scispace - formally typeset
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.

read more

Citations
More filters
Journal ArticleDOI

Hybrid Learning for Mobile Ad-Hoc Distancing/Positioning Using Bluetooth Low Energy

TL;DR: In this article , a hybrid learning method was proposed to support mobile ad-hoc distancing (MAD)/positioning (MAP) using BLE-enabled smartphones, which combines unsupervised learning, supervised learning, and genetic algorithms (GAs) for enhancing distance estimation accuracy.
Proceedings ArticleDOI

To the Green from the Bl(u)e: An innovative system for monitoring urban green areas

TL;DR: In this article , a prototypal sensor network has been realized making use of devices that are normally used in industry 4.0 realizations, which may help in obtaining a precise snapshot of the conditions of urban green areas.

Embedded Machine Learning for 3D Indoor Visible Light Positioning via Optimized Fingerprinting

TL;DR: In this article , a low power node featuring 3D indoor localization via Visible Light Positioning (VLP) and embedded Machine Learning (ML) is presented, where coordinates estimation is performed by a low-complexity shallow Neural Network (NN) running on board of a microcontroller and which approximates the regression model linking received light intensities and position in the workspace.
Posted Content

Indoor Distance Estimation using LSTMs over WLAN Network

TL;DR: This work presents the detailed design and implementation of a self-adaptive WiFi-based indoor distance estimation system using LSTMs, and shows that the LSTM based model performs better than other methods reported in the literature by a significant margin.
Proceedings ArticleDOI

LoRaWAN Parameters Optimization For Efficient Communication

TL;DR: In this article , different combinations of parameters, such as gain, spreading factor and coding rate are used to measure signal strength and signal to noise ratio at various distances between the transmitter and receiver.
References
More filters
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.
Journal ArticleDOI

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.
Related Papers (5)