1,146 research outputs found
AoA-aware Probabilistic Indoor Location Fingerprinting using Channel State Information
With expeditious development of wireless communications, location
fingerprinting (LF) has nurtured considerable indoor location based services
(ILBSs) in the field of Internet of Things (IoT). For most pattern-matching
based LF solutions, previous works either appeal to the simple received signal
strength (RSS), which suffers from dramatic performance degradation due to
sophisticated environmental dynamics, or rely on the fine-grained physical
layer channel state information (CSI), whose intricate structure leads to an
increased computational complexity. Meanwhile, the harsh indoor environment can
also breed similar radio signatures among certain predefined reference points
(RPs), which may be randomly distributed in the area of interest, thus mightily
tampering the location mapping accuracy. To work out these dilemmas, during the
offline site survey, we first adopt autoregressive (AR) modeling entropy of CSI
amplitude as location fingerprint, which shares the structural simplicity of
RSS while reserving the most location-specific statistical channel information.
Moreover, an additional angle of arrival (AoA) fingerprint can be accurately
retrieved from CSI phase through an enhanced subspace based algorithm, which
serves to further eliminate the error-prone RP candidates. In the online phase,
by exploiting both CSI amplitude and phase information, a novel bivariate
kernel regression scheme is proposed to precisely infer the target's location.
Results from extensive indoor experiments validate the superior localization
performance of our proposed system over previous approaches
RF Localization in Indoor Environment
In this paper indoor localization system based on the RF power measurements of the Received Signal Strength (RSS) in WLAN environment is presented. Today, the most viable solution for localization is the RSS fingerprinting based approach, where in order to establish a relationship between RSS values and location, different machine learning approaches are used. The advantage of this approach based on WLAN technology is that it does not need new infrastructure (it reuses already and widely deployed equipment), and the RSS measurement is part of the normal operating mode of wireless equipment. We derive the Cramer-Rao Lower Bound (CRLB) of localization accuracy for RSS measurements. In analysis of the bound we give insight in localization performance and deployment issues of a localization system, which could help designing an efficient localization system. To compare different machine learning approaches we developed a localization system based on an artificial neural network, k-nearest neighbors, probabilistic method based on the Gaussian kernel and the histogram method. We tested the developed system in real world WLAN indoor environment, where realistic RSS measurements were collected. Experimental comparison of the results has been investigated and average location estimation error of around 2 meters was obtained
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
Indoor wireless communications and applications
Chapter 3 addresses challenges in radio link and system design in indoor scenarios. Given the fact that most human activities take place in indoor environments, the need for supporting ubiquitous indoor data connectivity and location/tracking service becomes even more important than in the previous decades. Specific technical challenges addressed in this section are(i), modelling complex indoor radio channels for effective antenna deployment, (ii), potential of millimeter-wave (mm-wave) radios for supporting higher data rates, and (iii), feasible indoor localisation and tracking techniques, which are summarised in three dedicated sections of this chapter
Development and Experimental Analysis of Wireless High Accuracy Ultra-Wideband Localization Systems for Indoor Medical Applications
This dissertation addresses several interesting and relevant problems in the field of wireless technologies applied to medical applications and specifically problems related to ultra-wideband high accuracy localization for use in the operating room. This research is cross disciplinary in nature and fundamentally builds upon microwave engineering, software engineering, systems engineering, and biomedical engineering. A good portion of this work has been published in peer reviewed microwave engineering and biomedical engineering conferences and journals. Wireless technologies in medicine are discussed with focus on ultra-wideband positioning in orthopedic surgical navigation. Characterization of the operating room as a medium for ultra-wideband signal transmission helps define system design requirements.
A discussion of the first generation positioning system provides a context for understanding the overall system architecture of the second generation ultra-wideband positioning system outlined in this dissertation. A system-level simulation framework provides a method for rapid prototyping of ultra-wideband positioning systems which takes into account all facets of the system (analog, digital, channel, experimental setup). This provides a robust framework for optimizing overall system design in realistic propagation environments.
A practical approach is taken to outline the development of the second generation ultra-wideband positioning system which includes an integrated tag design and real-time dynamic tracking of multiple tags. The tag and receiver designs are outlined as well as receiver-side digital signal processing, system-level design support for multi-tag tracking, and potential error sources observed in dynamic experiments including phase center error, clock jitter and drift, and geometric position dilution of precision.
An experimental analysis of the multi-tag positioning system provides insight into overall system performance including the main sources of error. A five base station experiment shows the potential of redundant base stations in improving overall dynamic accuracy. Finally, the system performance in low signal-to-noise ratio and non-line-of-sight environments is analyzed by focusing on receiver-side digitally-implemented ranging algorithms including leading-edge detection and peak detection.
