A arietvy of factors such as sensor failure or data loss in communication may cause a wsn to produce incorrect data. Mar 25, 2019 a wireless sensor network consists of sensor nodes we will see about this later that are deployed in high density and often in large quantities and support sensing, data processing, embedded computing and connectivity. Sensor fusion also can be centralized or decentralized depending on where the fusion of the data occurs. 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. Information fusion for wireless sensor networks article 9 3 thissectiondiscussescommontermsandfactorsthatmotivateandencouragethepractical use of information fusion in wsns.
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. Wsn nodes have less power, computation and communication compared to manet nodes. The data fusion survivability analysis technology of wireless. 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. Basics of wireless sensor networks wsn classification. A new data fusion algorithm for wireless sensor networks inspired. Methods, concepts, and tools for iq iq in sensor networks and information fusion abstract. Need for energy efficient data fusion in wireless sensor. Distributed signal processing and data fusion methods for. Data mining and fusion techniques for wsns as a source of the big data. A wsn consists of many sensor nodes that cooperate with each other to perform a measurement or monitoring task, in which data are exchanged and shared between neighbours through wireless communication.
The most popular distributed paradigm in wireless sensor networks is in network aggregation. Hall and llinas provided the following wellknown definition of data fusion. Low complexity indoor localization in wireless sensor. A data fusion method in wireless sensor networks ncbi. This is especially problematic in data fusion, where a small fraction of low quality. Impact of data fusion on realtime detection in sensor networks. Decisionlevel fusion takes information from each sensor after it has measured or evaluated a target individually.
Mistica dhas school of computing, sathyabama university chennai, india email. Wireless sensor networks may be considered a subset of mobile adhoc networks manet. The recent developments in engineering, communication and networking has led to new. Application of compressive sensing techniques in distributed. On the other hand, serial data fusion imposes the utilization of routing algorithms. Due to the limitations of some sensor nodes, especially the limited amount of energy, in network data processing, such as data fusion, is very important. Unfortunately, low data quality is a prevalent problem in wsns. Han abstract wireless sensor networks place sensors into an area to collect data and send them back to a base station. First, the definition of data fusion survivability is put forward, and the data fusion model of wsn is constructed. An approach to implement data fusion techniques in wireless. Abstract wireless sensor networks are deployed for the purpose of sensing and monitoring an area of interest. A study on data fusion of wireless sensor networks security. Data fusion in wireless sensor networks yun liu, qingan. Multisensor data fusion in cluster based wireless sensor.
Data fusion utilization for optimizing largescale wireless. Sensorstosink data in wireless sensor networks wsns are typically characterized by correlation along the spatial, semantic, andor temporal dimensions. 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. The loss of battery or energy may lead to failure of the entire network 14.
Data mining and fusion techniques for wsns as a source of the. Energyefficient data fusion technique and applications in. Data fusion methods data compression wireless sensor. 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. In addition, the gating technique is also applied to solve the problem of msdft mobilesensor data fusion tracking for targets, i. 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 design of largescale sensor networks interconnecting various sensor nodes has spurred a great deal of interest due to its wide variety of applications. Structurefree approaches find, read and cite all the research you. Due to the limitations of sensor nodes capabilities, especially the strictly limited energy, in network data processing, such as data fusion which can significantly improve the. Hybrid data and decision fusion techniques for modelbased. A survey thakshila wimalajeewa, senior member, ieee and pramod k varshney, life fellow, ieee abstractin this survey paper, our goal is to discuss recent advances of compressive sensing cs based solutions in wireless sensor networks wsns including the main.
Data fusion in wireless sensor networks wsns can improve the performance of a network by eliminating redundancy and power consumption, ensuring faulttolerance between sensors, and managing. Data fusion, which fuses the collected data before they are sent to the base station, is usually. Data fusion can reduce quantity of data transmission in the network, extend network lifetime by reducing energy. Quality of information in wireless sensor networks. Data fusion improves the coverage of wireless sensor networks. In this paper, we propose an approach that allows the implementation of parallel data fusion techniques in ieee 802. Wireless sensor networks presents the latest practical solutions to the design issues presented in wireless sensor networkbased systems. The name of the game the terminology related to systems, architectures, applications, methods, and theories about the fusion of data from multiple sources is not uni. In 15, a variable weightbased fuzzy data fusion algorithm is proposed.
Wsn is a wireless network that consists of base stations and numbers of nodes wireless sensors. Algorithms, strategies, and applications mohammad abu alsheikh1,2, shaowei lin2, dusit niyato1 and hweepink tan2 1school of computer engineering, nanyang technological university, singapore 639798 2sense and senseabilities programme, institute for infocomm research, singapore 8632. In this phd dissertation we study the problem of continuous object tracking using large. Editorial energyefficient data fusion technique and applications in wireless sensor networks yunliu, 1 qinganzeng, 2 andyinghongwang 3 department of electronic and information engineering, key laboratory of communication and information systems. Sections 4 describe data fusion in wireless sensor networks along with its advantages. This paper proposes a novel, dynamic, selforganizing hesitant fuzzy entropybased opportunistic clustering and data fusion scheme hfecs in order to overcome the energy consumption and network lifetime bottlenecks. A clustering based fuzzy logic theory data fusion method was proposed in 10 to examine the influence of inaccurate wsn observation values and different. Localization is one of important functions in wireless sensor networks wsns. In a centralized situation, data are forwarded to a central location to be correlated and fused. Secure data aggregation used for wireless sensor network 6.
