249 research outputs found
MAC layer assisted localization in wireless environments with multiple sensors and multiple emitters
Extreme emitter density (EED) RF environments, defined
as 10k-100k emitters within a footprint of less than 1 km squared, are becoming increasingly common with the proliferation of personal devices containing myriad communication standards (e.g. WLAN, Bluetooth, 4G, etc). Attendees at concerts, sporting events, and other such large-scale events desire to be connected at all times, creating tremendous spectrum management challenges, especially in unlicensed frequencies such as 2.4 GHz, 5 GHz, or 900 MHz Industrial, Scientific, and Medical (ISM) bands. In licensed bands, there are often critical communication systems such as two-way radios for emergency personnel which must be free from interference. Identification and localization of a non-conforming or interfering Emitter of Interest (EoI) is important for these critical systems.
In this dissertation, research is conducted to improve localization for these EED RF environments by exploiting side information available at the Medium Access Control (MAC) layer. The primary contributions of this research are: (1) A testbed in Bobby
Dodd football stadium consisting of three spatially distributed, time-synchronized RF Sensor Nodes (RFSN) collecting and archiving complex baseband samples for algorithm development and
validation. (2) A modeling framework and analytical results on the benefits of exploiting
the structure of the MAC layer for associating physical layer measurements, such as Time Difference of Arrivals (TDoA), to emitters. (3) A three stage localization algorithm exploiting time between packets and a constrained geometry to shrink the error ellipse of the emitter position estimate. The results are expected to improve localization accuracy in wireless environments when multiple sensors observe multiple emitters using a known communications protocol within a constrained geometry.Ph.D
A Hybrid Stochastic Approach for Self-Location of Wireless Sensors in Indoor Environments
Indoor location systems, especially those using wireless sensor networks, are used in many application areas. While the need for these systems is widely proven, there is a clear lack of accuracy. Many of the implemented applications have high errors in their location estimation because of the issues arising in the indoor environment. Two different approaches had been proposed using WLAN location systems: on the one hand, the so-called deductive methods take into account the physical properties of signal propagation. These systems require a propagation model, an environment map, and the position of the radio-stations. On the other hand, the so-called inductive methods require a previous training phase where the system learns the received signal strength (RSS) in each location. This phase can be very time consuming. This paper proposes a new stochastic approach which is based on a combination of deductive and inductive methods whereby wireless sensors could determine their positions using WLAN technology inside a floor of a building. Our goal is to reduce the training phase in an indoor environment, but, without an loss of precision. Finally, we compare the measurements taken using our proposed method in a real environment with the measurements taken by other developed systems. Comparisons between the proposed system and other hybrid methods are also provided
Survey of energy efficient tracking and localization techniques in buildings using optical and wireless communication media
This paper presents a survey of beamforming, beamsteering and mobile tracking techniques. The survey was made in the context of the SOWICI project. The aim of this project is to reduce power consumption of data exchanging devices within houses. An optical fiber network is used for data transport to and from rooms whereas wireless transceivers communicate with appliances within the rooms. Using this approach, the aim is to reduce power consumption and exposure to electromagnetic radiation. To realize this, beamforming will be used to only radiate energy in, and receive signals from, the direction of interest. Because appliances within households can move, some of them even relatively fast, the pointing direction of the beam should be steerable. The pointing direction can be deduced from the communication link (beamsteering) or via separate mobile tracking techniques
The Effects of Cognitive Jamming on Wireless Sensor Networks used for Geolocation
The increased use of Wireless Sensor Networks (WSN) for geolocation has led to an increased reliance on this technology. Jamming, protecting jamming, and detecting jamming in a WSN are areas of study that have greatly increased in interest. This research uses simulations and data collected from hardware experiments to test the effects of jamming on a WSN. Hardware jamming was tested using a Universal Software Radio Peripheral (USRP) Version 2 to assess the effects of jamming on a cooperative network of Java Sun SPOTs. The research combines simulations and data collected from the hardware experiments to see the effects of jamming on cooperative and noncooperative geolocation
Design of an adaptive RF fingerprint indoor positioning system
RF fingerprinting can solve the indoor positioning problem with satisfactory
accuracy, but the methodology depends on the so-called radio map calibrated in
the offline phase via manual site-survey, which is costly, time-consuming and
somewhat error-prone. It also assumes the RF fingerprintâs signal-spatial
correlations to remain static throughout the online positioning phase, which
generally does not hold in practice. This is because indoor environments
constantly experience dynamic changes, causing the radio signal strengths to
fluctuate over time, which weakens the signal-spatial correlations of the RF
fingerprints. State-of-the-arts have proposed adaptive RF fingerprint
methodology capable of calibrating the radio map in real-time and on-demand
to address these drawbacks. However, existing implementations are highly
server-centric, which is less robust, does not scale well, and not privacy-friendly.
