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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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Journal ArticleDOI

Improved Visible Light-Based Indoor Positioning System Using Machine Learning Classification and Regression

Huy Q. Tran, +1 more
- 13 Mar 2019 - 
TL;DR: The execution time required by certain dual-function algorithms to determine indoor positioning is decreased after area division and noise reduction have been applied and the proposed solution took 78% less time and provided a 52.55% improvement in positioning accuracy.
Journal ArticleDOI

Smart Parking System Based on Bluetooth Low Energy Beacons With Particle Filtering

TL;DR: In this article, a parking system based on Bluetooth Low Energy (BLE) beacons and particle filtering is proposed for both indoor and outdoor parking spaces, which is able to correctly predict which spot the user has parked in, as well as estimate the distance of the user from the beacon.
Journal ArticleDOI

Neural Network-Based Alzheimer’s Patient Localization for Wireless Sensor Network in an Indoor Environment

TL;DR: It is deduced that the proposed ANN method outperformed other techniques in previous studies in terms of mean localization error, and was practically implemented to detect and determine the position of an Alzheimer’s patient in an indoor environment.
Journal ArticleDOI

Evaluating the Implications of Varying Bluetooth Low Energy (BLE) Transmission Power Levels on Wireless Indoor Localization Accuracy and Precision.

TL;DR: The effect of BLE signal variations on indoor localization caused by the change in BLE transmission power levels is investigated and trilateration based localization with MMSE method outperforms the CA method and results in a BLE based WILS with high accuracy and high precision.
Journal ArticleDOI

Random Forest Learning Based Indoor Localization as an IoT Service for Smart Buildings

TL;DR: In this article, a random forest based machine learning algorithm was proposed to improve the location accuracy in indoor localization as an IoT service for smart buildings and the obtained experimental results show 14% better success test prediction percentage in terms of overall deviation.
References
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Journal ArticleDOI

Internet of Things for Smart Cities

TL;DR: This paper will present and discuss the technical solutions and best-practice guidelines adopted in the Padova Smart City project, a proof-of-concept deployment of an IoT island in the city of Padova, Italy, performed in collaboration with the city municipality.
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Survey of Wireless Indoor Positioning Techniques and Systems

TL;DR: Comprehensive performance comparisons including accuracy, precision, complexity, scalability, robustness, and cost are presented.
Journal ArticleDOI

Smart objects as building blocks for the Internet of things

TL;DR: The authors introduce a hierarchy of architectures with increasing levels of real-world awareness and interactivity for smart objects, describing activity-, policy-, and process-aware smart objects and demonstrating how the respective architectural abstractions support increasingly complex application.
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

TL;DR: This work provides a detailed study of BLE fingerprinting using 19 beacons distributed around a ~600 m2 testbed to position a consumer device, and investigates the choice of key parameters in a BLE positioning system, including beacon density, transmit power, and transmit frequency.
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