In order to reduce the data processing load on bs and efficiently distinguish the authenticity of archived data, izadi et al. Varshney, geographic routing in wireless ad hoc networks, book chapter, guide to wireless ad hoc. Machine learning models and algorithms for big data. A new data fusion algorithm for wireless sensor networks. Chapters are written by several of the leading researchers exclusively for this book. Data fusion and collaborative state estimation in wireless sensor networks hiba haj chhade to cite this version. It gives clear guidance on the conceptual and implementation issues involved in the design and operation of multicamera networks, as well as presenting the stateoftheart in hardware, algorithms and system development. He is currently preparing two books titled initial investigation of mobile phone positioning technology and sound sensing and localization technologies.
A study on data fusion of wireless sensor networks security. This chapter deals with a wireless sensor and actuator network wsan and its main characteristics. A statistical signal processing perspective control, robotics and sensors book online at best prices. The iet shop data fusion in wireless sensor networks. The distinguishing aspect of our work is the novel use of fuzzy. View ahmed abdelgawad s profile on linkedin, the worlds largest professional community. A data fusion method in wireless sensor networks ncbi. We first enumerate and explain different classification schemes for data fusion. Gaucho project aspires at designing a novel distributed and heterogeneous. The effective use of data fusion in sensor networks is not new and has had extensive application to surveillance, security, traffic control, health care, environmental and industrial monitoring in the last decades. Data fusion and collaborative state estimation in wireless sensor networks. Read resourceaware data fusion algorithms for wireless sensor networks by ahmed abdelgawad available from rakuten kobo. Resourceaware data fusion algorithms for wireless sensor. Energy management in wireless sensor networks discusses this unavoidable issue in the application of wireless sensor networks wsn.
This edited book has dealt with data fusion in wireless sensor networks wsns from a statistical signalprocessing perspective. An intelligent data gathering schema with data fusion. Therefore, energy consumption in wireless sensor networks is one of the most challenging problems in practice. Following the latest developments in computer and communication technologies, everyday objects are becoming smarter, as ubiquitous connectivity and modern sensors allow them to communicate with each other. The state of the art of sensor networks written by an international team of recognized experts in sensor networks from prestigious organizations such as motorola, fujitsu, the massachusetts institute of technology, cornell university, and the university of illinois, handbook of sensor networks. A witnessbased approach for data fusion assurance in. These techniques can be used in centralized and distributed systems to overcome sensor failure, technological limitation, and spatial and. In wireless sensor networks, sensor nodes are spread randomly over the coverage area to collect information of interest. This book discusses topics in missionoriented sensor networks and presents novel theoretical and practical ideas, which led to the development of solid foundations for the design, analysis, and implementation of energyefficient, reliable, and secure missionoriented sensor network applications.
In 15, a variable weightbased fuzzy data fusion algorithm is proposed. This type of data fusion process inputs and outputs raw data. Data fusion improves the coverage of wireless sensor. This book is the definitive reference in multicamera networks. The algorithms described in this book are evaluated with simulation and experimental. The integration of data and knowledge from several sources is known as data fusion. In this paper, we present a fuzzybased data fusion approach for wsn with the. This book describes the advanced tools required to design stateoftheart inference algorithms for inference in wireless sensor networks. The distinguishing aspect of our work is the novel use of fuzzy membership functions and rules in the design of cost functions for the routing objectives considered in this work. Pdf a data fusion method in wireless sensor networks. Data fusion and collaborative state estimation in wireless. Security mechanism of transmission encryption of network is introduced to protect the security of data. Written for the signal processing, communications, sensors and information fusion research communities, it covers the emerging area of data fusion in wireless sensor networks.
Data fusion, target detection, coverage, performance limits, wireless sensor network 1. The proposed algorithm can greatly increase the safety and accuracy of data fusion, improve communication efficiency, save energy of sensor node, suit different application fields, and deploy environments. This book presents machine learning models and algorithms to address big data classification problems. Publications of professor yonghe liu, research areas.
However, such a high overhead is not acceptable in sensor networks based on embedded and wireless platforms. This paper summarizes the state of the data fusion field and describes the most relevant studies. Handling sensing data errors and uncertainties in wsn while maximizing network lifetime are important issues in the design of applications and protocols for wireless sensor networks. The use of hesitant fuzzy entropy hfe to the consumption of energy in wsn is novel. In this paper, we have presented a fuzzybased method for data fusion. Secure data fusion in wireless multimedia sensor networks. With the ability to monitor a vast range of physiological parameters, combined with wireless technology, wireless sensor networks and the internet of things, wearable sensors are revolutionising the field of digital health monitoring. Wireless sensor networks can be used to monitor the condition of civil infrastructure and related geophysical processes close to real time, and over long periods through data logging, using appropriately interfaced sensors. Emerging machine learning technology provides a novel direction for data fusion and makes it more available and intelligent. To guarantee efficiency and durability in a network, the science must go beyond hardware solutions and seek alternative software solutions that allow for better data control from the source to delivery. Existing machine learning techniques like the decision tree a hierarchical approach, random forest an ensemble hierarchical approach, and deep learning a layered approach are highly suitable for the system that can handle such problems. To save more energy, in network processing such as data fusion is a widely used technique, which, however, may often lead to unbalanced information among nodes in the data fusion tree. Varshney, distributed detection and decision fusion with applications to wireless sensor networks, integrated tracking, classification, and sensor management.
