59 research outputs found
A novel energy efficient wireless sensor network framework for object tracking
Object tracking is a typical application of Wireless Sensor Networks (WSNs), which
refers to the process of locating a moving object (or multiple objects) over time using
a sensor network. Object tracking in WSNs can be a time consuming and resource
hungry process due to factors, such as the amount of data generated or limited resources
available to the sensor network.
The traditional centralised approaches where a number of sensors transmit all information
to a base station or a sink node, increase computation burden. More recently static
or dynamic clustering approaches have been explored. Both clustering approaches suffer
from certain problems, such as, large clusters, redundant data collection and excessive
energy consumption. In addition, most existing object tracking algorithms mainly
focus on tracking an object instead of predicting the destination of an object.
To address the limitations of existing approaches, this thesis presents a novel framework
for efficient object tracking using sensor networks. It consists of a Hierarchical Hybrid
Clustering Mechanism (HHCM) with a Prediction-based Algorithm for Destinationestimation
(PAD). The proposed framework can track the destination of the object
without prior information of the objects movement, while providing significant reduction
in energy consumption. The costs of computation and communication are also
reduced by collecting the most relevant information and discarding irrelevant information
at the initial stages of communication. The contributions of this thesis are:
Firstly, a novel Prediction-based Algorithm for Destination-estimation (PAD) has
been presented, that predicts the final destination of the object and the path that
particular object will take to that destination. The principles of origin destination
(OD) estimation have been adopted to create a set of trajectories that a particular
object could follow. These paths are made up of a number of mini-clusters, formed
for tracking the object, combined together. PAD also contains a Multi-level Recovery
Mechanism (MRM) that recovers tracking if the object is lost. MRM minimises the
number of nodes involved in the recovery process by initiating the process at local level
and then expanding to add more nodes till the object is recovered.
Secondly, a network architecture called Hierarchical Hybrid Clustering Mechanism
(HHCM) has been developed, that forms dynamic mini-clusters within and across static
clusters to reduce the number of nodes involved in the tracking process and to distribute
the initial computational tasks amoung a larger number of mini-cluster heads.
Lastly, building upon the HHCM to create a novel multi-hierarchy aggregation and
next-step prediction mechanism to gather the most relevant data about the movement
of the tracked object and its next-step location, a Kalman-filter based approach for
prediction of next state of an object in order to increase accuracy has been proposed.
In addition, a dynamic sampling mechanism has been devised to collect the most
relevant data.
Extensive simulations were carried out and results were compared with the existing
approaches to prove that HHCM and PAD make significant improvements in energy
conservation. To the best of my knowledge the framework developed in unique and
novel, which can predicts the destination of the moving object without any prior historic
knowledge of the moving object
Emerging Communications for Wireless Sensor Networks
Wireless sensor networks are deployed in a rapidly increasing number of arenas, with uses ranging from healthcare monitoring to industrial and environmental safety, as well as new ubiquitous computing devices that are becoming ever more pervasive in our interconnected society. This book presents a range of exciting developments in software communication technologies including some novel applications, such as in high altitude systems, ground heat exchangers and body sensor networks. Authors from leading institutions on four continents present their latest findings in the spirit of exchanging information and stimulating discussion in the WSN community worldwide
Statistical models for energy-efficient selective communications in sensor networks
An inherent characteristic of Wireless Sensor Networks is their ability to operate
with autonomy when sensor node devices are resource-constrained. Optimizing energy
consumption with the goal of achieving longer sensor network lifetime is a major challenge.
This thesis focuses on energy-efficient strategies based on the reduction of communication processes, the most energy expensive tasks by far. In particular, we analyze selective communication policies that allow sensor nodes to save energy resources at the same time that can assure the quantity and quality of the transmitted information.
