9,931 research outputs found
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
Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey
Growing progress in sensor technology has constantly expanded the number and
range of low-cost, small, and portable sensors on the market, increasing the
number and type of physical phenomena that can be measured with wirelessly
connected sensors. Large-scale deployments of wireless sensor networks (WSN)
involving hundreds or thousands of devices and limited budgets often constrain
the choice of sensing hardware, which generally has reduced accuracy,
precision, and reliability. Therefore, it is challenging to achieve good data
quality and maintain error-free measurements during the whole system lifetime.
Self-calibration or recalibration in ad hoc sensor networks to preserve data
quality is essential, yet challenging, for several reasons, such as the
existence of random noise and the absence of suitable general models.
Calibration performed in the field, without accurate and controlled
instrumentation, is said to be in an uncontrolled environment. This paper
provides current and fundamental self-calibration approaches and models for
wireless sensor networks in uncontrolled environments
Emitter Location Finding using Particle Swarm Optimization
Using several spatially separated receivers, nowadays positioning techniques, which are implemented to determine the location of the transmitter, are often required for several important disciplines such as military, security, medical, and commercial applications. In this study, localization is carried out by particle swarm optimization using time difference of arrival. In order to increase the positioning accuracy, time difference of arrival averaging based two new methods are proposed. Results are compared with classical algorithms and Cramer-Rao lower bound which is the theoretical limit of the estimation error
Multibaseline gravitational wave radiometry
We present a statistic for the detection of stochastic gravitational wave
backgrounds (SGWBs) using radiometry with a network of multiple baselines. We
also quantitatively compare the sensitivities of existing baselines and their
network to SGWBs. We assess how the measurement accuracy of signal parameters,
e.g., the sky position of a localized source, can improve when using a network
of baselines, as compared to any of the single participating baselines. The
search statistic itself is derived from the likelihood ratio of the cross
correlation of the data across all possible baselines in a detector network and
is optimal in Gaussian noise. Specifically, it is the likelihood ratio
maximized over the strength of the SGWB, and is called the maximized-likelihood
ratio (MLR). One of the main advantages of using the MLR over past search
strategies for inferring the presence or absence of a signal is that the former
does not require the deconvolution of the cross correlation statistic.
Therefore, it does not suffer from errors inherent to the deconvolution
procedure and is especially useful for detecting weak sources. In the limit of
a single baseline, it reduces to the detection statistic studied by Ballmer
[Class. Quant. Grav. 23, S179 (2006)] and Mitra et al. [Phys. Rev. D 77, 042002
(2008)]. Unlike past studies, here the MLR statistic enables us to compare
quantitatively the performances of a variety of baselines searching for a SGWB
signal in (simulated) data. Although we use simulated noise and SGWB signals
for making these comparisons, our method can be straightforwardly applied on
real data.Comment: 17 pages and 19 figure
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
Building Information Modelling : Indoor Localization
This thesis presents an integrated system where BIM software is used together with IoT devices to visualize data generated in real-time. Two different IoT devices are modelled as case study that collect environmental and localization data. These devices were installed inside a Test room of an area approx. 22 m2 in UiT Narvik premises . The collected data were, filtered & transferred to database server which were then retrieved and visualized by BIM software in real time. The report presents tools and technologies that are implemented to develop such system and provides details on basic blocks required for such integrations. The combined platform visualize information about the things as it happens in real-time. This makes such systems capable for digitalization of physical process and have various application domains. In the report it is applied as monitoring platform for temperature and illumination data and can be used for facility management applications. Similarly, indoor localization is monitored making it applicable for localization and safety management purpose. The performance of the system is also discussed based on test, observations, and calculation
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