This thesis aims to address these drawbacks by exploring the
feasibility of implementing an adaptive RF fingerprint indoor positioning
system in a distributed and client-centric architecture using only commodity
Wi-Fi hardware, so it can seamlessly integrate with existing Wi-Fi network and
allow it to offer both networking and positioning services. Such approach has
not been explored in previous works, which forms the basis of this thesisâ main
contribution.
The proposed methodology utilizes a network of distributed location beacons as
its reference infrastructure; hence the system is more robust since it does not
have any single point-of-failure. Each location beacon periodically broadcasts its
coordinate to announce its presence in the area, plus coefficients that model its
real-time RSS distribution around the transmitting antenna. These coefficients
are constantly self-calibrated by the location beacon using empirical RSS
measurements obtained from neighbouring location beacons in a collaborative
fashion, and fitting the values using path loss with log-normal shadowing model
as a function of inter-beacon distances while minimizing the error in a least-squared
sense. By self-modelling its RSS distribution in real-time, the location
beacon becomes aware of its dynamically fluctuating signal levels caused by
physical, environmental and temporal characteristics of the indoor
environment. The implementation of this self-modelling feature on commodity
Wi-Fi hardware is another original contribution of this thesis.
Location discovery is managed locally by the clients, which means the proposed
system can support unlimited number of client devices simultaneously while
also protect userâs privacy because no information is shared with external
parties. It starts by listening for beacon frames broadcasted by nearby location
beacons and measuring their RSS values to establish the RF fingerprint of the
unknown point. Next, it simulates the reference RF fingerprints of
predetermined points inside the target area, effectively calibrating the siteâs
radio map, by computing the RSS values of all detected location beacons using
their respective coordinates and path loss coefficients embedded inside the
received beacon frames. Note that the coefficients model the real-time RSS
distribution of each location beacon around its transmitting antenna; hence, the
radio map is able to adapt itself to the dynamic fluctuations of the radio signal to
maintain its signal-spatial correlations. The final step is to search the radio map
to find the reference RF fingerprint that most closely resembles the unknown
sample, where its coordinate is returned as the location result.
One positioning approach would be to first construct a full radio map by
computing the RSS of all detected location beacons at all predetermined
calibration points, then followed by an exhaustive search over all reference RF
fingerprints to find the best match. Generally, RF fingerprint algorithm performs
better with higher number of calibration points per unit area since more
locations can be classified, while extra RSS components can help to better
distinguish between nearby calibration points. However, to calibrate and search
many RF fingerprints will incur substantial computing costs, which is unsuitable
for power and resource limited client devices. To address this challenge, this
thesis introduces a novel algorithm suitable for client-centric positioning as
another contribution. Given an unknown RF fingerprint to solve for location, the
proposed algorithm first sorts the RSS in descending order. It then iterates over
this list, first selecting the location beacon with the strongest RSS because this
implies the unknown location is closest to the said location beacon. Next, it
computes the beaconâs RSS using its path loss coefficients and coordinate
information one calibration point at a time while simultaneously compares the
result with the measured value. If they are similar, the algorithm keeps this
location for subsequent processing; else it is removed because distant points
relative to the unknown location would exhibit vastly different RSS values due
to the different site-specific obstructions encountered by the radio signal
propagation. The algorithm repeats the process by selecting the next strongest
location beacon, but this time it only computes its RSS for those points identified
in the previous iteration. After the last iteration completes, the average
coordinate of remaining calibration points is returned as the location result.
Matlab simulation shows the proposed algorithm only takes about half of the
time to produce a location estimate with similar positioning accuracy compared
to conventional algorithm that does a full radio map calibration and exhaustive
RF fingerprint search.
As part of the thesisâ contribution, a prototype of the proposed indoor
positioning system is developed using only commodity Wi-Fi hardware and
open-source software to evaluate its usability in real-world settings and to
demonstrate possible implementation on existing Wi-Fi installations.
Experimental results verify the proposed system yields consistent positioning
accuracy, even in highly dynamic indoor environments and changing location
beacon topologies
Software Defined Radio Localization using 802.11-style Communications
This major qualifying project implements a simple indoor localization system using software defined radio. Both time of arrival and received signal strength methods are used by an array of wireless receivers to trilaterate a cooperative transmitter. The implemented system builds upon an IEEE 802.11b-like communications platform implemented in GNU Radio. Our results indicate substantial room for improvement, particularly in the acquisition of time data. This project contributes a starting point for ongoing research in indoor localization, both through our literature review and system implementation
Effiziente Lokalisierung von Nutzern und GerÀten in Smarten Umgebungen
The thesis considers determination of location of sensors and users in smart environments using measurements of Received Signal Strength (RSS). The first part of the thesis focuses on localization in Wireless Sensor Networks and contributes two fully distributed algorithms which address the Sensor Selection Problem and provide the best trade-off between energy consumption and localization accuracy among the algorithms considered. Furthermore, the thesis contributes to Device Free Localization an indoor localization concept providing scalable and highly accurate location estimates (prototype: 0.36mÂČ MSE) while using a COTS passive RFID-System and not relying on user-carried sensors
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