This thesis proposes selective communication strategies for energy-constrained Wireless
Sensor Networks, which are based on statistical models of the information flowing through the nodes. Assuming that messages are graded according to an importance/priority value (and whose traffic can be statistically modeled) and that the energy consumption patterns of each individual node are known (or can be estimated), the design and evaluation of optimal selective communication policies that maximize the quality of the information arriving to destination along the network lifetime are analyzed. The problem is initially stated from a decision theory perspective and later reformulated as a dynamic programming problem (based on Markov Decision Processes). The total importance sum of the transmitted, forwarded
or finally delivered messages are used as performance measures to design optimal
transmission policies. The proposed solutions are fairly simple and based on forwarding thresholds whose values can be adaptively estimated. Simulated numerical tests, including a target tracking scenario, corroborate the analytical claims and reveal that significant energy saving can be obtained to enlarge sensor network lifetime when implementing the proposed schemes
Indoor positioning system for wireless sensor networks
Tese de Doutoramento - Programa Doutoral em Engenharia Electrónica e ComputadoresPositioning technologies are ubiquitous nowadays. From the implementation of the
global positioning system (GPS) until now, its evolution, acceptance and spread has been
unanimous, due to the underlying advantages the system brings. Currently, these systems are
present in many different scenarios, from the home to the movie theatre, at work, during a
walk in the park. Many applications provide useful information, based on the current position
of the user, in order to provide results of interest.
Positioning systems can be implemented in a wide range of contexts: in hospitals to
locate equipment and guide patients to the necessary resources, or in public spaces like
museums, to guide tourists during visits. They can also be used in a gymnasium to point the
user to his next workout machine and, simultaneously, gather information regarding his
fitness plan. In a congress or conference, the positioning system can be used to provide
information to its participants about the on-going presentations. Devices can also be
monitored to prevent thefts.
Privacy and security issues are also important in positioning systems. A user might not
want to be localized or its location to be known, permanently or during a time interval, in
different locations. This information is therefore sensitive to the user and influences directly
the acceptance of the system itself.
Concerning outdoor systems, GPS is in fact the system of reference. However, this
system cannot be used in indoor environment, due to the high attenuation of the satellite
signals from non-line-of-sight conditions. Another issue related to GPS is the power
consumption. The integration of these devices with wireless sensor networks becomes
prohibitive, due to the low power consumption profile associated with devices in this type of
networks. As such, this work proposes an indoor positioning system for wireless sensor
networks, having in consideration the low energy consumption and low computational
capacity profile.
The proposed indoor positioning system is composed of two modules: the received
signal strength positioning module and the stride and heading positioning module. For the
first module, an experimental performance comparison between several received signal
strength based algorithms was conducted in order to assess its performance in a predefined indoor environment. Modifications to the algorithm with higher performance were
implemented and evaluated, by introducing a model of the effect of the human body in the
received signal strength.
In the case of the second module, a stride and heading system was proposed, which
comprises two subsystems: the stride detection and stride length estimation system to detect
strides and infer the travelled distance, and an attitude and heading reference system to
provide the full three-dimensional orientation stride-by-stride.
The stride detection enabled the identification of the gait cycle and detected strides
with an error percentage between 0% and 0.9%. For the stride length estimation two methods
were proposed, a simplified method, and an improved method with higher computational
requirements than the former. The simplified method estimated the total distance with an error
between 6.7% and 7.7% of total travelled distance. The improved method achieved an error
between 1.2% and 3.7%. Both the stride detection and the improved stride length estimation
methods were compared to other methods in the literature with favourable results.
For the second subsystem, this work proposed a quaternion-based complementary
filter. A generic formulation allows a simple parameterization of the filter, according to the
amount of external influences (accelerations and magnetic interferences) that are expected,
depending on the location that the device is to be attached on the human body. The generic
formulation enables the inclusion/exclusion of components, thus allowing design choices
according to the needs of applications in wireless sensor networks. The proposed method was
compared to two other existing solutions in terms of robustness to interferences and execution
time, also presenting a favourable outcome.Os sistemas de posicionamento fazem parte do quotidiano. Desde a implementação do
sistema GPS (Global Positioning System) até aos dias que correm, a evolução, aceitação e
disseminação destes sistemas foi unânime, derivada das vantagens subjacentes da sua
utilização. Hoje em dia, eles estão presentes nos mais variados cenários, desde o lar até́ à sala
de cinema, no trabalho, num passeio ao ar livre. São várias as aplicações que nos fornecem
informação útil, usando como base a descrição da posição atual, de modo a produzir
resultados de maior interesse para os utilizadores.
