However, this centralized approach has two major drawbacks. Distributed spatial skyline query processing in wireless. On the optimal randomized clustering in distributed sensor. Because of the multidimensional nature of the problem of monitoring spatial position, traditional skyline query strategies cause enormous computational costs and energy consumption. Distributed robust data clustering in wireless sensor. Home browse by title proceedings edbt06 distributed spatial clustering in sensor networks. Distributed spatial clustering in sensor networks anand meka and ambuj k. Sensor networks monitor physical phenomena over large geographic regions. Distributed positioning and tracking in clusterbased. Request pdf distributed localization for anisotropic sensor networks using spatial clustering anisotropic networks are characterized by the properties that vary with the direction of measurement. The clustered aggregation cag technique leveraging spatial. Experimental results on both real world and synthetic data setsshow that the quality of clusters generated is comparable to thecentralized algorithm and \\emph innetwork clustering and modelingcombined together reduces the communication cost by two orders ofmagnitude.
Often, the ultimate objective is to derive an estimate of a parameter or function from these data. Index terms biological algorithms, clustering, spatial data dependency, sensor. Distributed data clustering in sensor netw orks sets. As the numbers of sensor nodes are more in the sensor field, the data. Distributed spatial analysis in wireless sensor networks a thesis submitted to the university of manchester for the degree of doctor of philosophy. Index terms biological algorithms, clustering, spatial data dependency, sensor networks i. However, in applications where targets may frequently travel in the same section e. We propose a modification to distributed localization of anisotropic sensor networks which uses linear mapping method projecting an embedded space based on proximities into geographic distance space.
Introduction lustering is a useful technique to adopt in sensor networks when collecting the data measured at a central base station. This kind of data redundancy due to the spatial correlation between sensor observations inspires the. A biologicallyinspired clustering algorithm dependent on. There have been some distributed clustering methods in sensor networks, such as elink 6 and dsic 8.
This kind of data redundancy due to the spatial correlation between sensor observations inspires the research of in network data aggregation. Gaussian pdf is more suitable in many wsn applications than, for example, uniform pdf which is commonly used for flat ad hoc networks. Such data characteristics can be efficiently extracted through spatial clustering, which partitions the network into a set of spatial regions. Pdf distributed clustering algorithm for spacial data mining.
Parallel clustering differs from distributed clustering in that all the data is available to all processes, or is carefully. Topology adaptive spatial clustering for sensor networks. This paper focuses on extracting regular structures through the distributed clustering scheme and later on carrying out the localization procedure. Distributed spatial correlationbased clustering for.
To simplify the discussion, we assume a square grid ofn nodes. Distributed spatial clustering in sensor networks ucsb. The tokenbased wireless sensor network cluster communication architecture in document is to achieve energysaving goals from this aspect, but the cost factor is introduced in the next hop node selection process, which increases the computing cost. Pdf a survey on clustering algorithms for wireless sensor. Distributed consensus estimation is a valuable resource for researchers and professionals working in wireless communications, networks and distributed computing.
However, their clustering processes all depend on some communication infrastructure, such. Distributed algorithms are an established tool for designing protocols for sensor networks. Realworld applications of distributed clustering mechanism. Clustering tasc algorithm is a distributed algorithm that partitions the network into a set of locally isotropic, non overlapping. In an attempt to contrive efficient heuristic solutions for this problem, we focus on randomized clustering and develop a framework to quantify the energy consumption. Spatial clustering also serves to prolong network lifetime. Singh department of computer science, university of california, santa barbara santa barbara, ca. To ensure the efficient use of sensor energy, a geometrybased distributed. Distributed compact routing using spatial distributions in wireless sensor networks a. Our indexing technique allows roaming users to navigate through sensor networks distributed over large geographical areas and to pose spatial queries about the location of the data in the network. Citeseerx distributed spatial clustering in sensor networks. Clustering is an efficient topology control approach, which can extend the lifetime and raise the scalability of these sensor networks.
Adaptive distributed indexing for spatial queries in. The goal of this paper is to perform such a spatial clustering, specifically. Pdf wireless sensor networks wsns are resource constrained systems that needs efficient. Data accuracy model for distributed clustering algorithm based on.
Data accuracy model for distributed clustering algorithm. Density gridbased clustering for wireless sensors networks. Distributed spatial clustering in sensor networks springerlink. Because of the multidimensional nature of the problem of monitoring spatial position, traditional skyline query strategies cause enormous computational costs and. Traditional kmeans based distributed clustering used in wireless sensor networks has limitation of getting stuck into local minima, thus many times results in giving inaccurate cluster partitions. Wsns measure environmental conditions like temperature, sound, pollution levels, humidity, wind, and so on.
