9,763 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
Distributed and adaptive location identification system for mobile devices
Indoor location identification and navigation need to be as simple, seamless,
and ubiquitous as its outdoor GPS-based counterpart is. It would be of great
convenience to the mobile user to be able to continue navigating seamlessly as
he or she moves from a GPS-clear outdoor environment into an indoor environment
or a GPS-obstructed outdoor environment such as a tunnel or forest. Existing
infrastructure-based indoor localization systems lack such capability, on top
of potentially facing several critical technical challenges such as increased
cost of installation, centralization, lack of reliability, poor localization
accuracy, poor adaptation to the dynamics of the surrounding environment,
latency, system-level and computational complexities, repetitive
labor-intensive parameter tuning, and user privacy. To this end, this paper
presents a novel mechanism with the potential to overcome most (if not all) of
the abovementioned challenges. The proposed mechanism is simple, distributed,
adaptive, collaborative, and cost-effective. Based on the proposed algorithm, a
mobile blind device can potentially utilize, as GPS-like reference nodes,
either in-range location-aware compatible mobile devices or preinstalled
low-cost infrastructure-less location-aware beacon nodes. The proposed approach
is model-based and calibration-free that uses the received signal strength to
periodically and collaboratively measure and update the radio frequency
characteristics of the operating environment to estimate the distances to the
reference nodes. Trilateration is then used by the blind device to identify its
own location, similar to that used in the GPS-based system. Simulation and
empirical testing ascertained that the proposed approach can potentially be the
core of future indoor and GPS-obstructed environments
Efficient AoA-based wireless indoor localization for hospital outpatients using mobile devices
The motivation of this work is to help outpatients find their corresponding departments or clinics, thus, it needs to provide indoor positioning services with a room-level accuracy. Unlike wireless outdoor localization that is dominated by the global positioning system (GPS), wireless indoor localization is still an open issue. Many different schemes are being developed to meet the increasing demand for indoor localization services. In this paper, we investigated the AoA-based wireless indoor localization for outpatients’ wayfinding in a hospital, where Wi-Fi access points (APs) are deployed, in line, on the ceiling. The target position can be determined by a mobile device, like a smartphone, through an efficient geometric calculation with two known APs coordinates and the angles of the incident radios. All possible positions in which the target may appear have been comprehensively investigated, and the corresponding solutions were proven to be the same. Experimental results show that localization error was less than 2.5 m, about 80% of the time, which can satisfy the outpatients’ requirements for wayfinding
Position Estimation of Robotic Mobile Nodes in Wireless Testbed using GENI
We present a low complexity experimental RF-based indoor localization system
based on the collection and processing of WiFi RSSI signals and processing
using a RSS-based multi-lateration algorithm to determine a robotic mobile
node's location. We use a real indoor wireless testbed called w-iLab.t that is
deployed in Zwijnaarde, Ghent, Belgium. One of the unique attributes of this
testbed is that it provides tools and interfaces using Global Environment for
Network Innovations (GENI) project to easily create reproducible wireless
network experiments in a controlled environment. We provide a low complexity
algorithm to estimate the location of the mobile robots in the indoor
environment. In addition, we provide a comparison between some of our collected
measurements with their corresponding location estimation and the actual robot
location. The comparison shows an accuracy between 0.65 and 5 meters.Comment: (c) 2016 IEEE. Personal use of this material is permitted. Permission
from IEEE must be obtained for all other uses, in any current or future
media, including reprinting/republishing this material for advertising or
promotional purposes, creating new collective works, for resale or
redistribution to servers or lists, or reuse of any copyrighted component of
this work in other work
Hybrid Radio-map for Noise Tolerant Wireless Indoor Localization
In wireless networks, radio-map based locating techniques are commonly used
to cope the complex fading feature of radio signal, in which a radio-map is
built by calibrating received signal strength (RSS) signatures at training
locations in the offline phase. However, in severe hostile environments, such
as in ship cabins where severe shadowing, blocking and multi-path fading
effects are posed by ubiquitous metallic architecture, even radio-map cannot
capture the dynamics of RSS. In this paper, we introduced multiple feature
radio-map location method for severely noisy environments. We proposed to add
low variance signature into radio map. Since the low variance signatures are
generally expensive to obtain, we focus on the scenario when the low variance
signatures are sparse. We studied efficient construction of multi-feature
radio-map in offline phase, and proposed feasible region narrowing down and
particle based algorithm for online tracking. Simulation results show the
remarkably performance improvement in terms of positioning accuracy and
robustness against RSS noises than the traditional radio-map method.Comment: 6 pages, 11th IEEE International Conference on Networking, Sensing
and Control, April 7-9, 2014, Miami, FL, US
Evaluating indoor positioning systems in a shopping mall : the lessons learned from the IPIN 2018 competition
The Indoor Positioning and Indoor Navigation (IPIN) conference holds an annual competition in which indoor localization systems from different research groups worldwide are evaluated empirically. The objective of this competition is to establish a systematic evaluation methodology with rigorous metrics both for real-time (on-site) and post-processing (off-site) situations, in a realistic environment unfamiliar to the prototype developers. For the IPIN 2018 conference, this competition was held on September 22nd, 2018, in Atlantis, a large shopping mall in Nantes (France). Four competition tracks (two on-site and two off-site) were designed. They consisted of several 1 km routes traversing several floors of the mall. Along these paths, 180 points were topographically surveyed with a 10 cm accuracy, to serve as ground truth landmarks, combining theodolite measurements, differential global navigation satellite system (GNSS) and 3D scanner systems. 34 teams effectively competed. The accuracy score corresponds to the third quartile (75th percentile) of an error metric that combines the horizontal positioning error and the floor detection. The best results for the on-site tracks showed an accuracy score of 11.70 m (Track 1) and 5.50 m (Track 2), while the best results for the off-site tracks showed an accuracy score of 0.90 m (Track 3) and 1.30 m (Track 4). These results showed that it is possible to obtain high accuracy indoor positioning solutions in large, realistic environments using wearable light-weight sensors without deploying any beacon. This paper describes the organization work of the tracks, analyzes the methodology used to quantify the results, reviews the lessons learned from the competition and discusses its future
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