Routing correlated data with fusion cost in wireless sensor networks hong luo, jun luo, yonghe liu, sajal k. And data fusion is commonly regarded as an efficient method that can improve precision of localization. Even when the sensors are properly calibrated at the time of deployment, they develop errors in their readings. In this article, we present an intelligent data gathering schema with data fusion called idgsdf. Distributed signal processing and data fusion methods for large scale wireless sensor network applications dimitris v. Inadditiontobu erover ows, akeysymptom of congestion in wireless sensor networks is a degradation inthe qualityof theradio channelcausedbyan increase in the amount of tra c being sent in other parts of the net. A wireless sensor network is characterized by resource constraint and limited computational capable sensor motes that are powered by battery. Pdf data fusion in wireless sensor networks biljana. An approach to implement data fusion techniques in. The success of a wireless sensor network wsn deployment strongly depends on the quality of service qos it provides regarding issues.
The objective of a wsn is to utilize the data at different locations to enhance the measurement performance. Novel features of the text, distributed throughout, include workable solutions, demonstration systems and case studies of the design and application of wireless. Data fusion techniques for auto calibration in wireless. 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. Application of compressive sensing techniques in distributed sensor networks. The sensornet community has embraced declarative queries as a key programming paradigm for large sets of sensors. Many strategies have been devised over the years for improving performance of wireless sensor networks with special consideration to energy efficiency.
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. Data fusion based on rbf and nonparametric estimation for. The sensor nodes could be equipped with various types of sensors. While all of these abovementioned data fusion methods attempt to achieve more reliable data, none of them address the issue of reducing the number of nodes in multi sensor networks, especially in largescale wireless sensor networks. Wireless sensor networks wsns consist of large number of constrained wireless sensor nodes for the purpose of data gathering. 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. Few of the energy efficient data fusion techniques. Pdf study of data fusion in wireless sensor network kamal kant. These networks are used to monitor physical or environmental conditions like sound, pressure, temperature, and cooperatively pass data through the network to the main location as shown in the figure. Internet of things iot has emerged as a natural evolution of environmental sensing systems such as wireless sensor networks wsns. 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. Ensuring optimal lifetime of a sensor network has always been a research question from the past decade with evidences of various ranges of solutions to mitigate them. Study on data fusion techniques in wireless sensor networks.
This information can be of the same type in homogeneous sensor networks or it. One of the most important challenges of such networks is the distributed management of the huge amount of data produced by sensors in network to reduce data traffic in network and. Information fusion for data dissemination in selforganizing wireless sensor networks. Adistributed database query methods bmobile agent based data fusion cprocessing according to message 52912 categories 11 neural network neural networks use unsupervised learning. Data fusion is defined as a method that can combine different information from various sensors to achieve improved accuracy and reliability compared to what could be attained by the evaluation of individual sensor readings. Data fusion generally outperforms decision fusion in accuracy at the price of a higher communication cost. Pdf the success of a wireless sensor network wsn deployment strongly depends on the quality of service qos it provides regarding. Secure and accurate data fusion in wireless sensor networks. Research article an anomaly detection based on data fusion.
Data fusion techniques for auto calibration in wireless sensor networks. Due to the limitations of some sensor nodes, especially the limited amount of energy, innetwork data processing, such as data fusion, is very important. Wireless sensor nodes being resource constrained in terms of limited energy supply through batteries, the communication overhead and power consumption are the most important issues for wsns design. Data fusion is one of the key techniques in wireless sensor network wsn. Pdf energy consumption in wireless sensor networks using.
Oct, 2015 fei j 2011 a data fusion strategy of wireless sensor networks based on specific applications. Need for energy efficient data fusion in wireless sensor networks. Ni 19 mar 2015 1 machine learning in wireless sensor networks. International journal of distributed an intelligent data. Editorial energyefficient data fusion technique and. Due to the limitations of sensor nodes capabilities, especially the strictly limited energy, innetwork data processing, such as data fusion which can significantly improve the. Low complexity indoor localization in wireless sensor networks by uwb and inertial data fusion alberto savioli, emanuele goldoni, pietro savazzi, and paolo gamba university of pavia dipartimento di ing. This paper presents the data fusion survivability analysis model of wireless sensor network wsn based on stochastic petri net spn. Data fusion is a critical step in designing a wireless sensor network as it handles data acquired by sensory devices. 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. Wireless sensor networks are deployed for the purpose of sensing and monitoring an area of interest.
