For example, these schemes apply to wireless sensor networks, where a set of nodes collect and transmit correlated data to a common sink in an energyef. Data fusion techniques for auto calibration in wireless. Pdf study of data fusion in wireless sensor network. One the computational intelligence algorithms is fuzzy logic or fuzzy system algorithm. It is now possible to construct, from commercial o theshelf cots com. Fuzzy data fusion for fault detection in 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. Energyefficient and reliable transmission of sensory information is a key problem in wireless sensor networks. Abstract wireless sensor networks consist of a large number sensor nodes that are deployed in some geographical area. In figure 3, the osi model is used in this article to compare the two wsn standards. Isbn 97839026523, pdf isbn 9789535158394, published 20090201.
As data either raw or fused is propagated towards the sink, multiple levels of data fusion are likely. One example of a good mac protocol for wireless sensor networks is bmac 24. Introduction wireless sensor networks is an emerging technology that is gaining a lot of attention for applications such as monitoring and data gathering. Evolution of wireless sensor networks for industrial control. However, with the continuous application of wireless sensor networks, it raises higher demands for information integrity and privacy, data fusion faces new challenges. But in the structural health monitoring based on wireless sensor networks, this method has some inevitable defects in data transmission. Need for energy efficient data fusion in wireless sensor. With the promotion of the latest technologies and the new requirement of humanitarian, the wireless multisensor system is applied broadly. Data fusion in wireless sensor networks using fuzzy systems.
This paper studies the data fusion of the industrial wireless sensor networks iwsns, in order to acquire more thoughtful data for the prognosis and diagnosis of the monitored device. We also explain each of the parameters in much more detail lines 434481. Wireless multiple access schemes, where correlated signals, observed at different nodes, need to be transferred to one or more collectors, model several communication scenarios. The captured image at the source could be noisy, incomplete and redundant. As a result, the sensing performance of a wireless sensor network is inevitably undermined by biases in imperfect sensor hardware and the noises in data measurements. Abstractwireless sensor networks consist of a large number sensor nodes that are deployed in some geographical area. Data fusion, target detection, coverage, performance limits, wireless sensor network 1.
Pdf a data fusion method in wireless sensor networks. Saad, nasrullah armi, nidal kamel abstractduring the last decades. A data fusion method in wireless sensor networks mdpi. If these redundant data are processed and transmitted, the node energy consumption will be too fast and will affect the overall lifetime of the network. Data fusion is a powerful tool for wireless sensor networks in many aspects, such as energy saving, target coverage, routing algorithm, data dissemination and so. Data fusion and collaborative state estimation in wireless. Information fusion in wireless sensor networks with source. Methods, models, and classifications nakamura, loureiro, frery 2 enabling robotic attitude sensing and autonomous navigation through inertial sensor technology david churchill 2010. We assume that the signal power attenuates as a function of the distance from the target, the number of sensors follows a poisson distribution, and the locations of sensors follow a.
We consider distributed detection in a clustered wireless sensor network wsn deployed randomly in a large field for the purpose of intrusion detection. Distributed detection and fusion in a large wireless. Pdf similarity clustering for data fusion in wireless. Pdf on jun 1, 2018, mahnaz koupaee and others published data fusion techniques in wireless sensor networks. Abstractin wireless sensor networks, innetwork data fusion is needed for energyef.
Data fusion with desired reliability in wireless sensor. Data acquisition and fusion system based on wireless sensor. Based on the analysis of typical application scenarios in traffic field and the. The data fusion at various levels should be synchronized in. Generally, a large number of sensor nodes, capable of collecting data, processing and communicating. Deddistance energy and degree, wireless sensor networks i. 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. Index terms sensor networks, data gathering, data fusion.
In addition, the gating technique is also applied to solve the problem of msdft mobilesensor data fusion tracking for targets, i. Together, these technologies have combined to enable a new generation of wsns that differ greatly from wireless networks developed. Mac protocol for wireless sensor networks must consume little power, avoid collisions, be implemented with a small code size and memory requirements, be e. Lemma1 the conditional pdf of y k, the observation from sensor k, given the local decision u. 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. Data fusion based on node trust evaluation in wireless. Compressive sampling and data fusionbased structural damage.
