219 research outputs found
A Robust Zero-Calibration RF-based Localization System for Realistic Environments
Due to the noisy indoor radio propagation channel, Radio Frequency (RF)-based
location determination systems usually require a tedious calibration phase to
construct an RF fingerprint of the area of interest. This fingerprint varies
with the used mobile device, changes of the transmit power of smart access
points (APs), and dynamic changes in the environment; requiring re-calibration
of the area of interest; which reduces the technology ease of use. In this
paper, we present IncVoronoi: a novel system that can provide zero-calibration
accurate RF-based indoor localization that works in realistic environments. The
basic idea is that the relative relation between the received signal strength
from two APs at a certain location reflects the relative distance from this
location to the respective APs. Building on this, IncVoronoi incrementally
reduces the user ambiguity region based on refining the Voronoi tessellation of
the area of interest. IncVoronoi also includes a number of modules to
efficiently run in realtime as well as to handle practical deployment issues
including the noisy wireless environment, obstacles in the environment,
heterogeneous devices hardware, and smart APs. We have deployed IncVoronoi on
different Android phones using the iBeacons technology in a university campus.
Evaluation of IncVoronoi with a side-by-side comparison with traditional
fingerprinting techniques shows that it can achieve a consistent median
accuracy of 2.8m under different scenarios with a low beacon density of one
beacon every 44m2. Compared to fingerprinting techniques, whose accuracy
degrades by at least 156%, this accuracy comes with no training overhead and is
robust to the different user devices, different transmit powers, and over
temporal changes in the environment. This highlights the promise of IncVoronoi
as a next generation indoor localization system.Comment: 9 pages, 13 figures, published in SECON 201
Identifying High-Traffic Patterns in the Workplace With Radio Tomographic Imaging in 3D Wireless Sensor Networks
The rapid progress of wireless communication and embedded mircro-sensing electro-mechanical systems (MEMS) technologies has resulted in a growing confidence in the use of wireless sensor networks (WSNs) comprised of low-cost, low-power devices performing various monitoring tasks. Radio Tomographic Imaging (RTI) is a technology for localizing, tracking, and imaging device-free objects in a WSN using the change in received signal strength (RSS) of the radio links the object is obstructing. This thesis employs an experimental indoor three-dimensional (3-D) RTI network constructed of 80 wireless radios in a 100 square foot area. Experimental results are presented from a series of stationary target localization and target tracking experiments using one and two targets. Preliminary results demonstrate a 3-D RTI network can be effectively used to generate 3-D RSS-based images to extract target features such as size and height, and identify high-traffic patterns in the workplace by tracking asset movement
Recent Advances in Indoor Localization Systems and Technologies
Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods
Novel approach to FM-based device free passive indoor localization through neural networks
Indoor Localization has been one of the most extensively researched topics for the past couple of years with a recent surge in a specific area of Device-free localization in wireless environments. Particularly FM-radio based technologies are being been preferred over WiFi-based technologies due to better penetration indoors and free availability. The major challenges for obtaining a consistent and highly accurate indoor FM based system are susceptibility to human presence, multipath fading and environmental changes. Our research works around these limitations and utilizes the environment itself to establish stronger fingerprints and thus creating a robust localization system. This novel thesis also investigates the feasibility of using neural networks to solve the problem of accuracy degradation when using a single passive receiver across multiple ambient FM radio stations. The system achieves high fidelity and temporal stability to the tunes of 95% by utilizing pattern recognition techniques for the multiple channel spectra
- …