486 research outputs found
Using context-aware sub sorting of received signal strength fingerprints for indoor localisation
Mobile indoor localisation has numerous uses for logistics, health, sport and social networking applications. Current wireless localisation systems experience reliability difficulties while operating within indoor environments due to interference caused by the presence of metallic infrastructure. Current position localisation use wireless channel propagation characteristics, such as RF receive signal strength to localise a user\u27s position, which is subject to interference. To overcome this, we developed a Fingerprint Context Aware Partitioning tracking model for tracking people within a building. The Fingerprint Context Aware Partitioning tracking model used received RF signal strength fingerprinting, combined with localised context aware information about the user\u27s immediate indoor environment surroundings. We also present an inexpensive and robust wireless localisation network that can track the location of users in an indoor environment, using the Zigbee/802.15.4 wireless communications protocol. The wireless localisation network used reference nodes placed at known positions in a building. The reference nodes are used by mobile nodes, carried by users to localise their position. We found that the Fingerprint Context Aware Partitioning model had improved performance than using only multilateration, in locations that were not in range of multiple reference nodes. Further work includes investigating how multiple mobile nodes can be used by Fingerprint Context Aware Partition model to improve position accuracy
Minimal Infrastructure Radio Frequency Home Localisation Systems
The ability to track the location of a subject in their home allows the provision of a
number of location based services, such as remote activity monitoring, context sensitive
prompts and detection of safety critical situations such as falls. Such pervasive monitoring
functionality offers the potential for elders to live at home for longer periods of their lives
with minimal human supervision.
The focus of this thesis is on the investigation and development of a home roomlevel
localisation technique which can be readily deployed in a realistic home environment
with minimal hardware requirements. A conveniently deployed Bluetooth ®
localisation
platform is designed and experimentally validated throughout the thesis. The platform
adopts the convenience of a mobile phone and the processing power of a remote location
calculation computer. The use of Bluetooth ®
also ensures the extensibility of the platform
to other home health supervision scenarios such as wireless body sensor monitoring.
Central contributions of this work include the comparison of probabilistic and nonprobabilistic
classifiers for location prediction accuracy and the extension of probabilistic
classifiers to a Hidden Markov Model Bayesian filtering framework. New location
prediction performance metrics are developed and signicant performance improvements
are demonstrated with the novel extension of Hidden Markov Models to higher-order
Markov movement models. With the simple probabilistic classifiers, location is correctly
predicted 80% of the time. This increases to 86% with the application of the Hidden
Markov Models and 88% when high-order Hidden Markov Models are employed.
Further novelty is exhibited in the derivation of a real-time Hidden Markov Model
Viterbi decoding algorithm which presents all the advantages of the original algorithm,
while producing location estimates in real-time. Significant contributions are also made
to the field of human gait-recognition by applying Bayesian filtering to the task of motion
detection from accelerometers which are already present in many mobile phones. Bayesian filtering is demonstrated to enable a 35% improvement in motion recognition rate and even
enables a
floor recognition rate of 68% using only accelerometers. The unique application
of time-varying Hidden Markov Models demonstrates the effect of integrating these freely
available motion predictions on long-term location predictions
A Review of Radio Frequency Based Localization for Aerial and Ground Robots with 5G Future Perspectives
Efficient localization plays a vital role in many modern applications of
Unmanned Ground Vehicles (UGV) and Unmanned aerial vehicles (UAVs), which would
contribute to improved control, safety, power economy, etc. The ubiquitous 5G
NR (New Radio) cellular network will provide new opportunities for enhancing
localization of UAVs and UGVs. In this paper, we review the radio frequency
(RF) based approaches for localization. We review the RF features that can be
utilized for localization and investigate the current methods suitable for
Unmanned vehicles under two general categories: range-based and fingerprinting.