Data fusion improves the coverage of wireless sensor networks. In 27, a surveillance system has both lowend passive infrared sensors and highquality. In our prior work 2, while a randomized algorithm termed minimum fusion steiner tree mfst is devised towards this end, it assumes that data fusion shall be. Research on wireless sensor networks data fusion algorithm. Therefore, to maximize the lifetime of sensor networks, aggressive energy optimization techniques have to be used for ensuring that energy is conserved for the sensor nodes. Data fusion based on node trust evaluation in wireless sensor. The three fundamental ways of combining sensor data are the following.
Data fusion utilization for optimizing largescale wireless sensor networks mohammadreza soltani, michael hempel, hamid sharif advanced telecommunications engineering laboratory, dept. Section 5 present existing data fusion techniques in wireless sensor networks. In this paper, we present a fuzzybased data fusion approach for wsn with the aim of increasing the qos whilst reducing the energy consumption. These authors propose a combination of back propagation neural. Wireless sensor networks, distributed detection, decision fusion, signal attenuation model. This investigate is to finish the factory monitoring at environment monitoring services ems. Data fusion in mobile wireless sensor networks muhammad arshad, member, iaeng, mohamad alsalem, farhan a. Particularly, in network data fusion techniques are very important to applications such as target. The gating mechanism is realized as a fullyconnected network. A large number of sampling data of damage response signal will cause huge wireless communication burden. Wireless sensor networks may be considered a subset of mobile adhoc networks manet. Synchronization of multiple levels of data fusion in wireless.
Section 6 justifies the need for energy efficient data fusion. Design and deployment of wsn in a home environment and real. To improve the wireless sensor networks data fusion efficiency and reduce network traffic and the energy consumption of sensor networks, combined with chaos optimization algorithm and bp algorithm designed a chaotic bp hybrid algorithm coabp, and establish a wsns data fusion model. Each sensor node has the monitoring privilege and obligation. Adaptive decision fusion with a guidance sensor in. Sensor fusion is the process of merging data from multiple sensors such that to reduce the amount of uncertainty that may be involved in a robot navigation motion or task performing. 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. A stochastic geometry framework is employed to derive the. The aim of this book is to present few important issues of wsns, from the application, design and technology points of view. The paper focuses on issues related to the integration of wireless sensor network security data, analyzes its attack types. Proceedings of 2010 uk workshop on computational intelligence. Image fusion forwireless sensor networks abstractmajor source of energy consumption in wireless sensor networks wsnsis transmission of image from source to sink and image processing at the nodes. Usually a wsn consists of a large number of lowcost and lowenergy sensors, which are deployed in the environment to collect observations and preprocess the. Wireless sensor networks, routing topology, network inference, compressed sensing, recovery algorithms.
Data acquisition and fusion system based on wireless sensor dan qiu1, shuli gong2. Decision fusion rules in wireless sensor networks using. In this research study, an energy efficient cluster head selection in mobile wireless sensor networks is proposed, analysed and validated on the basis of residual energy and randomized selection. Wireless sensor networks are used to monitor wine production, both in the field and the cellar. Wireless communications and mobile computing wirel. In typical wireless sensor networks, sensor nodes are usually limited in resources and energy. Wireless sensor networks are typically composed of lowcost sensors that are deeply integrated in physical environments.
In this article, we use a neural network to help set up a wsn with distributed data fusion. These computers typically called motes or sensor nodes are equipped with di erent types of sensors, such as e. Physiological signal acquisition system based on wireless sensor networks. Data fusion technology compresses the sampled data to eliminate redundancy, which can effectively reduce the amount of data sent by the node and prolong the lifetime of the. Data fusion of wireless sensor network for prognosis and. Data aggregation is necessary for wireless sensor networks. 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. Data fusion with desired reliability in wireless sensor networks abstract. Decision fusion in a wireless sensor network with a large. Optimal fusion rule for distributed detection in clustered. Fellow, ieee abstractwe propose a distributed sequential estimation scheme for wireless sensor networks with asynchronous measurements. Since the sensor nodes are battery operated, collecting and transmitting data will cost a lot of energy resources. 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. We show that data fusion can significantly improve sensing.