In this paper, we propose a novel energy efficient. A concise way to extract thisstructure is through \\emph spatial clustering, which partitions thenetwork into a set of spatial regions with similar observations. Research article hierarchical spatial clustering in multihop. The clustered aggregation cag technique leveraging spatial and temporal correlations in wireless sensor networks sunhee yoon and cyrus shahabi university of southern california sensed data in wireless sensor networks wsn re. An energy efficient hierarchical clustering algorithm for wireless sensor networks seema bandyopadhyay and edward j. In consideration of these drawbacks, we take a distributed approach to clus. Such data characteristics can be efficiently extracted through spatial clustering, which partitions the network into a set of spatial regions with similar observations. One of the major tasks of sensor networks is the distributed collection and processing of sensor. We address the problem of discovering spatial relationships in sensor data through the identification of clusters. Data accuracy model for distributed clustering algorithm based on spatial data correlation in wireless sensor networks 1jyotirmoy karjee,2h. Duetothe adhoc natureandresource constraints of wsns, distributed spatial clustering would be abetterchoice. Distributed clustering based aggregation algorithm for spatial correlated sensor networks abstract. The total forwarding rates produce randomly as listed in table 4. Distributed spatial clustering in sensor networks 985.
International journal of distributed dynamic clustering. A biologicallyinspired clustering algorithm dependent on spatial. Scientists can gain valuable insight into these phenomena, if. We provide a formal and general definition of the problem of optimal clustering in distributed sensor networks with arbitrary ch selection and prove that this problem is nphard. Distributed spatial clustering in sensor networks core. Keywords sensor networks, distributed cluster ing, robust aggregation 1 introduction to analyze large data sets, it is common practice to employ clustering 6. International journal of distributed spatiotemporal. In clustering, the data values are partitioned into several clusters, and each clus. 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.
Our proposed approach does not make any assumptions about the presence of infrastructure or about node capabilities, other than the availability of multiple power levels in sensor nodes. Topology adaptive spatial clustering for sensor networks reino virrankoski, dimitrios lymberopoulos and andreas savvides embedded networks and applications lab enalab electrical engineering department yale university, new haven, ct 06520 abstractthe ability to extract topological regularity. Attributebased clustering for information dissemination. Pdf an energy efficient clustering scheme in wireless. Pdf optimal number of clusters in wireless sensor networks. A heterogeneous energy wireless sensor network clustering. Centralized and distributed clustering methods for energy. More, dhcs considers both data similarity and spatial proximity. Algorithms for skyline querying based on wireless sensor networks wsns have been widely used in the field of environmental monitoring. As opposd to communicating directly with the base station, the nodes can form clusters to. To alleviate this drawback, evolutionary based robust distributed clustering techniques are proposed in this paper. In this paper, we model the spatial distribution of sensor nodes in a cluster of wsn using gaussian pdf. An energy efficient hierarchical clustering algorithm for. In this paper, we concentrate on a recent hierarchical routing protocols, which are depending on leach protocol to enhance its performance and increase the lifetime of wireless sensor network.
We design a clustering strategy for the parallel execution of general skyline queries on the non spatial attributes of the spatial skyline, and conduct the spatial. The topology adaptive spatial clustering tasc algorithm presented here is a distributed algorithm that partitions the network into a set of locally isotropic, nonoverlapping clusters without. Thus we need our second componentin the routing scheme that complements the approximate distance oracle, which is our main contribution. We propose a modification to distributed localization. Distributed data clustering in sensor networks sets. Keywords sensor networks, distributed clustering, robust aggregation 1 introduction to analyze large data sets, it is common practice to employ clustering 6. Data volume in wireless sensor networks tends to grow continuously in both input and output. Sensors free fulltext a data clustering algorithm for detecting. Distributed energyefficient clustering algorithm for. To alleviate this drawback, evolutionary based robust distributed clustering techniques are proposed in.
Optimal energy aware clustering in sensor netw orks soheil ghiasi, ankur srivastava, xiaojian yang. Dhcs is distributed, thus can fully utilize nodes computation ability. An energy efficient hierarchical clustering algorithm for wireless. Data gathering is a common but critical operation in many applications of wireless sensor networks. Department of computer science, university of california, santa barbara, santa barbara, ca.
Ma et al distributed clustering based 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. Distributed optimization in sensor networks michael rabbat and robert nowak. A stable election protocol for clustered heterogeneous. A distributed clustering algorithm is proposed based on spatial data correlation among sensor nodes with data accuracy. Geometrybased distributed spatial skyline queries in wireless sensor networks yan wang 1,2, baoyan song 1, junlu wang 1. Innovative techniques that improve energy efficiency to prolong the network lifetime are highly required. Fernandes school of computer science university of manchester manchester m 9pl, uk email. Distributed localization for anisotropic sensor networks. Wireless sensor networks wsns are special networks consist of devices sensor nodes in large numbers and spatial distribution. By choosing dynamic cluster head, this problem can be eliminated. Such data characteristics can be efficiently extracted through spatial clustering, which partitions the network into a set of spatial regions with similar.