Os sistemas de posicionamento podem ser implementados nos mais variados
contextos, como por exemplo: nos hospitais, para localizar equipamento e guiar os pacientes
aos recursos necessários, ou nas grandes superfícies públicas, como por exemplo museus, para
guiar os turistas durante as visitas. Podem ser igualmente utilizados num ginásio para indicar
ao utilizador qual a máquina para onde se deve dirigir durante o seu treino e,
simultaneamente, obter informação acerca desta mesma máquina. Num congresso ou
conferência, o sistema de localização pode ser utilizado para fornecer informação aos seus
participantes sobre as apresentações que estão a decorrer no momento. Os dispositivos
também podem ser monitorizados para prevenir roubos.
Existem também questões de privacidade e segurança associados aos sistemas de
posicionamento. Um utilizador poderá não desejar ser localizado ou que a sua localização seja
conhecida, permanentemente ou num determinado intervalo de tempo, num ou em vários
locais. Esta informação é por isso sensível ao utilizador e influencia diretamente a aceitação
do próprio sistema.
No que diz respeito aos sistemas utilizados no exterior, o GPS (ou posicionamento por
satélite) é de facto o sistema mais utilizado. No entanto, em ambiente interior este sistema não
pode ser usado, por causa da grande atenuação dos sinais provenientes dos satélites devido à
falta de linha de vista. Um outro problema associado ao recetor GPS está relacionado com as
suas características elétricas, nomeadamente os consumos energéticos. A integração destes
dispositivos nas redes de sensores sem fios torna-se proibitiva, devido ao perfil de baixo
consumo associado a estas redes. Este trabalho propõe um sistema de posicionamento para redes de sensores sem fio em
ambiente interior, tendo em conta o perfil de baixo consumo de potência e baixa capacidade
de processamento.
O sistema proposto é constituído por dois módulos: o modulo de posicionamento por
potência de sinal recebido e o módulo de navegação inercial pedestre. Para o primeiro módulo
foi feita uma comparação experimental entre vários algoritmos que utilizam a potência do
sinal recebido, de modo a avaliar a sua utilização num ambiente interior pré-definido. Ao
algoritmo com melhor prestação foram implementadas e testadas modificações, utilizando um
modelo do efeito do corpo na potência do sinal recebido.
Para o segundo módulo foi proposto um sistema de navegação inercial pedestre. Este
sistema é composto por dois subsistemas: o subsistema de deteção de passos e estimação de
distância percorrida; e o subsistema de orientação que fornece a direção do movimento do
utilizador, passo a passo.
O sistema de deteção de passos proposto permite a identificação das fases da marcha,
detetando passos com um erro entre 0% e 0.9%. Para o sistema de estimação da distância
foram propostos dois métodos: um método simplificado de baixa complexidade e um método
melhorado, mas com maiores requisitos computacionais quando comparado com o primeiro.
O método simplificado estima a distância total com erros entre 6.7% e 7.7% da distância
percorrida. O método melhorado por sua vez alcança erros entre 1.2% e 3.7%. Ambos os
sistemas foram comparados com outros sistemas da literatura apresentando resultados
favoráveis.
Para o sistema de orientação, este trabalho propõe um filtro complementar baseado em
quaterniões. É utilizada uma formulação genérica que permite uma parametrização simples do
filtro, de acordo com as influências externas (acelerações e interferências magnéticas) que são
expectáveis, dependendo da localização onde se pretende colocar o dispositivo no corpo
humano. O algoritmo desenvolvido permite a inclusão/exclusão de componentes, permitindo
por isso liberdade de escolha para melhor satisfazer as necessidades das aplicações em redes
de sensores sem fios. O método proposto foi comparado com outras soluções em termos de
robustez a interferências e tempo de execução, apresentando também resultados positivos
Interference Mitigation and Localization Based on Time-Frequency Analysis for Navigation Satellite Systems
Interference Mitigation and Localization
Based on Time-Frequency Analysis for
Navigation Satellite SystemsNowadays, the operation of global navigation satellite systems (GNSS) is imperative across a multitude of applications worldwide. The increasing reliance on accurate positioning and timing information has made more serious than ever the consequences of possible service outages in the satellite navigation systems. Among others, interference is regarded as the primary threat to their operation. Due the recent proliferation of portable interferers, notably jammers, it has now become common for GNSS receivers to endure simultaneous attacks from multiple sources of interference, which are likely spatially distributed and transmit different modulations.
