Data fusion techniques in wireless sensor networks book

Advanced techniques have been introduced, including data aggregation or data fusion, in. In this paper, we bridge this gap by investigating the fundamental realtime detection performance of largescale sensor networks under stochastic sensing models. A handbook on recent advancements and the state of the art in array processing and sensor networks. At present, the resolved strategies mainly focus on nonuniform node distribution and adjusting transmission power.

Principles and techniques for sensor data fusion 1. The loss of battery or energy may lead to failure of the entire network 14. The book contains chapters with different methods of sensor. Nowadays, wireless sensor networks wsns emerge as an active research area in which challenging topics involve energy consumption, routing algorithms, selection of sensors location according to a given premise, robustness, e ciency, and so forth. 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. Wireless sensor networks brings together multiple strands of research in the design of wsns, mainly from software engineering, electronic engineering, and wireless communication perspectives, into an overarching examination of the subject, benefiting students, field engineers, system developers and it professionals. Sensor network localization systems and network management techniques are covered in part v. 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.

In this paper, we propose an approach that allows the implementation of parallel data fusion techniques in ieee 802. These techniques can be used in centralized and distributed systems to overcome sensor failure, technological limitation, and spatial and temporal coverage problems. Varshney, multiobjective evolutionary algorithms for wireless sensor network design, multiobjective optimization in computational intelligence. Sensor fusion foundation and applications intechopen. Wireless sensor and actuator networks sciencedirect. 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. Wireless sensor networks are used to monitor wine production, both in the field and the cellar. 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. Oct, 2015 kalpana b, sangeetha r 2011 a novel framework for target tracking and data fusion in wireless sensor networks using kernel based learning algorithm. Energy efficient data fusion in wireless sensor networks are necessary because, the sensor nodes are battery operated, and it is important to keep track of the energy issues 12.

It is an interdisciplinary domain that consists of wireless and wired communication, algorithms and protocols as well as energy sources to supply these networks. Wireless sensor network wsn is a kind of energy constrained network, by using data fusion technology, the elimination of redundant data, can save energy, prolong the network life purpose. When having a look at wireless sensor networks, a huge number of tiny devices equipped with lowcost radio transceivers form a selforganizing ad hoc network. Data fusion in wireless sensor networks using fuzzy 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. Wsns measure environmental conditions like temperature, sound, pollution levels, humidity, wind, and so on. International journal of computer and information security, issn 1947 5500, 1.

Common procedures of this level include spatiotemporal alignment, association, correlation, clustering or grouping techniques, state estimation, the removal of false positives, identity fusion, and the combining of features that were extracted from images. Handbook on array processing and sensor networks book. 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. For the wsns, the innetwork preprocessing techniques could lead to saving in. 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. Data fusion generally outperforms decision fusion in accuracy at the price of a higher communication cost. Therefore, energy consumption in wireless sensor networks is one of the most. A new data fusion algorithm for wireless sensor networks inspired. Handbook on array processing and sensor networks wiley.

Introduction the problem of combining observations into a coherent description of the world is basic to perception. Impact of data fusion on realtime detection in sensor. It discusses the social, regulatory, and design considerations specific to these domains. Data fusion based on node trust evaluation in wireless. In proceedings of the 4th international conference on networking icn 2005, p. These methods and algorithms are presented using three different categories. Wireless sensor networks wsn the many tiny principle.

Wireless sensor network wsn architecture and applications. Impact of data fusion on realtime detection in sensor networks rui tan 1guoliang xing2 benyuan liu3 jianping wang 1city university of hong kong, hksar 2michigan state university, usa 3university of massachusetts lowell, usa abstractrealtime detection is an important requirement of many missioncritical wireless sensor network applications. The wsn is built with nodes that are used to observe the surroundings like temperature, humidity, pressure, position, vibration, sound etc. A data fusion method in wireless sensor networks article pdf available in sensors 152. Data fusion techniques for auto calibration in wireless. With the number of nodes increasing, the total number of nodes grows exponentially, and there is a need to improve physical conditions. Application of compressive sensing techniques in distributed. However, these techniques consume excessive resources such as energy and channel capacity and increase network latency.

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. Impact of data fusion on realtime detection in sensor networks. An approach to implement data fusion techniques in. 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 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. The integration of data and knowledge from several sources is known as data fusion. Surveillance tracking systems, disaster management, medical systems, transportation, business intelligence, environmental monitoring systems, elearning and virtual campuses, smart grids and. Data fusion in sensors is defined as the process 1. Hybrid data and decision fusion techniques for modelbased. The focus of this work is to provide some hybrid data and decision fusion techniques for the estimation of parameters of pde models in wireless sensor networks. Hall and llinas provided the following wellknown definition of data fusion.

Therefore, we propose a novel statistical information fusion method not only for structural chain and treebased sensor networks, but also for unstructured bidirectional graph noisy wireless sensor networks in sg environments. An approach to implement data fusion techniques in wireless. Many strategies have been devised over the years for improving performance of wireless sensor networks with special consideration to energy efficiency. Healthcare, wellness and environmental applications explores the key aspects of sensor technologies, covering wired, wireless, and discrete sensors for the specific application domains of healthcare, wellness and environmental sensing. Gaucho project aspires at designing a novel distributed and heterogeneous. Sensor data fusion article about sensor data fusion by. In 15, a variable weightbased fuzzy data fusion algorithm is proposed. As an important element of internet of things, wireless sensor networks wsn are composed of many compact microsensors. Energy holes problem is one of key issues for wireless sensor networks wsn. Data fusion based on node trust evaluation in wireless sensor. Few of the energy efficient data fusion techniques have already been listed below 3. This book introduces resourceaware data fusion algorithms to gather and combine. Handbook on array processing and sensor networks is the first book of its kind and will appeal to researchers, professors, and graduate students in array processing, sensor networks, advanced signal processing, and networking.

