Written for the signal processing, communications, sensors and information fusion research communities, it covers the emerging area of data fusion in wireless sensor networks. Eventually each node has all the data in the network, and thus can act as a fusion center to obtain ml. Wsns measure environmental conditions like temperature, sound, pollution levels, humidity, wind, and so on. With recursive least square method in the algorithm, the dynamic model of sensor was built up, and the achievement of data fusion between sensors measure value and estimate value increased the measure precision. Data fusion algorithms in clusterbased wireless sensor. A bayesian approach to data fusion in sensor networks zhiyuan weng, petar m.
Resourceaware data fusion algorithms for wireless sensor. With the feature of large amount of data for wireless sensor networks, high data redundancy and low energy of nodes, we propose the sensor nodes data fusion. 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. Varshney, geographic routing in wireless ad hoc networks, book chapter, guide to wireless ad hoc networks, springer, 2008. Theory and practice incorporates concepts, processes, methods, and approaches in data fusion that can help you with integrating df mathematics. Algorithms for position and data recovery in wireless. For the sake of avoiding the data abundance and balancing the energy consumption in wireless sensor networks, a data fusion clustering hierarchy based on data fusion chdf is proposed.
The term data aggregation has become popular in the wireless sensor network com munity as a synonym for information fusion kalpakis et al. Download for offline reading, highlight, bookmark or take notes while you read resourceaware data fusion algorithms for wireless sensor networks. The term uncertainty reduction in this case can mean more accurate, more complete, or more dependable, or refer to the result of an emerging view, such as stereoscopic vision calculation. There is continuously increasing interest in research on multisensor data fusion technology. Data fusion among the same type of sensors in an active sensor. A new data fusion algorithm for wireless sensor networks. This method can require a large amount of data communication, storage memory, and book keeping overhead. The wireless sensor network wsn is mainly composed of a large number of sensor nodes that are equipped with limited energy and resources. The iet shop data fusion in wireless sensor networks. This book introduces resourceaware data fusion algorithms to gather and combine data from multiple sources e. On the other hand, data fusion can effectively decrease data redundancy, reduce the amount of data transmission and energy consumption in the. Department of electrical and computer engineering stony brook university, stony brook, new york 11794 phone.
Secure data aggregation used for wireless sensor network 6. A new data fusion algorithm for wireless sensor networks inspired. This edited book has dealt with data fusion in wireless sensor networks wsns from a statistical signalprocessing perspective. As the communication consumes a significant part of the energy in wireless networks, ordinary parallel data fusion approaches may expend more energy than serial data fusion techniques, due to the fact that all sensed data is sent to a central node. In 15, a variable weightbased fuzzy data fusion algorithm is proposed. The success of a wireless sensor network wsn deployment strongly depends on the quality of service qos it provides regarding issues such as data accuracy, data aggregation delays and network lifetime maximisation. Algorithms for position and data recovery in wireless sensor. In this section we discussed about some published techniques related to the data fusion. The algorithms described in this book are evaluated with simulation and experimental. Varshney, geographic routing in wireless ad hoc networks, book chapter, guide to wireless ad hoc. Data fusion improves the coverage of wireless sensor. In this paper, we present a novel level based path. Data fusion in wireless sensor networks a statistical. Because dempsters rule of combination can be problematic when dealing with conflicting data, there are numerous issues that make data fusion a challenging.
A data fusion method in wireless sensor networks ncbi. Describes techniques to overcome real problems posed by wireless sensor networks deployed in circumstances that might interfere with measurements provided, such as strong. Sensor fusion is combining of sensory data or data derived from disparate sources such that the resulting information has less uncertainty than would be possible when these sources were used individually. Data gathering and fusion in sensor networks weipeng chen and jennifer hou. This work proposes an algorithm for hybrid positioning in wireless sensor networks based on data fusion of uwb and inertial information. Path exposure, target location, classification and tracking in sensor networks kousha moaveninejad, and xiangyang li.
We show that data fusion can significantly improve sensing coverage by exploiting the collaboration among sensors. Algorithms and protocols for wireless sensor networks. Resourceaware data fusion algorithms for wireless sensor networks 118 by magdy bayoumi and ahmed abdelgawad 2014, paperback at the best online prices at ebay. 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. A novel clusterbased data fusion algorithm for wireless. 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. Algorithms and protocols for wireless sensor networks november 2008. Written for the signal processing, communications, sensors and information fusion research communities, it covers the emerging area of data fusion in. An algorithm of mobile sensors data fusion tracking for.
