Probabilistic RSS Fingerprinting for Localization in Smart Platforms

Shit, Rathin Chandra; Sharma, Suraj; Puthal, Deepak; Tripathi, Shankar Sharan

doi:10.1109/icit.2018.00059

Cited by 4 publications

(2 citation statements)

References 26 publications

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“…Therefore, localization for various terrestrial networks is widely studied, which mainly rely on the Global Positioning System (GPS), cellular positioning systems, and indoor positioning systems. For instance, in [16], the authors proposed a probabilistic RSS-based fingerprinting localization for terrestrial IoT networks. Quite a good number of review articles are presented on localization for the terrestrial networks.…”

Section: Related Workmentioning

confidence: 99%

Accurate 3-D Localization of Selected Smart Objects in Optical Internet of Underwater Things

Saeed

Alouini

Al-Naffouri

2020

IEEE Internet Things J.

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Localization is a fundamental task for optical internet of underwater things (O-IoUT) to enable various applications such as data tagging, routing, navigation, and maintaining link connectivity. The accuracy of the localization techniques for O-IoUT greatly relies on the location of the anchors. Therefore, recently localization techniques for O-IoUT which optimize the anchor's location are proposed. However, optimization of anchors location for all the smart objects in the network is not a useful solution. Indeed, in a network of densely populated smart objects, the data collected by some sensors are more valuable than the data collected from other sensors. Therefore, in this paper, we propose a three-dimensional accurate localization technique by optimizing the anchor's location for a set of smart objects. Spectral graph partitioning is used to select the set of valuable sensors. Numerical results show that the proposed technique of optimizing anchor's location for a set of selected sensors provides a better location accuracy.

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Section: Related Workmentioning

confidence: 99%

Accurate 3-D Localization of Selected Smart Objects in Optical Internet of Underwater Things

Saeed

Alouini

Al-Naffouri

2020

IEEE Internet Things J.

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“…The main challenge in the offline phase is to minimize the time and effort required to construct a radio map for the area of interest [11]. To address this issue, the authors in [27] and [29] adopted probabilistic fingerprinting methods based on RSSI distribution information. Experimental results showed that probabilistic approaches provide good performance.…”

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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