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Open AccessJournal ArticleDOI

RSSI-Based Indoor Localization With the Internet of Things

Sebastian Sadowski, +1 more
- 04 Jun 2018 - 
- Vol. 6, pp 30149-30161
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TLDR
Four wireless technologies for indoor localization: Wi-Fi (IEEE 802.11n-2009 at the 2.4 GHz band), Bluetooth low energy, Zigbee, and long-range wide-area network are compared in terms of localization accuracy and power consumption when IoT devices are used.
Abstract
In the era of smart cities, there are a plethora of applications where the localization of indoor environments is important, from monitoring and tracking in smart buildings to proximity marketing and advertising in shopping malls. The success of these applications is based on the development of a cost-efficient and robust real-time system capable of accurately localizing objects. In most outdoor localization systems, global positioning system (GPS) is used due to its ease of implementation and accuracy up to five meters. However, due to the limited space that comes with performing localization of indoor environments and the large number of obstacles found indoors, GPS is not a suitable option. Hence, accurately and efficiently locating objects is a major challenge in indoor environments. Recent advancements in the Internet of Things (IoT) along with novel wireless technologies can alleviate the problem. Small-size and cost-efficient IoT devices which use wireless protocols can provide an attractive solution. In this paper, we compare four wireless technologies for indoor localization: Wi-Fi (IEEE 802.11n-2009 at the 2.4 GHz band), Bluetooth low energy, Zigbee, and long-range wide-area network. These technologies are compared in terms of localization accuracy and power consumption when IoT devices are used. The received signal strength indicator (RSSI) values from each modality were used and trilateration was performed for localization. The RSSI data set is available online. The experimental results can be used as an indicator in the selection of a wireless technology for an indoor localization system following application requirements.

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Citations
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Indoor Localisation with Intelligent Luminaires for Home Monitoring.

TL;DR: It is shown that the presence of walls, furniture and other objects in typical indoor settings precludes accurate localisation, and several software-based approaches are employed, including Kalman filtering and neural networks to improve accuracy.
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Non-Gaussian BLE-Based Indoor Localization Via Gaussian Sum Filtering Coupled with Wasserstein Distance

TL;DR: A Gaussian Sum Filter (GSF) approach is designed to more realistically model the non-Gaussian nature of RSSIs, and the simulation results based on real collected RSSI signals confirm the success of the proposed WD-based GSF framework compared to its conventional counterparts.
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A Physical Layer, Zero-Round-Trip-Time, Multifactor Authentication Protocol

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Virtual fingerprint and two-way ranging-based Bluetooth 3D indoor positioning with RSSI difference and distance ratio

TL;DR: Various positioning algorithms, based on virtual fingerprint and one-way ranging, are discussed, such as NN, KNN, Multi-Step and nonlinear optimization, inference and regression, which hold the best performance in time and error.
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LoRaWAN Based Indoor Localization Using Random Neural Networks

TL;DR: In this paper , a low-power intelligent localization system for indoor environments using LoRaWAN RSSI values with Random Neural Networks (RNN) was proposed, which demonstrates 98.5% improvement in average localization error compared to related studies with a minimum average localization errors of 0.12 m in the Line-of-Sight (LOS) and 13.94 m in Non-Line-ofSight(NLOS) scenarios.
References
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Journal ArticleDOI

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Survey of Wireless Indoor Positioning Techniques and Systems

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Journal ArticleDOI

A Survey of Indoor Localization Systems and Technologies

TL;DR: This paper aims to provide a detailed survey of different indoor localization techniques, such as angle of arrival (AoA), time of flight (ToF), return time ofFlight (RTOF), and received signal strength (RSS) based on technologies that have been proposed in the literature.
Journal ArticleDOI

Location Fingerprinting With Bluetooth Low Energy Beacons

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