To the best knowledge of the author, the present dissertation is the first publication to investigate the use of the S-transform (ST) to devise countermeasures to interference. The original contributions in this context are mainly:
• the formulation of a complexity-scalable ST implementable in real time as a
bank of filters;
• a method for characterizing and localizing multiple in-car jammers through
interference snapshots that are collected by separate receivers and analysed
with a clever use of the ST;
• a preliminary assessment of novel methods for mitigating generic interference
at the receiver end by means the ST and more computationally efficient variants of the transform.
Besides GNSSs, the countermeasures to interference proposed are equivalently applicable to protect any direct-sequence
spread spectrum (DS-SS) communication
Computational Intelligence Algorithms for Optimisation of Wireless Sensor Networks
Recent studies have tended towards incorporating Computation Intelligence,
which is a large umbrella for all Machine Learning and Metaheuristic
approaches into wireless sensor network (WSN) applications
for enhanced and intuitive performance. Meta-heuristic optimisation
techniques are used for solving several WSN issues such as energy
minimisation, coverage, routing, scheduling and so on. This research
designs and develops highly intelligent WSNs that can provide the
core requirement of energy efficiency and reliability. To meet these
requirements, two major decisions were carried out at the sink node
or base station. The first decision involves the use of supervised and
unsupervised machine learning algorithms to achieve an accurate decision
at the sink node. This thesis presents a new hybrid approach
for event (fire) detection system using k-means clustering on aggregated
fire data to form two class labels (fire and non-fire). The resulting
data outputs are trained and tested by the Feed Forward Neural
Network, Naive Bayes, and Decision Trees classifier. This hybrid approach
was found to significantly improve fire detection performance
against the use of only the classifiers. The second decision employs
a metaheuristic approach to optimise the solution of WSNs clustering
problem. Two metaheuristic-based protocols namely the Dynamic
Local Search Algorithm for Clustering Hierarchy (DLSACH) and Heuristics
Algorithm for Clustering Hierarchy (HACH) are proposed to achieve
an evenly balanced energy and minimise the net residual energy of
each sensor nodes. This thesis proved that the two protocols outperforms
state-of-the-art protocols such as LEACH, TCAC and SEECH
in terms of network lifetime and maintains a favourable performance
even under different energy heterogeneity settings
Telecommunications Networks
This book guides readers through the basics of rapidly emerging networks to more advanced concepts and future expectations of Telecommunications Networks. It identifies and examines the most pressing research issues in Telecommunications and it contains chapters written by leading researchers, academics and industry professionals. Telecommunications Networks - Current Status and Future Trends covers surveys of recent publications that investigate key areas of interest such as: IMS, eTOM, 3G/4G, optimization problems, modeling, simulation, quality of service, etc. This book, that is suitable for both PhD and master students, is organized into six sections: New Generation Networks, Quality of Services, Sensor Networks, Telecommunications, Traffic Engineering and Routing
A Survey on Reservoir Computing and its Interdisciplinary Applications Beyond Traditional Machine Learning
Reservoir computing (RC), first applied to temporal signal processing, is a
recurrent neural network in which neurons are randomly connected. Once
initialized, the connection strengths remain unchanged. Such a simple structure
turns RC into a non-linear dynamical system that maps low-dimensional inputs
into a high-dimensional space. The model's rich dynamics, linear separability,
and memory capacity then enable a simple linear readout to generate adequate
responses for various applications. RC spans areas far beyond machine learning,
since it has been shown that the complex dynamics can be realized in various
physical hardware implementations and biological devices. This yields greater
flexibility and shorter computation time. Moreover, the neuronal responses
triggered by the model's dynamics shed light on understanding brain mechanisms
that also exploit similar dynamical processes. While the literature on RC is
vast and fragmented, here we conduct a unified review of RC's recent
developments from machine learning to physics, biology, and neuroscience. We
first review the early RC models, and then survey the state-of-the-art models
and their applications. We further introduce studies on modeling the brain's
mechanisms by RC. Finally, we offer new perspectives on RC development,
including reservoir design, coding frameworks unification, physical RC
implementations, and interaction between RC, cognitive neuroscience and
evolution.Comment: 51 pages, 19 figures, IEEE Acces
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