Wireless sensor networks wsn provide a bridge between the real physical and virtual worlds allow the ability to observe the previously unobservable at a fine resolution over large spatiotemporal scales have a wide range of potential applications to industry, science, transportation, civil infrastructure, and security. Wireless sensor networks wsns consist of a large number of source limited wireless sensor nodes for the purpose of data collection, processing, and transmission. In proceedings of the 4th international conference on networking icn 2005, p. Recent advancements in sensor technology, wireless networks and consequently wireless sensor networks and the increase in their applications in different fields have led to their great importance. Data fusion based on distributed quality estimation in. In this paper, a multisensor data fusion algorithm in wsn using fuzzy logic for event detection application is proposed. Nodes in each cluster optimization techniques, ensuring that energy.
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. In order to reduce the data processing load on bs and efficiently distinguish the authenticity of archived data, izadi et al. Data fusion, target detection, coverage, performance limits, wireless sensor network 1. Sensor fusion is also known as multi sensor data fusion and is a subset of information fusion.
Wireless sensor networks are used to monitor wine production, both in the field and the cellar. Frery federal university of alagoas ufal wireless sensor networks produce a large amount of data that needs to be processed, delivered, and assessed according to the application objectives. Sensor measurements in sensor networks usually suffer from both random errors noise and. Clustering based data collection using data fusion in. 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. Pdf data fusion techniques in wireless sensor networks. A number of sensor networks may have keywords wsn, data fusion, energy, cluster. However, till now such energy preservation solutions were not found to be. Fei j 2011 a data fusion strategy of wireless sensor networks based on specific applications.
Limited energy resources of sensor nodes in wireless sensor networks wsns make energy consumption the most significant problem in practice. These nodes gather data about their environment and collaborate to forward sensed data to centralized backend units called base stations or sinks for further processing. For the wsns, the innetwork preprocessing techniques could lead to saving in. Even when the sensors are properly calibrated at the time of deployment, they develop errors in their readings leading to erroneous inferences to be made by the network. Data fusion based on rbf and nonparametric estimation for localization in wireless sensor networks abstract. In network data aggregation and fusion see jones, sivalingam, agrawal, and chen survey article in acm winet, july 2001.
Pdf a data fusion method in wireless sensor networks. Scalable structurefree data fusion on wireless sensor. In wireless sensor networks wsns the operating conditions andor user requirements are often desired to be evolvable, whether driven by changes of the monitored parameters or wsn properties of configuration, structure, communication. Data fusion is a wellknown technique that can be useful for the enhancement of data quality and for the maximization of wsn lifetime. Introduction to wireless sensor networks types and applications. Wireless sensor networks introduction to wireless sensor networks february 2012 a wireless sensor network is a selfconfiguring network of small sensor nodes communicating among themselves using radio signals, and deployed in quantity to sense, monitor and understand the physical world. In idgsdf, we adopt a neural network to conduct data fusion to improve network performance. Section 6 justifies the need for energy efficient data fusion. The main idea of this method is to use the computational capacity of sensor nodes to perform data fusion while routing data to the sink. Industriale e dellinformazione via ferrata 1 27100 pavia, italy email. The wireless sensor network wsn is mainly composed of a large number of sensor nodes that are equipped with limited energy and resources.
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. Neural network probabilistic model time series kalman filtering. One part is related to finding a general applicable approach for data management in sensor networks which will become a selfaware, selfconfiguring. In this paper, we have presented a fuzzybased method for data fusion. This paradigm is also known as data centric routing 5. However, the security or assurance of the data requires more processing power and is an. In this paper, we present a fuzzybased data fusion approach for wsn with the aim of increasing the qos whilst reducing the energy consumption of the sensor network. Distributed wireless sensor networks is a collection of embedded sensor devices with networking capabilities. Data fusion techniques reduce total network traffic in a wireless sensor network, since data fusion can integrate multiple raw data sets into one fused data set. Hybrid data and decision fusion techniques for modelbased data gathering in wireless sensor networks lorenzo a. Oct 27, 20 hall and llinas provided the following wellknown definition of data fusion. In this section we discussed about some published techniques related to the data fusion. First, we partition the whole sensor fields into several subdomains by virtual grids.
Sensor measurements in sensor networks usually suffer from both random errors noise and systematic errors drift and bias. Data fusion and topology control in wireless sensor networks. 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. 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. Consequently, the energy consumption in largescale wsns has been ignored thus far in this area of research. Data mining and fusion techniques for wsns as a source of. 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. Manets have high degree of mobility, while sensor networks are mostly stationary. Redundancy elimination during data aggregation in wireless. Pdf data fusion techniques for auto calibration in. In addition, the gating technique is also applied to solve the problem of msdft mobile sensor data fusion tracking for targets, i. Section 5 present existing data fusion techniques in. Data fusion techniques in wireless sensor networks. An algorithm of mobile sensors data fusion tracking for.
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