Kernel-based Machine Learning for Tracking and Environmental Monitoring in Wireless Sensor Networkds PDF Download
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Author: Sandy Mahfouz
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Languages : en
Pages : 0
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Book Description
This thesis focuses on the problems of localization and gas field monitoring using wireless sensor networks. First, we focus on the geolocalization of sensors and target tracking. Using the powers of the signals exchanged between sensors, we propose a localization method combining radio-location fingerprinting and kernel methods from statistical machine learning. Based on this localization method, we develop a target tracking method that enhances the estimated position of the target by combining it to acceleration information using the Kalman filter. We also provide a semi-parametric model that estimates the distances separating sensors based on the powers of the signals exchanged between them. This semi-parametric model is a combination of the well-known log-distance propagation model with a non-linear fluctuation term estimated within the framework of kernel methods. The target's position is estimated by incorporating acceleration information to the distances separating the target from the sensors, using either the Kalman filter or the particle filter. In another context, we study gas diffusions in wireless sensor networks, using also machine learning. We propose a method that allows the detection of multiple gas diffusions based on concentration measures regularly collected from the studied region. The method estimates then the parameters of the multiple gas sources, including the sources' locations and their release rates.
Author: Sandy Mahfouz
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Languages : en
Pages : 0
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Book Description
This thesis focuses on the problems of localization and gas field monitoring using wireless sensor networks. First, we focus on the geolocalization of sensors and target tracking. Using the powers of the signals exchanged between sensors, we propose a localization method combining radio-location fingerprinting and kernel methods from statistical machine learning. Based on this localization method, we develop a target tracking method that enhances the estimated position of the target by combining it to acceleration information using the Kalman filter. We also provide a semi-parametric model that estimates the distances separating sensors based on the powers of the signals exchanged between them. This semi-parametric model is a combination of the well-known log-distance propagation model with a non-linear fluctuation term estimated within the framework of kernel methods. The target's position is estimated by incorporating acceleration information to the distances separating the target from the sensors, using either the Kalman filter or the particle filter. In another context, we study gas diffusions in wireless sensor networks, using also machine learning. We propose a method that allows the detection of multiple gas diffusions based on concentration measures regularly collected from the studied region. The method estimates then the parameters of the multiple gas sources, including the sources' locations and their release rates.
Author:
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Languages : en
Pages : 0
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Author:
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Languages : en
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Author: Mao, Guoqiang
Publisher: IGI Global
ISBN: 1605663972
Category : Computers
Languages : en
Pages : 526
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Book Description
Wireless localization techniques are an area that has attracted interest from both industry and academia, with self-localization capability providing a highly desirable characteristic of wireless sensor networks. Localization Algorithms and Strategies for Wireless Sensor Networks encompasses the significant and fast growing area of wireless localization techniques. This book provides comprehensive and up-to-date coverage of topics and fundamental theories underpinning measurement techniques and localization algorithms. A useful compilation for academicians, researchers, and practitioners, this Premier Reference Source contains relevant references and the latest studies emerging out of the wireless sensor network field.
Author: S. Y. Kung
Publisher: Cambridge University Press
ISBN: 110702496X
Category : Computers
Languages : en
Pages : 617
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Book Description
Covering the fundamentals of kernel-based learning theory, this is an essential resource for graduate students and professionals in computer science.
Author: Sharvari Tamane
Publisher: Springer Nature
ISBN: 9464631368
Category : Computers
Languages : en
Pages : 1027
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Book Description
This is an open access book. As on date, huge volumes of data are being generated through sensors, satellites, and simulators. Modern research on data analytics and its applications reveal that several algorithms are being designed and developed to process these datasets, either through the use of sequential and parallel processes. In the current scenario of Industry 4.0, data analytics, artificial intelligence and machine learning are being used to support decisions in space and time. Further, the availability of Graphical Processing Units (GPUs) and Tensor Processing Units (TPUs) have enabled to processing of these datasets. Some of the applications of Artificial Intelligence, Machine Learning and Data Analytics are in the domains of Agriculture, Climate Change, Disaster Prediction, Automation in Manufacturing, Intelligent Transportation Systems, Health Care, Retail, Stock Market, Fashion Design, etc. The international conference on Applications of Machine Intelligence and Data Analytics aims to bring together faculty members, researchers, scientists, and industry people on a common platform to exchange ideas, algorithms, knowledge based on processing hardware and their respective application programming interfaces (APIs).
