wireless track and trace device communicating over Sigfox and using nearby Wi-Fi access points as references.Abstract: Supply chain management requires regular updates of the location of assets, which can be enabled by low power wide area networks, such as Sigfox. While it is useful to localize a device simply by its communication signals, this is very difficult to do with Sigfox because of wide area and ultra narrowband nature. On the other hand, installing a satellite localization element on the device greatly increases its power consumption. We investigated using information about nearby Wi-Fi access points as a way to localize the asset over the Sigfox network, so without connecting to those Wi-Fi networks. This paper reports the location error that can be achieved by this type of outdoor localization. By using a combination of two databases, we could localize the device on all 36 test locations with a median location error of 39 m. This shows that the localization accuracy of this method is promising enough to warrant further study, most specifically the minimal power consumption.

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Model for autonomous agents in machine‐to‐machine navigation networks

Ali

Shah

Aslam

2017

Int J Communication

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Machine-to-machine (M2M) is an evolving architecture and tends to provide enormous services through the swarm presence of the networked devices. Localization is one of those services. Previous localization techniques require complex computation that is not suitable and affordable in such architecture. Moreover, integrating intelligent multiagents on these ubiquitous devices makes the network more independent and reactive requiring for a less complex localization model. This paper reviews the present localization techniques and discusses their infeasibility for M2M communication while proposing a mathematical model that is derived from Anderson model for the distributed structure of machine-type-communication network involving autonomous agents. This paper has made an attempt to use the property of Anderson model that structures the distributed objects. This paper also classifies autonomous agents according to their functionalities in a navigational network. Recently, Anderson model have been customized for implication of optical communication; in this paper, the proposed mathematical model involves intelligent agents for localization that aim to reduce complexity of positioning computations for nodes having restricted computational resources and battery life, which are the main characteristics of M2M communication.

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Localization in long-range ultra narrow band IoT networks using RSSI

Cited by 61 publications

References 12 publications

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

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

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

Model for autonomous agents in machine‐to‐machine navigation networks

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