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A Survey of Indoor Localization Systems and Technologies

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TLDR
A detailed survey of different indoor localization techniques such as Angle of Arrival (AoA), Time of Flight (ToF), return time of flight (RTOF), Received Signal Strength (RSS); based on technologies such as WiFi, Radio Frequency Identification Device (RFID), Ultra Wideband (UWB), Bluetooth and systems that have been proposed in the literature is presented in this article.
Abstract: 
Indoor localization has recently witnessed an increase in interest, due to the potential wide range of services it can provide by leveraging Internet of Things (IoT), and ubiquitous connectivity. Different techniques, wireless technologies and mechanisms have been proposed in the literature to provide indoor localization services in order to improve the services provided to the users. However, there is a lack of an up-to-date survey paper that incorporates some of the recently proposed accurate and reliable localization systems. In this paper, we aim to provide a detailed survey of different indoor localization techniques such as Angle of Arrival (AoA), Time of Flight (ToF), Return Time of Flight (RTOF), Received Signal Strength (RSS); based on technologies such as WiFi, Radio Frequency Identification Device (RFID), Ultra Wideband (UWB), Bluetooth and systems that have been proposed in the literature. The paper primarily discusses localization and positioning of human users and their devices. We highlight the strengths of the existing systems proposed in the literature. In contrast with the existing surveys, we also evaluate different systems from the perspective of energy efficiency, availability, cost, reception range, latency, scalability and tracking accuracy. Rather than comparing the technologies or techniques, we compare the localization systems and summarize their working principle. We also discuss remaining challenges to accurate indoor localization.

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Citations
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Journal ArticleDOI

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

TL;DR: This article examines two memoryless positioning techniques, KNN and Naive Bayes, and compares them with simple trilateration, in terms of accuracy, precision, and complexity, and presents a comprehensive analysis between the techniques through the use of three popular IoT wireless technologies.
Proceedings ArticleDOI

Semi-supervised Variational Autoencoder for WiFi Indoor Localization

TL;DR: This work develops a semi-supervised deep learning method able to train a prediction model from a small set of annotated WiFi observations and a massive set of non-annotated ones based on the variational au-toencoder deep network.
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Accurate indoor positioning using IEEE 802.11mc round trip time

TL;DR: The design and implementation of WiNar is presented, a WiFi RTT-based indoor location determination system that combines the advantages of both fingerprint and ranging-based techniques to overcome the different challenges of indoor environments and is also robust to heterogeneous devices.
Proceedings ArticleDOI

EHAAS: Energy Harvesters As A Sensor for Place Recognition on Wearables

TL;DR: In this article, a wearable based long-term lifelogging system called EHAAS (Energy Harvesters As A Sensor) where energy harvesting elements are used as a sensor is proposed.
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EHAAS: Energy Harvesters As A Sensor for Place Recognition on Wearables

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