Novel features of the text, distributed throughout, include workable solutions, demonstration systems and case studies of the design and application of wireless sensor networks wsns based on the firsthand research and development experience of the author, and the chapters on real applications. A new data fusion algorithm for wireless sensor networks inspired. Over the past decade, there has been a prolific increase in the research, development and commercialisation of wireless sensor networks wsns and their associated technologies. Wireless sensor networks are used to monitor wine production, both in the field and the cellar. Data fusion is a research area that is growing rapidly due to the fact that it provides means for combining pieces of information coming from different sourcessensors, resulting in ameliorated overall system performance improved decision making, increased detection capabilities, diminished number of false alarms, improved reliability in various situations at hand with respect to separate. Wireless sensor networks presents a comprehensive and tightly organized compilation of chapters that surveys many of the exciting research developments taking place in this field. The algorithm developed under this framework transmits effective volume of sensor data in different. Pdf data fusion techniques in wireless sensor networks. A statistical signal processing perspective book chapter, 2019. Wsns have found application in a vast range of different domains, scenarios and disciplines. This book introduces resourceaware data fusion algorithms to gather and combine data from multiple sources e. This paper gives a survey on classical data fusion in wireless sensor networks from the following aspects.
In idgsdf, we adopt a neural network to conduct data fusion to improve network performance. Willett decentralized detection via running consensus data fusion in wireless sensor networks. Introduction recent years have witnessed the deployments of wireless sensor networks wsns for many critical applications such as security surveillance 16, environmental monitoring 25, and target detectiontracking 21. This book addresses the tremendous progress made over the last few decades in algorithm development and mathematical analysis for filtering, multitarget multi sensor tracking, sensor management and control, and target classification. The term data aggregation has become popular in the wireless sensor network com munity as a synonym for information fusion kalpakis et al. Lowlevel data fusion combines several sources of raw data to produce new raw data. This book constitutes the refereed proceedings of the 11th china conference on wireless sensor networks, cwsn 2017, held in tianjin, china, in october 2017. On the other hand, data fusion can effectively decrease data redundancy, reduce the amount of data transmission and energy consumption in the.
Novel features of the text, distributed throughout, include workable solutions, demonstration systems and case studies of the design and application of wireless. Data fusion at this level is conducted immediately after the data are gathered from the sensors. Resourceaware data fusion algorithms for wireless sensor networks. Distributed wireless sensor networks is a collection of embedded sensor devices with networking capabilities. His research interests include visual information processing, embedded systems, wireless sensor networks, reconfigurable computing, the development of realworld computer systems, largescale data gathering in wireless multimedia sensor systems, big data analytics for sensor networks, the multimedia internetofthings, and the internet of vehicles. Algorithms and architectures tackles important challenges and presents the latest trends and. A wireless sensor network wsn in its simplest form can be defined as a network of devices denoted as nodesthat can sense the environment and communicate the information gathered from the monitored field through wireless links. These methods and algorithms are presented using three different. Written for the signal processing, communications, sensors and information fusion research communities, it covers the emerging area of data fusion in. The wireless sensor network wsn is mainly composed of a large number of sensor nodes that are equipped with limited energy and resources. Data fusion with desired reliability in wireless sensor. If such data dependencies are not accounted for, the fusion algorithm, may suffer from overunder. Energyefficient and reliable transmission of sensory information is a key problem in wireless sensor networks.
Authors address many of the key challenges faced in the design, analysis and deployment of wireless sensor networks. As an important element of internet of things, wireless sensor networks wsn are composed of many compact microsensors. Wireless sensor networks presents the latest practical solutions to the design issues presented in wirelesssensornetworkbased systems. Pdf the success of a wireless sensor network wsn deployment strongly. Many strategies have been devised over the years for improving performance of wireless sensor networks with special consideration to energy efficiency. In this paper, we present a novel level based path. Integrated tracking, classification, and sensor management. Wireless sensor networks wsns can be defined as a selfconfigured and infrastructureless wireless networks to monitor physical or environmental conditions, such as temperature, sound, vibration, pressure, motion or pollutants and to cooperatively pass their data through the network to a main location or sink where the data can be observed and analysed. A unique guide to the state of the art of tracking, classification, and sensor management.
These techniques can be used in centralized and distributed systems to overcome sensor failure, technological limitation, and spatial and temporal coverage problems. Data fusion is the process of integrating multiple data sources to produce more consistent, accurate, and useful information than that provided by any individual data source data fusion processes are often categorized as low, intermediate, or high, depending on the processing stage at which fusion takes place. In this article, we present an intelligent data gathering schema with data fusion called idgsdf. Data fusion based on node trust evaluation in wireless. Wireless sensor and actuator networks sciencedirect. Due to the advantage of data fusion in deleting redundant information and extending lifetime of network, data fusion has become one of the important ways of effectively relieving the bottleneck of wireless sensor networks resources, which has been widely used in wireless sensor networks.