895 research outputs found
Real-time localisation system for GPS denied open areas using smart street furniture
Real-time measurement of crowd dynamics has been attracting significant interest, as it has many applications including real-time monitoring of emergencies and evacuation plans. To effectively measure crowd behaviour, an accurate estimate for pedestrians’ locations is required. However, estimating pedestrians’ locations is a great challenge especially for open areas with poor Global Positioning System (GPS) signal reception and/or lack of infrastructure to install expensive solutions such as video-based systems.
Street furniture assets such as rubbish bins have become smart, as they have been equipped with low-power sensors. Currently, their role is limited to certain applications such as waste management. We believe that the role of street furniture can be extended to include building real-time localisation systems as street furniture provides excellent coverage across different areas such as parks, streets, homes, universities.
In this thesis, we propose a novel wireless sensor network architecture designed for smart street furniture. We extend the functionality of sensor nodes to act as soft Access Point (AP), sensing Wifi signals received from surrounding Wifi-enabled devices. Our proposed architecture includes a real-time and low-power design for sensor nodes. We attached sensor nodes to rubbish bins located in a busy GPS denied open area at Murdoch University (Perth, Western Australia), known as Bush Court. This enabled us to introduce two unique Wifi-based localisation datasets: the first is the Fingerprint dataset called MurdochBushCourtLoC-FP (MBCLFP) in which four users generated Wifi fingerprints for all available cells in the gridded Bush Court, called Reference Points (RPs), using their smartphones, and the second is the APs dataset called MurdochBushCourtLoC-AP (MBCLAP) that includes auto-generated records received from over 1000 users’ devices.
Finally, we developed a real-time localisation approach based on the two datasets using a four-layer deep learning classifier. The approach includes a light-weight algorithm to label the MBCLAP dataset using the MBCLFP dataset and convert the MBCLAP dataset to be synchronous. With the use of our proposed approach, up to 19% improvement in location prediction is achieved
Enhancing Indoor Localisation: a Bluetooth Low Energy (BLE) Beacon Placement approach
Indoor location-based services have become increasingly vital in various sectors,
including industries, healthcare, airports, and crowded infrastructures, facilitating
asset tracking and user navigation. This project addresses the critical challenge of
optimising beacon placement for indoor location, employing Bluetooth technology
as the communication protocol. The significance of this research lies in the effi ciency and accuracy that an optimised beacon layout can provide, enhancing the
effectiveness of indoor positioning systems. The algorithm developed takes into con sideration materials attenuation, coverage and Line of Sight (LOS) conditions to
optimise its layouts. Experimental validation of the algorithm’s performance was
conducted by comparing two beacon layouts: one optimised by the algorithm and
the other manually arranged by individuals with empirical knowledge in the field.
The experiment considered three distinct positions within the schematic, allowing
for a comprehensive assessment of the optimised layout’s superior performance. The
results of this research offer insights into the potential of the algorithm to revolu tionise indoor location services, providing a more reliable and cost-effective solution
for a multitude of applications.Os serviços de localização em ambientes internos tornaram-se cada vez mais essenciais em vários setores, incluindo indústrias, cuidados de saúde, aeroportos e
infraestruturas movimentadas, facilitando o rastreamento de objetos e a navegação
de utilizadores. Este projeto aborda o desafio crÃtico da otimização da colocação de
beacons para localização em ambientes internos, utilizando a tecnologia Bluetooth
como protocolo de comunicação. A importância desta pesquisa reside na eficiência e
precisão que uma disposição otimizada de beacons pode proporcionar, melhorando
a eficácia de sistemas de posicionamento em ambientes internos. O algoritmo desenvolvido leva em consideração a atenuação de materiais, a cobertura e as condições
de visão direta para otimizar as suas disposições. A validação experimental do desempenho do algoritmo foi realizada ao comparar duas disposições de beacons: uma
otimizada pelo algoritmo e outra organizada manualmente por indivÃduos com conhecimento empÃrico na área. A experiência considerou três posições distintas no
esquema, permitindo uma avaliação abrangente do desempenho superior da disposição otimizada. Os resultados desta pesquisa oferecem descobertas importantes
sobre o potencial do algoritmo para revolucionar os serviços de localização em ambientes internos, proporcionando uma solução mais confiável e econômica para uma
variedade de aplicações
3D Indoor Positioning in 5G networks
Over the past two decades, the challenge of accurately positioning objects or users indoors, especially in areas where Global Navigation Satellite Systems (GNSS) are not available, has been a significant focus for the research community. With the rise of 5G IoT networks, the quest for precise 3D positioning in various industries has driven researchers to explore various machine learning-based positioning techniques.
Within this context, researchers are leveraging a mix of existing and emerging wireless communication technologies such as cellular, Wi-Fi, Bluetooth, Zigbee, Visible Light Communication (VLC), etc., as well as integrating any available useful data to enhance the speed and accuracy of indoor positioning. Methods for indoor positioning involve combining various parameters such as received signal strength (RSS), time of flight (TOF), time of arrival (TOA), time difference of arrival (TDOA), direction of arrival (DOA) and more.
Among these, fingerprint-based positioning stands out as a popular technique in Real Time Localisation Systems (RTLS) due to its simplicity and cost-effectiveness.
Positioning systems based on fingerprint maps or other relevant methods find applications in diverse scenarios, including malls for indoor navigation and geo-marketing, hospitals for monitoring patients, doctors, and critical equipment, logistics for asset tracking and optimising storage spaces, and homes for providing Ambient Assisted Living (AAL) services.
A significant challenge facing all indoor positioning systems is the objective evaluation of their performance. This challenge is compounded by the coexistence of heterogeneous technologies and the rapid advancement of computation. There is a vast potential for information fusion to be explored. These observations have led to the motivation behind our work. As a result, two novel algorithms and a framework are introduced in this thesis
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
Information Fusion for 5G IoT: An Improved 3D Localisation Approach Using K-DNN and Multi-Layered Hybrid Radiomap
Indoor positioning is a core enabler for various 5G identity and context-aware applications requiring precise and real-time simultaneous localisation and mapping (SLAM). In this work, we propose a K-nearest neighbours and deep neural network (K-DNN) algorithm to improve 3D indoor positioning. Our implementation uses a novel data-augmentation concept for the received signal strength (RSS)-based fingerprint technique to produce a 3D fused hybrid. In the offline phase, a machine learning (ML) approach is used to train a model on a radiomap dataset that is collected during the offline phase. The proposed algorithm is implemented on the constructed hybrid multi-layered radiomap to improve the 3D localisation accuracy. In our implementation, the proposed approach is based on the fusion of the prominent 5G IoT signals of Bluetooth Low Energy (BLE) and the ubiquitous WLAN. As a result, we achieved a 91% classification accuracy in 1D and a submeter accuracy in 2D
Using hidden markov models to improve floor level localisation
The focus of this paper is on estimating the oor level of a robot/person moving in a multi-oor environment. It demonstrates how in- formation about transitions between oors can be employed within a probabilistic framework to improve the accuracy of oor level estimation. This is achieved by combining a simple linear classifier with a Hidden Markov Model that captures the two basic motion patterns in a multi-oor environment: Within-oor and be-Tween oors, switching from one to the other as oor transition events are detected. Through real-world experiments, we demonstrate the ability of this framework to produce accurate oor level estimates using only RSSI (Received Signal Strength Indicator) measurements, even when operating in an environment with as little as five WiFi access points per oor
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