Extending lifetime of wireless sensor networks using multi. The application of these methods, however, requires some care due to a number of issues that are particular to sensor networks. Research on data fusion scheme for wireless sensor. A study on data fusion of wireless sensor networks. Manets have high degree of mobility, while sensor networks are mostly stationary. 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. 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.
Wireless sensor networks consist of a powerful technology for monitoring the physical world. Due to the limitations of some sensor nodes, especially the limited amount of energy, innetwork data processing, such as data fusion, is very important. Data fusion improves the coverage of wireless sensor. Distributed sequential estimation in asynchronous wireless. The wsn is modeled by a homogeneous poisson point process. Since it is impossible to confirm that the collected data are true values of the events without taking samples or analyzing data history, we suggest assigning a weight for each collected data. Based on the management pattern of cluster structure, in 1, conti et al. Multimedia data fusion method based on wireless sensor. This study attempts to apply a backpropagation network bpn for multisensors data fusion in a wireless sensor networks wsns system with a nodesink mobile network structure. Data fusion in wireless sensor networks a statistical.
Challenges in wireless sensor network wireless sensor network assure a wide variety of. 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. Sensor networks although fusion frames can be used to model general distributed processing applications, in this paper we intend to focus on the modeling of sensor networks. Wireless sensor networks wsns are resourceconstrained networks, especially when the energy is highly constrained. Dynamic data fusion for future sensor networks umakishore ramachandran, rajnish kumar, matthew wolenetz, brian cooper, bikash agarwalla, junsuk shin, phillip hutto, and arnab paul college of computing, georgia institute of technology dfuse is an architectural framework for dynamic applicationspeci. 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. Chief of among these are the distributed nature of computation and deployment coupled with communications bandwidth and energy constraints typical of many 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. A survey wireless sensor networks wsns consist of small nodes with sensing, computation, and wireless communications capabilities. 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. Evolution of wireless sensor networks for industrial control arthur low the rio through any b and r node. Data fusion technology is widely used in data processing due to its characteristic of less transfer data. Wireless sensor data fusion algorithm based on the sensor.
Analytically and experimentally, we show that afst achieves better performance than existing algorithms including slt, spt, and mfst. A data fusion method in wireless sensor networks article pdf available in sensors 152. An intelligent data gathering schema with data fusion supported for. In order to realize the ubiquitous perception of urban traffic system integration, a universal technology architecture supporting multiple heterogeneous access, universalization and tailoring is needed to realize the interconnection and interoperability of perception systems in different application scenarios. A data fusion method in wireless sensor networks ncbi.
Data fusion in wireless sensor networks yun liu, qingan. Data fusion methods data compression wireless sensor network. Wireless sensor networks wsns consist of large number of constrained wireless sensor nodes for the purpose of data gathering. 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. Many practical wireless sensor networks have multiple sensor modalities 26. Recent advances insemiconductor, networking and material science technologies are driving the ubiquitous deployment of largescale wireless sensor networks wsns. An algorithm of mobile sensors data fusion tracking for. Data fusion and collaborative state estimation in wireless sensor networks. In wireless sensor networks, resourceconstrained sensor nodes are spread over a potentially large area to measure environmental characteristics such as. Distributed sequential estimation in asynchronous wireless sensor networks ondrej hlinka, franz hlawatsch, fellow, ieee,andpetarm. Data fusion based on distributed quality estimation in. A study on data fusion of wireless sensor networks security.
