Adaptive clustering and data aggregation in wireless sensor networks acda by babak sepehr a thesis presented to the university of guelph in partial fulfillment of requirements for the degree of master of science in computer science guelph, ontario, canada babak sepehr, october, 20. Spatial data correlation based clustering algorithms for. This kind of data redundancy, due to the spatial correlation between sensor observations, enriches the research of in network data aggregation. Wireless sensor networks for maximizing the amount of data gathered during the lifetime of a network. Attributebased clustering for information dissemination. Abstractwe consider the problem of optimal clusterbased data gathering in wireless sensor networks wsns when near by readings are spatially correlated. Entropybased correlation clustering for wireless sensor. A survey on data aggregation techniques to improve the.
The main objective of this paper is to construct a distributed clustering algorithm based upon spatial data correlation among sensor nodes and perform data accuracy for each distributed cluster at their respective cluster head node. A spatialtemporal correlation approach for data reduction. Introduction recent development of wireless technology and embedded system made a drastic improvement over wireless sensor networks. The efficient way to overcome these challenges is clustering sensor nodes.
Precise distance estimation is needed in various wsn applications, such as velocity measurement, object identification, deployment, control, localization and tracking 46. Every cluster would have a clusterhead ch as their leader. Clustering has been widely pursued to achieve the network scalability objective by the research community. A survey on clustering algorithms for wireless sensor networks. Considering the requirements of energy saving and data aggregation, and the characteristics of spatial correlation and temporal correlation in wireless sensor networks wsn, this paper proposes a spatialtemporal correlation based novel clustering algorithm, stcbca. Request pdf data correlationbased clustering in sensor networks many types of sensor data exhibit strong correlation in both space and time. Section iii introduces a novel random update method and cluster head rotation scheme to update sensor clusters dynamically. A survey on hierarchical clustering algorithm for wireless. The objective of this paper is to develop a new adaptive iterative linear regression based clustering algorithm for wireless sensor network. It is shown that this problem is solved optimally using mincost network.
A sub clustering algorithm based on spatial data correlation for energy conservation in wireless sensor networks minghui tsai and yuehmin huang department of engineering science, national chengkung university, no. Kmeans based clustering approach for data aggregation in periodic sensor networks. In this section, the clustering algorithm based on data density correlation. Pdf data accuracy model for distributed clustering. Recently wireless sensor networks featuring direct sink access have been studied as an efficient architecture to gather and process data for numerous applications. In distributed clustering, where each sensor node can run their own algorithm and takes the decision of becoming cluster. Nov 18, 2014 a sub clustering algorithm based on spatial data correlation for energy conservation in wireless sensor networks. A subclustering algorithm based on spatial data correlation. The proposed clustering algorithm is applied for uniform. The remainder of this paper is organized as follows. Lowenergy adaptive clustering lowenergy adaptive clustering 10 is one of the milestones in clustering algorithms. Wireless sensor networks wsn are often deployed to sam ple the desired environmental. The monitoring data contain many types, such as temperature, humidity, light and so on. Clusterbased routing algorithms using spatial data.
Information about the openaccess article spatial correlationbased clustering in wireless sensor network in doaj. Various clustering techniques in wireless sensor network. A spatial correlation based data aggregation algorithm for. Ma et al distributed clusteringbased aggregation algorithm for spatial correlated sensor networks 643 is designed for timecritical applications and the sensor transmits the data to the sink only when the collected data is greater thanaprede. May 23, 2008 we focus on the joint effect of clustering and data correlation on the performance of such networks. Therefore the clustering is a suitable method that used in energy consumption. Data gathering in wireless sensor networks is one of the important operations in these networks. Uneven clustering routing algorithm based on optimal. Considering the requirements of energy saving and data aggregation, and the characteristics of spatialcorrelation and temporalcorrelation in wireless sensor networks wsn, this paper proposes a spatialtemporal correlation based novel clustering algorithm, stcbca. The levelk hierarchy with singlehop communication between nodes. Due to the dense nature of wsns, data samples taken from nearby locations are statisti cally similar. Data aggregation is the important method to reduce data traffic and lower energy expenditure in wireless sensor networks wsn. Qin and zimmermann proposed a voting based clustering algorithm vca for energyefficient data dissemination in quasistationary sensor networks. Energyefficient clustering using correlation and random.
The levelk hierarchy with singlehop communication between. Recently, wireless sensor networks wsns have attracted tremendous attention in both the research community and industry. A survey of correlation clustering abstract the problem of partitioning a set of data points into clusters is found in many applications. A subclustering algorithm based on spatial data correlation for energy conservation in wireless sensor networks. Minghui tsai department of engineering science, national chengkung university, no. Data correlationbased clustering in sensor networks ieee xplore.
