1,897 research outputs found
Accuracy and stability analysis of path loss exponent measurement for localization in wireless sensor network
In wireless sensor network localization, path loss model is often used to provide a conversion between distance and received signal strength (RSS). Path loss exponent is one of the main environmental parameters for path loss model to characterize the rate of conversion. Therefore, the accuracy of path loss exponent directly influences the results of RSS-to-distance conversion. When the conversion requires distance estimation from RSS value, small error of measured path loss exponent could lead to large error of the conversion output. To improve the localization results, the approaches of measuring accurate parameters from different environments have become important. Different approaches provide different measurement stabilities, depending on the performance and robustness of the approach. This paper presents four calibration approaches to provide measurements of path loss exponent based on measurement arrangement and transmitter/receiver nodeâs allocation. These include one-line measurement, online-update spread locations measurement, online-update small-to big rectangular measurement, and online-update big-to-small rectangular measurement. The first two are general approaches, and the last two are our newly proposed approaches. Based on our research experiments, a comparison is presented among the four approaches in terms of accuracy and stability. The results show that both online-update rectangular measurements have better stability of measurements. For accuracy of measurement, online-update big-to-small rectangular measurement provides the best result after convergence
Measurement arrangement for the estimation of path loss exponent in wireless sensor network
Path loss model is generally used to relate distance and signal strength in wireless applications. This has been widely implemented in ranging, localization, and location tracking systems. A range of extension models have been proposed to enhance the performance for various environments and applications. Nevertheless, path loss exponent remains its significance as the main factor in the model regardless of how the model is varied. Based on the nature as an exponent of the model, inaccurate path loss exponent amplifies the error if it is used to estimate distance from received signal strength. Therefore, measurement of accurate value for path loss exponent becomes
very important as it directly influences the output of distance estimation. Researchers have been studying the methods of measuring accurate path loss exponent in various environments. Instead of emphasizing the calculation process, this paper focuses more on the allocation of transmitters and receivers, and the arrangement among them. From the results obtained from experiments, properly arranged transmitter and receiver nodes provides better estimation of the path loss exponent. Based on the results, this paper also proposes a suitable nodes arrangement
scheme for path loss exponent estimation
Design and realization of precise indoor localization mechanism for Wi-Fi devices
Despite the abundant literature in the field, there is still the need to find a time-efficient, highly accurate, easy to deploy and robust localization algorithm for real use. The algorithm only involves minimal human intervention. We propose an enhanced Received Signal Strength Indicator (RSSI) based positioning algorithm for Wi-Fi capable devices, called the Dynamic Weighted Evolution for Location Tracking (DWELT). Due to the multiple phenomena affecting the propagation of radio signals, RSSI measurements show fluctuations that hinder the utilization of straightforward positioning mechanisms from widely known propagation loss models. Instead, DWELT uses data processing of raw RSSI values and applies a weighted posterior-probabilistic evolution for quick convergence of localization and tracking. In this paper, we present the first implementation of DWELT, intended for 1D location (applicable to tunnels or corridors), and the first step towards a more generic implementation. Simulations and experiments show an accuracy of 1m in more than 81% of the cases, and less than 2m in the 95%.Peer ReviewedPostprint (published version
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
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Experimental study on RSS based indoor positioning algorithms
This work compares the performance of indoor positioning systems suitable for
low power wireless sensor networks. The research goal is to study positioning
techniques that are compatible with real-time positioning in wireless sensor
networks, having low-power and low complexity as requirements. Map matching,
approximate positioning (weighted centroid) and exact positioning algorithms
(least squares) were tested and compared in a small predefined indoor
environment. We found that, for our test scenario, weighted centroid algorithms
provide better results than map matching. Least squares proved to be completely
unreliable when using distances obtained by the one-slope propagation model.
Major improvements in the positioning error were found when body influence
was removed from the test scenario. The results show that the positioning error
can be improved if the body effect in received signal strength is accounted for in
the algorithms.Helder D. Silva is supported by the Portuguese Foundation for Science
and Technology under the grant SFRBD/78018/2011.info:eu-repo/semantics/publishedVersio
Dial It In: Rotating RF Sensors to Enhance Radio Tomography
A radio tomographic imaging (RTI) system uses the received signal strength
(RSS) measured by RF sensors in a static wireless network to localize people in
the deployment area, without having them to carry or wear an electronic device.
This paper addresses the fact that small-scale changes in the position and
orientation of the antenna of each RF sensor can dramatically affect imaging
and localization performance of an RTI system. However, the best placement for
a sensor is unknown at the time of deployment. Improving performance in a
deployed RTI system requires the deployer to iteratively "guess-and-retest",
i.e., pick a sensor to move and then re-run a calibration experiment to
determine if the localization performance had improved or degraded. We present
an RTI system of servo-nodes, RF sensors equipped with servo motors which
autonomously "dial it in", i.e., change position and orientation to optimize
the RSS on links of the network. By doing so, the localization accuracy of the
RTI system is quickly improved, without requiring any calibration experiment
from the deployer. Experiments conducted in three indoor environments
demonstrate that the servo-nodes system reduces localization error on average
by 32% compared to a standard RTI system composed of static RF sensors.Comment: 9 page
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