Lecture notes in electrical engineering book 118 thanks for sharing. Bougiouklis naval postgraduate school department of electrical engineering 833 dyer road, monterey, ca 93943. Data fusion privacy preserving algorithm based on failure. Datacentric protocols for wireless sensor networks ivan stojmenovic and stephan olariu. The book instills a deeper understanding of the basics of multisensor data fusion as well as a practical knowledge of. Data fusion improves the coverage of wireless sensor networks. In this paper an algorithm of data fusion to track both of nonmaneuvering and maneuvering targets with mobile sensors deployed in an wsn wireless sensor network is proposed and investigated. Discusses information filtering, bayesian approaches, several df rules, image algebra and image fusion, decision fusion, and wireless sensor network wsn multimodality fusion. Request pdf on jan 1, 2012, ahmed abdelgawad and others published resourceaware data fusion algorithms for wireless sensor networks find, read and cite all the research you need on researchgate. These are similar to wireless ad hoc networks in the sense that. Ahmed abdelgawad, magdy bayoumi resourceaware data fusion algorithms for wireless sensor networks published. We focus on sensor deployment and coverage, routing, and sensor fusion. Networked filtering and fusion in wireless sensor networks. Elsewhere the area of statistical signal processing provides a powerful toolbox to attack bothering theoretical and practical problems.
It presents the known methods, algorithms, architectures, and models of information fusion and discusses their applicability in the context of wireless sensor networks wsns. Low complexity indoor localization in wireless sensor. 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. This is especially challenging in data fusion mechanisms, where a small fraction of low quality data in the fusion input may negatively impact the overall fusion result. Wireless sensor networks wsns are formed of various nodes that gather. Clustering based data collection using data fusion in. However, the appearance of conflicting evidence results in a series of problems when conducting fusion using the ds theory. Algorithms and architectures tackles important challenges and presents the latest trends and. Paying particular attention to the wide range of topics that have been covered in recent literature, the text presents the results of a number of typical case studies.
These techniques can be used in centralized and distributed systems to overcome sensor failure, technological limitation, and spatial and. A fuzzy data fusion solution to enhance the qos and the energy. In particular, for signal path loss exponent of k typically between 2. The characteristics such as high reliability, high scalability, fault tolerance, low cost and rapid deployment make wireless sensor networks wsn useful in many military and civilian applications. Novel algorithm for identifying and fusing conflicting data in wireless sensor networks. Data fusion, target detection, coverage, performance limits, wireless sensor network 1. An approach to implement data fusion techniques in. In the field of multi sensor data fusion, the ds theory is a popular method to express and fuse the uncertainty information, and is especially suitable for decision level fusion. With the feature of large amount of data for wireless sensor networks, high data redundancy and low energy of nodes, we propose the sensor nodes data fusion algorithm based on timedriven network data aggregation with the combination of sensor nodes scheduling and batch estimation. This chapter deals with a wireless sensor and actuator network wsan and its main characteristics. Novel algorithm for identifying and fusing conflicting data.
The distinguishing aspect of our work is the novel use of fuzzy. Wireless sensor networks presents the latest practical solutions to the design issues presented in wirelesssensornetworkbased systems. 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. These techniques can be used in centralized and distributed systems to overcome sensor failure, technological limitation, and spatial and temporal coverage problems. Wireless sensor networks presents the latest practical solutions to the design issues presented in wireless sensor networkbased systems. Keywords wireless sensor networks, distributed data fusion, neural. 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. An approach to implement data fusion techniques in wireless. Wireless sensor and actuator networks sciencedirect. Sensor fusion is also known as multi sensor data fusion and is a subset of information fusion. An intelligent data gathering schema with data fusion supported for. Multi sensor was needed while using data fusion technique in wireless sensor networks. Energyefficient data fusion technique and applications in.