Section 4 presents some highlevel protocols for energye. Loureiro federal university of minas gerais ufmg and alejandro c. The loss of battery or energy may lead to failure of the entire network 14. Sections 2 and 3 provide examples of mac and network protocols, respectively, for use in sensor networks. Data fusion in wireless sensor networks maen takruri submitted in partial fulfillment of the requirements for the degree of doctor of philosophy faculty of engineering and inforrnation technology university of technology, sydney march 2009. Nakamura analysis, research and technological innovation center fucapi federal university of minas gerais ufmg antonio a. Abnormal behavior detection and trust evaluation mode of traditional sensor node have a single function without considering all the factors, and the trust value algorithm is relatively complicated.
Data fusion and topology control in wireless sensor networks. A new data fusion algorithm for wireless sensor networks. Pdf the success of a wireless sensor network wsn deployment strongly depends on the quality of service qos it provides regarding issues such as. We propose a deep learning architecture for the sensor fusion problem that consists of two convolutional neural networks cnns, each consisting of a different input modality, which are fused with a gating mechanism. The sensor nodes sns compute local decisions about the intruders presence and send them to the cluster heads chs. 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 book highlights power efficient design issues related to wireless sensor networks, the existing wsn applications, and discusses the research efforts being undertaken in this field which put the reader in good pace to be able to understand more advanced research and.
To save more energy, innetwork processing such as data fusion is a widely used technique, which, however, may often lead to unbalanced information among nodes. 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. Data fusion in wireless sensor networks ieee conference. This paper discusses about wireless sensor network, its. Many conventional methods in various sciences are not able to properly support a high volume of quantitative and qualitative information 8. Multiorder fusion data privacypreserving scheme for wireless. Pdf data fusion techniques in wireless sensor networks.
Wireless sensor networks technology and applications. Impact of data fusion on realtime detection in sensor. Adaptive decision fusion with a guidance sensor in wireless sensor networks zhaohuayu,1 qiangling,1 andyiyu2. For a wireless sensor network wsn with a random number of sensors, we propose a decision fusion rule that uses the total number of detections reported by local sensors as a statistic for hypothesis testing. There are a lot of redundant data in wireless sensor networks 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. Extending lifetime of wireless sensor networks using multisensor data fusion soumitra das1, s barani2, sanjeev wagh3 and s s sonavane4 1department of computer science and engineering, sathyabama university, chennai 600119, india 2department of electronics and control engineering, sathyabama university, chennai 600119, india 3department of computer engineering, k. Related issues study of wireless sensor network security. Efficient multisource data fusion for decentralized sensor networks unclassifiedunlimited if nodes a and b communicate their information, the updated estimate can be calculated as the product of their distributions divided by the common information 12. Wireless sensor networks wsns have been used in various domains such as military applications e. The purpose of the network is to sense the environment and report what happens in the area it is deployed in. Decision fusion rules in wireless sensor networks using fading channel statistics ruixin niu, biao chen, and pramod k. We derive the scaling laws between coverage, network density, and signalto noise ratio snr.
Distributed signal processing and data fusion methods for. In order to reduce the data processing load on bs and efficiently distinguish the authenticity of archived data, izadi et al. Data fusion and collaborative state estimation in wireless sensor networks hiba haj chhade to cite this version. Wireless sensor network sensor data elderly person smart home current. The objective is to maximize the network lifetime equation 4 ensuring that the percentage of the true value of data and data redundancy are satisfied by a userdefined value equation 5. Wsn nodes have less power, computation and communication compared to manet nodes. Sensor modality fusion with cnns for ugv autonomous. Sensor fusion helps in building a more accurate world model in order for the robot to navigate and behave more successfully. Routing topology inference for wireless sensor networks. Efficient multisource data fusion for decentralized.
Multisensors data fusion system for wireless sensors. Data fusion can effectively reduce the volume of data transmission in the network, reduce the energy consumption to extend network lifetime and improve bandwidth utilization, as a result, it can overcome. Systemlevel calibration for data fusion in wireless sensor. The isa network also shows a backbone network solid thick line connecting the gms and the backbone routers. 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. When a sensor node sends out a packet, it puts the residual energy e r in the header file of the data packet to convey the information to the neighbor nodes.
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