These technologies are aimed at use in next-generation medical systems with many applications including surgical navigation, wireless telemetry, medical asset tracking, and in vivo wireless sensors
Interference charecterisation, location and bandwidth estimation in emerging WiFi networks
Wireless LAN technology based on the IEEE 802.11 standard, commonly referred
to as WiFi, has been hugely successful not only for the last hop access to the Internet
in home, office and hotspot scenarios but also for realising wireless backhaul in mesh
networks and for point -to -point long- distance wireless communication. This success
can be mainly attributed to two reasons: low cost of 802.11 hardware from reaching
economies of scale, and operation in the unlicensed bands of wireless spectrum.The popularity of WiFi, in particular for indoor wireless access at homes and offices,
has led to significant amount of research effort looking at the performance issues
arising from various factors, including interference, CSMA/CA based MAC protocol
used by 802.11 devices, the impact of link and physical layer overheads on application
performance, and spatio-temporal channel variations. These factors affect the performance
of applications and services that run over WiFi networks. In this thesis, we
experimentally investigate the effects of some of the above mentioned factors in the
context of emerging WiFi network scenarios such as multi- interface indoor mesh networks,
802.11n -based WiFi networks and WiFi networks with virtual access points
(VAPs). More specifically, this thesis comprises of four experimental characterisation
studies: (i) measure prevalence and severity of co- channel interference in urban WiFi
deployments; (ii) characterise interference in multi- interface indoor mesh networks;
(iii) study the effect of spatio-temporal channel variations, VAPs and multi -band operation
on WiFi fingerprinting based location estimation; and (iv) study the effects of
newly introduced features in 802.11n like frame aggregation (FA) on available bandwidth
estimation.With growing density of WiFi deployments especially in urban areas, co- channel
interference becomes a major factor that adversely affects network performance. To
characterise the nature of this phenomena at a city scale, we propose using a new measurement
methodology called mobile crowdsensing. The idea is to leverage commodity
smartphones and the natural mobility of people to characterise urban WiFi co- channel
interference. Specifically, we report measurement results obtained for Edinburgh, a
representative European city, on detecting the presence of deployed WiFi APs via the
mobile crowdsensing approach. These show that few channels in 2.4GHz are heavily
used and there is hardly any activity in the 5GHz band even though relatively it
has a greater number of available channels. Spatial analysis of spectrum usage reveals
that co- channel interference among nearby APs operating in the same channel
can be a serious problem with around 10 APs contending with each other in many locations. We find that the characteristics of WiFi deployments at city -scale are similar
to those of WiFi deployments in public spaces of different indoor environments. We
validate our approach in comparison with wardriving, and also show that our findings
generally match with previous studies based on other measurement approaches. As
an application of the mobile crowdsensing based urban WiFi monitoring, we outline a
cloud based WiFi router configuration service for better interference management with
global awareness in urban areas.For mesh networks, the use of multiple radio interfaces is widely seen as a practical
way to achieve high end -to -end network performance and better utilisation of
available spectrum. However this gives rise to another type of interference (referred to
as coexistence interference) due to co- location of multiple radio interfaces. We show
that such interference can be so severe that it prevents concurrent successful operation
of collocated interfaces even when they use channels from widely different frequency
bands. We propose the use of antenna polarisation to mitigate such interference and
experimentally study its benefits in both multi -band and single -band configurations. In
particular, we show that using differently polarised antennas on a multi -radio platform
can be a helpful counteracting mechanism for alleviating receiver blocking and adjacent
channel interference phenomena that underlie multi -radio coexistence interference.
We also validate observations about adjacent channel interference from previous
studies via direct and microscopic observation of MAC behaviour.Location is an indispensable information for navigation and sensing applications.