Resourceaware data fusion algorithms for wireless sensor networks. Pdf a data fusion method in wireless sensor networks. Sensor fusion deals with merging information from two or more sensors. Magdy a bayoumi this book introduces resourceaware data fusion algorithms that generate inferences by combining data from multiple sourcestechniques useful in centralized and distributed systems to overcome sensor. The algorithms described in this book are evaluated with. From algorithms and architectural design to applications is a robust collection of modern multisensor data fusion methodologies. Algorithms for position and data recovery in wireless sensor networks by lance doherty research project submitted to the department of electrical engineering and computer sciences, university of california at berkeley, in partial satisfaction of the requirements for the degree of master of science, plan ii. The algorithms described in this book are evaluated with simulation and experimental results to show they will maintain data integrity and make data useful and informative. Varshney, multiobjective evolutionary algorithms for wireless sensor network design, multiobjective optimization in computational intelligence. This book describes the advanced tools required to design stateoftheart inference algorithms for inference in wireless sensor networks. Resourceaware data fusion algorithms for wireless sensor networks ebook written by ahmed abdelgawad, magdy bayoumi. Multisensor was needed while using data fusion technique in wireless sensor networks. A data fusion algorithm of single sensor was proposed in the paper. The role of data fusion has been expanding in recent years through the incorporation of pervasive applications, where the physical infrastructure is coupled with information and communication technologies, such as wireless sensor networks for the internet of things iot, ehealth and industry 4.
Wireless sensor network wsn refers to a group of spatially dispersed and dedicated sensors for monitoring and recording the physical conditions of the environment and organizing the collected data at a central location. Novel algorithm for identifying and fusing conflicting. On the other hand, serial data fusion imposes the utilization of routing algorithms. In this edited reference, the authors provide advanced tools for the design, analysis and. Pdf data fusion techniques in wireless sensor networks. Therefore, energy consumption in wireless sensor networks is one of the most challenging problems in practice. Direct fusion is the fusion of sensor data from a set of heterogeneous or homogeneous sensors, soft sensors, and history values of sensor data, while indirect fusion uses information sources like a priori knowledge about the environment and human input. Novel features of the text, distributed throughout, include workable solutions, demonstration systems and case studies of the design and application of wireless. Bayesian approach for data fusion in sensor networks j. Much more sophisticated algorithms for distributed detection, estimation and inference in sensor networks have been studied. Wireless sensor networks, algorithms, routing, coverage, fusion. Wireless sensor networks are used to monitor wine production, both in the field and the cellar.
Wireless sensor data fusion algorithm based on the sensor. Data fusion algorithms of single sensor in wireless sensor. Sep 02, 2005 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. With recursive least square method in the algorithm, the dynamic model of sensor was built up, and the achievement of data fusion between sensor s measure value and estimate value increased the measure precision. Unfortunately, this ratio is heavily dependent on application scenarios. The paper a clusterbased fuzzy fusion algorithm for event detection in heterogeneous wireless sensor networks proposes a clusterbased data fusion algorithm for event detection. The book instills a deeper understanding of the basics of multisensor data fusion as well as a practical knowledge of the problems that can be faced during its execution. We focus on sensor deployment and coverage, routing and sensor fusion. In addition, the gating technique is also applied to solve the problem of msdft mobilesensor data fusion tracking for targets, i. A data fusion method in wireless sensor networks mdpi.
Resourceaware data fusion algorithms for wireless sensor networks 118 by magdy bayoumi and ahmed abdelgawad 2014, paperback at the best online. Algorithms for position and data recovery in wireless sensor networks by lance doherty research project submitted to the department of electrical engineering and computer sciences, university of california at berkeley, in partial satisfaction of the requirements for. A scheme for robust distributed sensor fusion based on. Varshney, image registration using mutual information. Data mining and fusion techniques for wsns as a source of the. The objective of this book is to explain state of the art theory and algorithms into statistical sensor fusion, covering estimation, detection and nonlinear filtering theory with. Gaucho project aspires at designing a novel distributed and. Magdy bayoumi this book introduces resourceaware data fusion algorithms to gather and combine data from multiple sources e. Bayesian approach for data fusion in sensor networks. The authors use k means algorithm to form the nodes into clusters, which can significantly reduce the energy consumption of intracluster communication. Pdf the success of a wireless sensor network wsn deployment strongly. Many studies adopt the metaheuristic algorithm for better routing.
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