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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
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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Multiview Variational Deep Learning With Application to Practical Indoor Localization

TL;DR: A view-selective deep learning system for indoor localization using CSI of WiFi, the first approach to apply variational inference and to construct a practical system for radio localization, and a methodology for supervised learning with multiview data where informative and noninformative views coexist.
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Hybrid Indoor Positioning System for Pedestrians With Swinging Arms Based on Smartphone IMU and RSSI of BLE

TL;DR: In this paper, a hybrid indoor positioning algorithm based on IMU and RSSI is proposed for pedestrians with swinging arms, which is improved by analyzing the characteristics of walking postures.
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Wi-Fi indoor positioning and navigation: a cloudlet-based cloud computing approach

TL;DR: A cloudlet-based cloud computing system enablingWi-Fi indoor positioning and navigation through a Wi-Fi located on a one-hop wireless network is proposed and tested.
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IoT-Based Smart Home Device Monitor Using Private Blockchain Technology and Localization

TL;DR: This letter proposes a low-end design that reinforces the security of a home network that uses private blockchain technology and localization via RSSI-based trilateration and improves the precision of the localization algorithm by testing it against different wireless technologies.
Journal ArticleDOI

A Multi-View Discriminant Learning Approach for Indoor Localization Using Amplitude and Phase Features of CSI

TL;DR: MuDLoc is proposed, the first multi-view discriminant learning approach for device free indoor localization using both amplitude and phase features of Channel State Information (CSI) from multiple Access Points (APs).
References
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Location Fingerprinting With Bluetooth Low Energy Beacons

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