The existing state-of-the-art literature on RF-based localization for both UAVs
and UGVs is examined, and the envisioned 5G NR for localization enhancement,
and the future research direction are explored
Using context-aware sub sorting of received signal strength fingerprints for indoor localisation
Mobile indoor localisation has numerous uses for logistics, health, sport and social networking applications. Current wireless localisation systems experience reliability difficulties while operating within indoor environments due to interference caused by the presence of metallic infrastructure. Current position localisation use wireless channel propagation characteristics, such as RF receive signal strength to localise a user\u27s position, which is subject to interference. To overcome this, we developed a Fingerprint Context Aware Partitioning tracking model for tracking people within a building. The Fingerprint Context Aware Partitioning tracking model used received RF signal strength fingerprinting, combined with localised context aware information about the user\u27s immediate indoor environment surroundings. We also present an inexpensive and robust wireless localisation network that can track the location of users in an indoor environment, using the Zigbee/802.15.4 wireless communications protocol. The wireless localisation network used reference nodes placed at known positions in a building. The reference nodes are used by mobile nodes, carried by users to localise their position. We found that the Fingerprint Context Aware Partitioning model had improved performance than using only multilateration, in locations that were not in range of multiple reference nodes. Further work includes investigating how multiple mobile nodes can be used by Fingerprint Context Aware Partition model to improve position accuracy
A survey of self organisation in future cellular networks
This article surveys the literature over the period of the last decade on the emerging field of self organisation as applied to wireless cellular communication networks. Self organisation has been extensively studied and applied in adhoc networks, wireless sensor networks and autonomic computer networks; however in the context of wireless cellular networks, this is the first attempt to put in perspective the various efforts in form of a tutorial/survey. We provide a comprehensive survey of the existing literature, projects and standards in self organising cellular networks. Additionally, we also aim to present a clear understanding of this active research area, identifying a clear taxonomy and guidelines for design of self organising mechanisms. We compare strength and weakness of existing solutions and highlight the key research areas for further development. This paper serves as a guide and a starting point for anyone willing to delve into research on self organisation in wireless cellular communication networks
New Approach of Indoor and Outdoor Localization Systems
Accurate determination of the mobile position constitutes the basis of many new applications. This book provides a detailed account of wireless systems for positioning, signal processing, radio localization techniques (Time Difference Of Arrival), performances evaluation, and localization applications. The first section is dedicated to Satellite systems for positioning like GPS, GNSS. The second section addresses the localization applications using the wireless sensor networks. Some techniques are introduced for localization systems, especially for indoor positioning, such as Ultra Wide Band (UWB), WIFI. The last section is dedicated to Coupled GPS and other sensors. Some results of simulations, implementation and tests are given to help readers grasp the presented techniques. This is an ideal book for students, PhD students, academics and engineers in the field of Communication, localization & Signal Processing, especially in indoor and outdoor localization domains
Indoor and Outdoor Location Estimation in Large Areas Using Received Signal Strength
PhDLocation estimation when deployed on wireless networks supports a range of services
including user tracking and monitoring, health care support and push and pull marketing.
The main subject of this thesis is improving indoor and outdoor location estimation
accuracy using received signal strength (RSS) from neighbouring base stations (BSs) or
access points (APs), without using the global positioning system (GPS) or triangulation
methods. For the outdoor environment, state-of-the-art deterministic and probabilistic
algorithms are adapted to exploit principal components (PCs) and clustering. The
accuracy is compared with K-nearest neighbour (KNN) algorithms using different partitioning
models. The proposed scheme clusters the RSS tuples based on deviations from
an estimated RSS attenuation model and then transforms the raw RSS in each cluster
into new uncorrelated dimensions, using PCs. As well as simple global dimensionality
reduction using PCs, the data reduction and rotation within each cluster improves estimation
accuracy because a) each cluster can model the different local RSS distributions
and b) it efficiently preserves the RSS correlations that are observed (some of which are
substantial) in local regions and which independence approximations ignore. Different
simulated and real environments are used for the comparisons. Experimental results
show that positioning accuracy is significantly improved and fewer training samples are
needed compared with traditional methods. Furthermore, a technique to adjust RSS
data so that radio maps collected in different environmental conditions can be used together
to enhance accuracy is also demonstrated. Additionally, in the radio coverage
domain, a non-parametric probability approach is used for the radio reliability estimation
and a semi-supervised learning model is proposed for the monitoring model training
and evolution according to real-time mobile users’ RSS feedback.
For the indoor environment, an approach for a large multi-story indoor location estimaiii
tion using clustering and rank order matching is described. The accuracies using WiFi
RSS alone, cellular GSM RSS alone and integrated WiFi and GSM RSS are presented.