Kalpana b, sangeetha r 2011 a novel framework for target tracking and data fusion in wireless sensor networks using kernel based learning algorithm. Sep 12, 2010 a middleware that enables the application to cache collected data in the network using data centric storage would empower applications with powerful mechanisms. The book explores some of the latest practices and research works in the area of sensor fusion. Wireless sensor networks presents the latest practical solutions to the design issues presented in wirelesssensornetworkbased systems. Study on data fusion techniques in wireless sensor networks. This book provides some novel ideas, theories, and solutions related to the research areas in the field of sensor fusion. Research on the wireless sensor network data fusion. Data fusion with desired reliability in wireless sensor networks abstract. Resourceaware data fusion algorithms for wireless sensor networks. Wireless sensor networks and data fusion techniques data analytics applications. This paper summarizes the state of the data fusion field and describes the most relevant studies.

Data fusion in sensor networks is founded on the methods. In this paper, we present a fuzzybased data fusion approach for wsn with the aim. This paper focuses on the challenges involved in supporting fusion applications in wireless ad hoc sensor networks wasn. Handbook on array processing and sensor networks provides readers with a collection of tutorial articles contributed by worldrenowned experts on recent advancements and the state of the art in array processing and sensor networks.

Pdf a data fusion method in wireless sensor networks. In this paper, we present a novel level based path. Data fusion techniques for auto calibration in wireless sensor networks maen takruri 1, subhash challa 2, ramah yunis 1 centre for realtime information networks crin university of technology, sydney, australia 2 nicta victoria research laboratory, australia email. The distinguishing aspect of our work is the novel use of fuzzy. Data fusion is a wellknown technique that can be useful for the enhancement of data quality and for the maximization of wsn lifetime. Data fusion privacy preserving algorithm based on failure. Data fusion improves the coverage of wireless sensor networks. 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. 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. In this paper, we present a framework for sensor data fusion and then postulate a set of principles based on experiences from building systems.

We first enumerate and explain different classification schemes for data fusion. 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. In order to improve the data fusion accuracy and the fusion efficiency of intermediate fusion nodes, this paper presents a new data fusion method for privacy protection in. These methods and algorithms are presented using three different. This chapter deals with a wireless sensor and actuator network wsan and its main characteristics. Pdf data fusion techniques in wireless sensor networks. A clustering based fuzzy logic theory data fusion method was proposed in 10 to examine the influence of inaccurate wsn observation values and different fuzzy.

Sabzevari, introducing a sensor network for advanced driver assistance systems using fuzzy logic and sensor data fusion techniques, adhoc and sensor wireless networks, vol. A middleware that enables the application to cache collected data in the network using data centric storage would empower applications with powerful mechanisms. Developing a fusion application is challenging in general, for the fusion operation typically requires timecorrelation and synchronization of. 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. 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. This book reports advanced techniques that add to stateoftheart in data centric storage, with a focus on quality of service in data storage, data encoding and data transport.

The term data aggregation has become popular in the wireless sensor network com munity as a synonym for information fusion kalpakis et al. Computer engineering, university of shanghai for science and technology. These are similar to wireless ad hoc networks in the sense that. Data fusion, target detection, coverage, performance limits, wireless sensor network 1. Lowlevel data fusion combines several sources of raw data to produce new raw data. In this paper, we have presented a fuzzybased method for data fusion. In 57, the application of cs for compressed data gathering, distributed compression and source localization has been brie. This book introduces resourceaware data fusion algorithms to gather and combine data from multiple sources e. Authors address many of the key challenges faced in the design, analysis and deployment of wireless sensor networks. This edited book has dealt with data fusion in wireless sensor networks wsns from a statistical signalprocessing perspective. Lecture notes in electrical engineering book 118 thanks for sharing. Data fusion with desired reliability in wireless sensor. Resourceaware data fusion algorithms for wireless sensor. Information fusion for data dissemination in selforganizing wireless sensor networks.

Sensor fusion foundation and applications comprehensively covers the foundation and applications of sensor fusion. Surveillance tracking systems, disaster management, medical systems, transportation, business intelligence, environmental monitoring systems, elearning and virtual campuses, smart grids and energy efficiency systems, etc. Currently, wsn wireless sensor network is the most standard services employed in commercial and industrial applications, because of its technical development in a processor, communication, and lowpower usage of embedded computing devices. The final part focuses on target detection and habitat monitoring applications of sensor networks.

Data fusion improves the coverage of wireless sensor. Jan 22, 2010 handbook on array processing and sensor networks is the first book of its kind and will appeal to researchers, professors, and graduate students in array processing, sensor networks, advanced signal processing, and networking. Energyefficient and reliable transmission of sensory information is a key problem in wireless sensor networks. Minimizing network traffic and consequently overall energy consumption, scalability, etc. Data fusion in wireless sensor network can realize different protocol layers, based on the introduction of wireless sensor network and data fusion related knowledge, prove that the arithmetic mean method. Chapters are written by several of the leading researchers exclusively for this book. An improved data fusion method iickpad for privacy. In particular, for signal path loss exponent of k typically between 2. Data mining and fusion techniques for wsns as a source of the. A fuzzy data fusion solution to enhance the qos and the energy consumption in wireless sensor networks. Part iii is on data storage and manipulation in sensor networks, and part iv deals with security protocols and mechanisms for wireless sensor networks. However, some costs are incurred by nonuniform node distribution. A strategy for avoiding energy holes based on data fusion.