In densely deployed wireless sensor networks wsn, sensor observations are. We propose a novel cluster based data collection scheme for sensor networks with direct sink access cdcdsa, and provide an analytical framework to evaluate its performance in terms of energy consumption, latency, and robustness. The key issue in determining the lifetime of wireless sensor network wsn is the energy burning up of individual node. There are many available techniques to estimate distance. Due to the limited energy of nodes, the energy productivity should be considered as a key objective in design of sensor networks.
On the basis of this, this paper proposes a spatial correlation based. Spatial correlation aware protocols for efficient data. In addition, it is possible to consume less energy by using the spatial correlation and redundancy of data in dense networks to form clusters of nodes sensing similar values and, in turn, transmit one data packet per cluster. Many hierarchical protocols show better ability of energy efficiency in the literature. The algorithm considers the calculation of optimal cluster number, cluster head selection, cluster radius calculation, and isolated node management. This leads sensor observations to have spatial correlation. A data mining based technique to handle missing data in mobile sensor network applications.
Qin and zimmermann proposed a votingbased clustering algorithm vca for energyefficient data dissemination in quasistationary sensor networks. Research open access performance evaluation of data. In clustering, the data values are partitioned into several clusters, and each clus. A survey on clustering algorithms for wireless sensor networks ameer ahmed abbasi a, mohamed younis b a department of computing, alhussan institute of management and computer science, dammam 31411, saudi arabia b department of computer science and electrical engineering, university of maryland, baltimore county, baltimore, md 21250, usa available online 21 june 2007. A survey on clustering algorithms of wireless sensor network. Traditional csbased data gathering algorithm, such as compressive data gathering cdg of luo et al. Traditional cs based data gathering algorithm, such as compressive data gathering cdg of luo et al. A clustering approximation mechanism based on data spatial. This kind of data redundancy, due to the spatial correlation between sensor observations, enriches the research of innetwork data aggregation.
A resilient data aggregation method based on spatiotemporal. Each sensor nodes data are time series with a temporal correlation. In cluster formation phase, stcbca clusters nodes in the network according to the spatial correlation between nodes and cluster. A distancebased data aggregation technique for periodic.
To resolve the above problems, an energyefficient sleep scheduling mechanism with similarity measure for wireless sensor networks essm is proposed, which will schedule the sensors into the active or sleep mode to reduce energy. The algorithm uses the spatial correlation between the sensed data of the sensors to build the clusters. Data correlationbased clustering in sensor networks. Adaptive clusterbased data collection in sensor networks. Effect of partially correlated data on clustering in wireless. Data aggregation, clustering methods, energy efficiency,wireless sensor network. Dataaware clustering and aggregation in querydriven. A spatialtemporal correlation based novel clustering. A sub clustering algorithm based on spatial data correlation for energy conservation in wireless sensor networks. In this paper, we propose a dataaware clustering and aggregation scheme daca to.
We propose a novel clusterbased data collection scheme for sensor networks with direct sink access cdcdsa. We focus on the joint effect of clustering and data correlation on the performance of such networks. The cluster based communication model can provide an architectural framework for exploring data correlation in sensor networks. Leach stands for low energy adaptive clustering hierarchy which is the first protocol of hierarchical routing which proposed data fusion, it is of milestone significance in clustering routing protocol all the nodes in a network organize themselves into local cluster, with one node acting as the cluster head. Besides, data reduction based on the correlation of sensed readings can efficiently reduce the amount of required transmissions. In this paper, an effective multilevel cluster algorithm using link correlation is proposed for heterogeneous wsn. In wireless sensor networks, the high density of nodes distribution will result in transmission collision and energy dissipation of redundant data. In this paper, we survey the attacks on sensor networks and the possibilities of avoiding these by the use of data mining technique called data clustering data mining is the process of discovering meaningful new correlation, patterns and trends by. Parallel clustering differs from distributed clustering in that all the data is available to all processes, or is carefully. Adaptive clustering and data aggregation in wireless. Data density correlation degree clustering method for data. Wireless sensor networks wsns have emerged as a promising solution for various applications due to their low cost and easy deployment. Therefore, we use a subclustering procedure based on spatial data correlation to further separate the hierarchical clustered architecture of a wsn. We investigate that due to deployment of high density of sensor nodes in the sensor field, spatial data are highly correlated.
A survey on data aggregation techniques to improve the energy. Request pdf on apr 1, 2014, fei yuan and others published data density. The clusterbased communication model can provide an architectural framework for exploring data correlation in sensor networks. Pdf data correlation based energy aware energy efficient. Clusterbased correlated data gathering in wireless sensor. In this paper, we group the sensor nodes based on their inherent spatial and data correlation in wsn. A spatialtemporal correlation approach for data reduction in.