The methods were tested on real indoor environments. A hierarchical clustering method
is used to partition the RSS space, where a cluster is defined as a set of mobile users
who share exactly the same strongest RSS ranking set of transmitters. The experimental
results show that while integrating of WiFi RSS with GSM RSS creates a marginal
improvement, the GSM data can be used to ameliorate the loss of accuracy when AP
Self-organizing Network Optimization via Placement of Additional Nodes
Das Hauptforschungsgebiet des Graduiertenkollegs "International Graduate
School on Mobile Communication" (GS Mobicom) der Technischen Universität
Ilmenau ist die Kommunikation in Katastrophenszenarien. Wegen eines
Desasters oder einer Katastrophe können die terrestrischen Elementen der
Infrastruktur eines Kommunikationsnetzwerks beschädigt oder komplett
zerstört werden. Dennoch spielen verfügbare Kommunikationsnetze eine sehr
wichtige Rolle während der Rettungsmaßnahmen, besonders für die
Koordinierung der Rettungstruppen und fĂĽr die Kommunikation zwischen ihren
Mitgliedern. Ein solcher Service kann durch ein mobiles Ad-Hoc-Netzwerk
(MANET) zur VerfĂĽgung gestellt werden. Ein typisches Problem der MANETs
ist Netzwerkpartitionierung, welche zur Isolation von verschiedenen
Knotengruppen führt. Eine mögliche Lösung dieses Problems ist die
Positionierung von zusätzlichen Knoten, welche die Verbindung zwischen den
isolierten Partitionen wiederherstellen können. Hauptziele dieser Arbeit
sind die Recherche und die Entwicklung von Algorithmen und Methoden zur
Positionierung der zusätzlichen Knoten. Der Fokus der Recherche liegt auf
Untersuchung der verteilten Algorithmen zur Bestimmung der Positionen fĂĽr
die zusätzlichen Knoten. Die verteilten Algorithmen benutzen nur die
Information, welche in einer lokalen Umgebung eines Knotens verfĂĽgbar ist,
und dadurch entsteht ein selbstorganisierendes System. Jedoch wird das
gesamte Netzwerk hier vor allem innerhalb eines ganz speziellen Szenarios -
Katastrophenszenario - betrachtet. In einer solchen Situation kann die
Information ĂĽber die Topologie des zu reparierenden Netzwerks im Voraus
erfasst werden und soll, natĂĽrlich, fĂĽr die Wiederherstellung mitbenutzt
werden. Dank der eventuell verfügbaren zusätzlichen Information können
die Positionen für die zusätzlichen Knoten genauer ermittelt werden. Die
Arbeit umfasst eine Beschreibung, Implementierungsdetails und eine
Evaluierung eines selbstorganisierendes Systems, welche die
Netzwerkwiederherstellung in beiden Szenarien ermöglicht.The main research area of the International Graduate School on Mobile
Communication (GS Mobicom) at Ilmenau University of Technology is
communication in disaster scenarios. Due to a disaster or an accident, the
network infrastructure can be damaged or even completely destroyed.
However, available communication networks play a vital role during the
rescue activities especially for the coordination of the rescue teams and
for the communication between their members. Such a communication service
can be provided by a Mobile Ad-Hoc Network (MANET). One of the typical
problems of a MANET is network partitioning, when separate groups of nodes
become isolated from each other. One possible solution for this problem is
the placement of additional nodes in order to reconstruct the communication
links between isolated network partitions. The primary goal of this work is
the research and development of algorithms and methods for the placement of
additional nodes. The focus of this research lies on the investigation of
distributed algorithms for the placement of additional nodes, which use
only the information from the nodes’ local environment and thus form a
self-organizing system. However, during the usage specifics of the system
in a disaster scenario, global information about the topology of the
network to be recovered can be known or collected in advance. In this case,
it is of course reasonable to use this information in order to calculate
the placement positions more precisely. The work provides the description,
the implementation details and the evaluation of a self-organizing system
which is able to recover from network partitioning in both situations
A fuzzy logic approach to localisation in wireless local area networks
This thesis examines the use and value of fuzzy sets, fuzzy logic and fuzzy inference in wireless positioning systems and solutions. Various fuzzy-related techniques and methodologies are reviewed and investigated, including a comprehensive review of fuzzy-based positioning and localisation systems. The thesis is aimed at the development of a novel positioning technique which enhances well-known multi-nearest-neighbour (kNN) and fingerprinting algorithms with received signal strength (RSS) measurements. A fuzzy inference system is put forward for the generation of weightings for selected nearest-neighbours and the elimination of outliers. In this study, Monte Carlo simulations of a proposed multivariable fuzzy localisation (MVFL) system showed a significant improvement in the root mean square error (RMSE) in position estimation, compared with well-known localisation algorithms. The simulation outcomes were confirmed empirically in laboratory tests under various scenarios. The proposed technique uses available indoor wireless local area network (WLAN) infrastructure and requires no additional hardware or modification to the network, nor any active user participation. The thesis aims to benefit practitioners and academic researchers of system positioning
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