The rapidly growing adoption of smartphones has resulted in a plethora of mobile
applications that rely on position information (e.g., shopping apps that use user position
information to recommend products to users and help them to find what they want
in the store). WiFi fingerprinting is a popular and well studied approach for indoor
location estimation that leverages the existing WiFi infrastructure and works based on
the difference in strengths of the received AP signals at different locations. However,
understanding the impact of WiFi network deployment aspects such as multi -band
APs and VAPs has not received much attention in the literature. We first examine the
impact of various aspects underlying a WiFi fingerprinting system. Specifically, we
investigate different definitions for fingerprinting and location estimation algorithms
across different indoor environments ranging from a multi- storey office building to
shopping centres of different sizes. Our results show that the fingerprint definition
is as important as the choice of location estimation algorithm and there is no single
combination of these two that works across all environments or even all floors of a given environment. We then consider the effect of WiFi frequency bands (e.g., 2.4GHz
and 5GHz) and the presence of virtual access points (VAPs) on location accuracy with
WiFi fingerprinting. Our results demonstrate that lower co- channel interference in the
5GHz band yields more accurate location estimation. We show that the inclusion of
VAPs has a significant impact on the location accuracy of WiFi fingerprinting systems;
we analyse the potential reasons to explain the findings.End -to -end available bandwidth estimation (ABE) has a wide range of uses, from
adaptive application content delivery, transport-level transmission rate adaptation and
admission control to traffic engineering and peer node selection in peer -to- peer /overlay
networks [ 1, 2]. Given its importance, it has been received much research attention in
both wired data networks and legacy WiFi networks (based on 802.11 a/b /g standards),
resulting in different ABE techniques and tools proposed to optimise different criteria
and suit different scenarios. However, effects of new MAC/PHY layer enhancements
in new and next generation WiFi networks (based on 802.11n and 802.11ac
standards) have not been studied yet. We experimentally find that among different
new features like frame aggregation, channel bonding and MIMO modes (spacial division
multiplexing), frame aggregation has the most harmful effect as it has direct
effect on ABE by distorting the measurement probing traffic pattern commonly used
to estimate available bandwidth. Frame aggregation is also specified in both 802.11n
and 802.1 lac standards as a mandatory feature to be supported. We study the effect of
enabling frame aggregation, for the first time, on the performance of the ABE using an
indoor 802.11n wireless testbed. The analysis of results obtained using three tools -
representing two main Probe Rate Model (PRM) and Probe Gap Model (PGM) based
approaches for ABE - led us to come up with the two key principles of jumbo probes
and having longer measurement probe train sizes to counter the effects of aggregating
frames on the performance of ABE tools. Then, we develop a new tool, WBest+ that
is aware of the underlying frame aggregation by incorporating these principles. The
experimental evaluation of WBest+ shows more accurate ABE in the presence of frame
aggregation.Overall, the contributions of this thesis fall in three categories - experimental
characterisation, measurement techniques and mitigation/solution approaches for performance
problems in emerging WiFi network scenarios. The influence of various factors
mentioned above are all studied via experimental evaluation in a testbed or real - world setting. Specifically, co- existence interference characterisation and evaluation
of available bandwidth techniques are done using indoor testbeds, whereas characterisation of urban WiFi networks and WiFi fingerprinting based location estimation are
carried out in real environments. New measurement approaches are also introduced
to aid better experimental evaluation or proposed as new measurement tools. These
include mobile crowdsensing based WiFi monitoring; MAC/PHY layer monitoring of
co- existence interference; and WBest+ tool for available bandwidth estimation. Finally,
new mitigation approaches are proposed to address challenges and problems
identified throughout the characterisation studies. These include: a proposal for crowd - based interference management in large scale uncoordinated WiFi networks; exploiting
antenna polarisation diversity to remedy the effects of co- existence interference
in multi -interface platforms; taking advantage of VAPs and multi -band operation for
better location estimation; and introducing the jumbo frame concept and longer probe
train sizes to improve performance of ABE tools in next generation WiFi networks
Location-Enabled IoT (LE-IoT): A Survey of Positioning Techniques, Error Sources, and Mitigation
The Internet of Things (IoT) has started to empower the future of many
industrial and mass-market applications. Localization techniques are becoming
key to add location context to IoT data without human perception and
intervention. Meanwhile, the newly-emerged Low-Power Wide-Area Network (LPWAN)
technologies have advantages such as long-range, low power consumption, low
cost, massive connections, and the capability for communication in both indoor
and outdoor areas. These features make LPWAN signals strong candidates for
mass-market localization applications. However, there are various error sources
that have limited localization performance by using such IoT signals. This
paper reviews the IoT localization system through the following sequence: IoT
localization system review -- localization data sources -- localization
algorithms -- localization error sources and mitigation -- localization
performance evaluation. Compared to the related surveys, this paper has a more
comprehensive and state-of-the-art review on IoT localization methods, an
original review on IoT localization error sources and mitigation, an original
review on IoT localization performance evaluation, and a more comprehensive
review of IoT localization applications, opportunities, and challenges. Thus,
this survey provides comprehensive guidance for peers who are interested in
enabling localization ability in the existing IoT systems, using IoT systems
for localization, or integrating IoT signals with the existing localization
sensors
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