Author: Neena A. George
Publisher:
ISBN:
Category :
Languages : en
Pages : 106
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Book Description
Randomly deployed wireless sensor networks are becoming increasingly viable for applications such as environmental monitoring, battlefield awareness, tracking and smart environments. Such networks can comprise anywhere from a few hundred to thousands of sensor nodes, and these sizes are likely to grow with advancing technology, making scalability a primary concern. Each node in these sensor networks is a small unit with limited resources and localized sensing and communication. Thus, all global tasks must be accomplished through self-organized distributed algorithms, which also lead to improved scalability, robustness and flexibility. In this thesis, we examine the use of distributed algorithms to infer the spatial structure of an extended environment monitored by a self organizing sensor network. Based on its sensing, the network segments the environment into regions with distinct characteristics, thereby inferring a cognitive map of the environment. This, in turn, can be used to answer global queries about the environment efficiently and accurately. We consider distributed machine learning techniques for segmentation. We also present a heuristic for segmenting within boundaries to obtain distinct segments and study the variation of segmentation quality with reconstruction at different node densities and in environments of varying complexity.
Author: Nishchal K. Verma
Publisher: Springer
ISBN: 9811311358
Category : Technology & Engineering
Languages : en
Pages : 679
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Book Description
This book presents selected proceedings of ICCI-2017, discussing theories, applications and future directions in the field of computational intelligence (CI). ICCI-2017 brought together international researchers presenting innovative work on self-adaptive systems and methods. This volume covers the current state of the field and explores new, open research directions. The book serves as a guide for readers working to develop and validate real-time problems and related applications using computational intelligence. It focuses on systems that deal with raw data intelligently, generate qualitative information that improves decision-making, and behave as smart systems, making it a valuable resource for researchers and professionals alike.
Author: Sajeeva Salgadoe
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
The rapid growth in sensors, low-power integrated circuits, and wireless communication standards has enabled a new generation of applications based on ultra-low powered wireless sensor networks. These are employed in many environments including health-care, industrial automation, smart building and environmental monitoring. According to industry experts, by the year 2020, over 20 billion low powered, sensor devices will be deployed and an innumerable number of data objects will be created. The objective of this work is to investigate the feasibility and analyze optimal methods of using low powered wireless characteristics, attributes of communication protocols and machine learning techniques to determine traffic anomalies in low powered networks. Traffic anomalies can be used to detect security violations as well as network performance issues. Both live and simulated data have been used with four machine learning methods, to examine the relationship between performance and the various factors and methods. Several factors including the number of nodes, sample size, noise influence, model aging process and classification algorithm are investigated against performance accuracy using data collected from an operational wireless network, comprising more than one hundred nodes, during a six-month period. An important attribute of this work is that the proposed model is able to implement in any low powered network, regardless of the software and hardware architecture of individual nodes (as long as the network complies with an open standard communication mechanism). Furthermore, the experiment portion of this work includes over 80 independent experiments to evaluate the behaviour of various attributes of low powered networks. Machine learning models trained using carefully selected input features and other factors including adequate training samples and classification algorithm are able to detect traffic anomalies of low powered wireless networks with over 95% accuracy. Furthermore, in this work, a framework for an aggregated classification model has been evaluated and the experiment results confirm a further improvement of the prediction accuracy and a reduction of both false positive and negative rates in comparison to basic classification models.
Author: Tareq Ahram
Publisher: Springer Nature
ISBN: 3030553078
Category : Technology & Engineering
Languages : en
Pages : 634
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Book Description
This book reports on research and developments in human-technology interaction. A special emphasis is given to human-computer interaction, and its implementation for a wide range of purposes such as healthcare, aerospace, telecommunication, and education, among others. The human aspects are analyzed in detail. Timely studies on human-centered design, wearable technologies, social and affective computing, augmented, virtual and mixed reality simulation, human rehabilitation and biomechanics represent the core of the book. Emerging technology applications in business, security, and infrastructure are also critically examined, thus offering a timely, scientifically-grounded, but also professionally-oriented snapshot of the current state of the field. The book is based on contributions presented at the 3rd International Conference on Human Interaction and Emerging Technologies: Future Applications, IHIET 2020, held on August 27-29, 2020. It offers a timely survey and a practice-oriented reference guide to researchers and professionals dealing with design and/or management of the new generation of service systems.