Mediumcontention based energyefficient distributed. A distributed energyefficient clustering protocol for. Introduction wireless sensor networks wsns are simple lowcost approaches that can be used in a distributed environment. This necessitates additional study into the characteristics of clustering with partially correlated data. Our proposed clustering technique based on divergence measure is. Leach is an example of clustering protocol for wireless sensor network which consider homogeneous sensor networks where all sensor nodes are designed with the same battery energy. Based on a data density correlation degree ddcd clustering method representative data. Pdf a subclustering algorithm based on spatial data. Attributebased clustering for information dissemination in. An energyefficient sleep scheduling mechanism with. By choosing dynamic cluster head, this problem can be eliminated. A novel modelbased hierarchical clustering that adapts to attribute dynamics.
In this paper, we focus on the joint effect of clustering and data correlation on the performance of such networks. Correlation clustering is a clustering technique motivated by the the problem of document clustering, in which given a large corpus of. Dynamic clustering and compressive data gathering algorithm. A new energy efficient data gathering approach in wireless. Distributed spatial correlationbased clustering for. Keywords sensor networks, distributed cluster ing, robust aggregation 1 introduction to analyze large data sets, it is common practice to employ clustering 6. Unfortunately, existing clustering algorithms are difficult to utilize the spatial or temporal opportunities, because they just organize clusters based on the distribution of sensor nodes or the network. For instance, lowenergy adaptive clustering hierarchy leach, one of the first clustering algorithms proposed for sensor networks, is a distributed, proactive, dynamic algorithm that forms. Many clustering algorithms have been for adhoc networks 1115. Entropy, correlation clustering, entropy correlation coefficient, multi correlation regions 1. An abnormal data detection method based on the temporal. Section iii introduces a novel random update method and cluster head rotation scheme to.
Of these two, the best is selected based on the proposed similarity index and with this selected cluster as reference. Adaptive clustering and data aggregation in wireless sensor. Spatial correlationbased clustering in wireless sensor. We show how this data correlation can be exploited t o. Oct 16, 2014 the key issue in determining the lifetime of wireless sensor network wsn is the energy burning up of individual node. A subclustering algorithm based on spatial data correlation for energy conservation in wireless sensor networks minghui tsai and yuehmin huang department of engineering science, national chengkung university, no. In this paper, we propose a data aware clustering and aggregation scheme daca to. Uncertain data clusteringbased distance estimation in. All noncluster head node transmit their data to the cluster head, while the ch node. The cluster based routing improves the energy usage of wsn compared to other routing approach. In this paper various energy efficient hierarchical clustering based routing algorithm are discussed and compared. Many types of sensor data exhibit strong correlation in both space and time.
Spatial correlation, distributed clusters, data accuracy, wireless sensor networks. Request pdf a clustering approximation mechanism based on data spatial correlation in wireless sensor networks in wireless sensor networks wsns, the sensor nodes that locate near often sense. We present a generic algorithm that solves the distributed. In wireless sensor networks, the existing data aggregation algorithms. A novel regression based clustering technique for wireless. A strategy that reduces energy consumption without affecting the accuracy of readings significantly is highly preferable. This paper analyzes the characteristics of data sampled by nodes, and gives the method to decide spatial correlation between neighboring nodes and the criteria to classify and decide data in wsn. Both temporal and spatial suppression provides opportunities for reducing the energy cost of sensor data collection. However, it does not consider the sensor readings from other. Distributed data clustering in sensor networks sets.
Effect of partially correlated data on clustering in. Request pdf spatial data correlation based clustering algorithms for wireless sensor networks in this paper, spatial data correlations are exploited to group sensor nodes into clusters of high. Survey of clustering algorithm in wireless sensor networks. Abstractone data aggregation method in a wireless sensor network wsn is sending local representative data to the sink node based on the. According to this, the initial cluster is classified horizontally and vertically in parallel, each resulting in two subclusters. Therefore, we use a sub clustering procedure based on spatial data correlation to further separate the hierarchical clustered architecture of a wsn. Data accuracy model for distributed clustering algorithm. Wireless sensor networks clustering network lifetime distributed energy ef. Entropy correlation and its impacts on data aggregation in a wireless sensor network. Mining data correlation from multifaceted sensor data in the. The aim of lowenergy adaptive clustering was to select nodes as cluster heads in such a way. In proceedings of the 10th ieee international conference on wireless and mobile computing, networking and communications wimob14. In this paper, we propose an uneven clustering routing algorithm based on optimal clustering.