Memoryless Techniques and Wireless Technologies for Indoor Localization With the Internet of Things

In recent years, the Internet of Things (IoT) has grown to include the tracking of devices through the use of indoor positioning systems (IPSs) and location-based services (LBSs). When designing an IPS, a popular approach involves using wireless networks to calculate the approximate location of the...

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Bibliographic Details
Published in:IEEE internet of things journal 2020-11, Vol.7 (11), p.10996-11005
Main Authors: Sadowski, Sebastian, Spachos, Petros, Plataniotis, Konstantinos N.
Format: Article
Language:English
Subjects:
<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">K -nearest neighbor (KNN)
Bluetooth
Bluetooth low energy (BLE)
indoor localization
Interference
Internet of Things
IP networks
Localization
Location based services
location-based services (LBSs)
Naive Bayes
Signal strength
smart buildings
Tracking devices
trilateration
WiFi
Wireless communication
Wireless fidelity
Wireless networks
Zigbee
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Summary:In recent years, the Internet of Things (IoT) has grown to include the tracking of devices through the use of indoor positioning systems (IPSs) and location-based services (LBSs). When designing an IPS, a popular approach involves using wireless networks to calculate the approximate location of the target from devices with predetermined positions. In many smart building applications, LBS is necessary for efficient workspaces to be developed. In this article, we examine two memoryless positioning techniques, K -nearest neighbor (KNN) and Naive Bayes, and compare them with simple trilateration, in terms of accuracy, precision, and complexity. We present a comprehensive analysis between the techniques through the use of three popular IoT wireless technologies: 1) ZigBee; 2) Bluetooth low energy (BLE); and 3) WiFi (2.4-GHz band), along with three experimental scenarios to verify results across multiple environments. According to experimental results, KNN is the most accurate localization technique as well as the most precise. The received signal strength indicator data set of all the experiments is available online.
ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2020.2992651