Considering that environmental monitoring involves more spatial attributes, and often incurs a lot of computational cost for general skyline queries, we propose the geometrybased distributed spatial skyline query method in wireless sensor networks gdssky. In this paper we propose a new clustering approach for very large spatial datasets. In this simulation, the total forwarding rates of normal chs are located. 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. Research article hierarchical spatial clustering in. Lncs 4505 distributed, hierarchical clustering and. 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. We evaluate the behavior of our approach and show that our mechanism provides an efficient and scalable way to run spatial queries over sparse and dense sensor networks. Pdf adaptive distributed indexing for spatial queries in. The topology adaptive spatial clustering tasc algorithm is a distributed algorithm that partitions the network into a set of locally isotropic, nonoverlapping clusters without prior knowledge of the number of clusters, cluster size and node coordinates. Abstract wireless sensor networks are capable of collecting an enormous amount of data over space and time. Dynamic clustering and compressive data gathering algorithm for energyefficient wireless sensor networks ce zhang, xia zhang, ou li, yanping yang and guangyi liu abstract existing clustering algorithms of data gathering in wireless sensor networks neglect the impact of event source on the data spatial correlation.
Advancedlevel students studying computer science and electrical engineering will also find the content helpful. A distributed clustering algorithm guided by the base. S jamadagni 1centre for electronics design and technology, indian institute of science, bangalore, india. Lncs 3896 distributed spatial clustering in sensor networks. Distributed positioning and tracking in clusterbased wireless sensor networks chinder wann 1 and chihying lee2 1department of computer and communication engineering national kaohsiung first university of science and technology, kaohsiung 81164, taiwan. Clustering is an effective topology control approach in wireless sensor networks, which can increase network scalability and lifetime. For the skyline query in the sensor network, the most common method is the cut method based on a filter 18,19,20,21,22,23. Pdf distributed data mining techniques and mainly distributed clustering are widely used. We develop a distributed spatial index over the sensor nodes that is used in processing spatial queries in a distributed fashion. Using these spatial clusters, we show that rangequeries canprune large portions of network leading to huge communicationgains. Distributed, hierarchical clustering in sensor networks article pdf available in journal of computer science and technology 264. Clustering tasc algorithm is a distributed algorithm that partitions the network into a set of locally isotropic, nonoverlapping. Pdf data accuracy model for distributed clustering. A new algorithm for cluster head selection in leach.
We show that the behavior of such sensor networks becomes very unstable once the. Distributed spatial analysis in wireless sensor networks. Distributed clusteringbased aggregation algorithm for. Anisotropic networks are characterized by the properties that vary with the direction of measurement. The wellliked criterion for clustering method 5 is to select a cluster head. Classical clustering protocols assume that all the.
First, according to spatial correlations between sensor nodes, a distributed clustering mechanism based on data gradient and residual energy level is proposed, and the whole network is divided. The sensor nodes can then be operated with distributed algorithms. Scientists can gain valuable insight into these phenomena, if they understand the underlying data distribution. Scientists can gain valuable insights intophysical phenomena if they understand the structure and shape inherentin the underlying data distribution. Unfortunately, these traditional clustering methods are infeasible in sensor networks, because they are mostly centralized. These characteristics are the result of geographic shape of the region, different node densities, irregular radio patterns and anisotropic terrain conditions. Scientists can gain valuable insight into these phenomena, if they understand the. A distributed and compact routing using spatial distributions. Instead of gathering data from every node in the cluster, only a set of cluster representatives need to be sampled based on their spatiotemporal correlations.
In order to understand the sentinel sets, we begin with a decomposition of the sensor network. We suppose that any sensor can be either a regular sensor or a ch and that every regular sensor has to be connected to a ch, i. Geometrybased distributed spatial skyline queries in. Collaborative beamforming for wireless sensor networks with gaussian distributed sensor nodes. Given this hypothesis, this article proposed an adaptive sampling algorithm based on temporal and spatial correlation of sensory data for clustered wsns. Sensors free fulltext geometrybased distributed spatial. Distributed, hierarchical clustering and summarization in sensor networks 169 station after the sensory readings are collected there. Centralized and distributed clustering methods for energy e. A distributed energyefficient clustering protocol for. Distributed spatial analysis in wireless sensor networks farhana jabeen and alvaro a. Spatial skyline queries can be used in wireless sensor networks for collaborative positioning of multiple objects.
Current clustering approaches typically select nodes based on their distance to the target or detection capability 5. International journal of distributed sensor networks equivalentimportance. Collaborative beamforming for wireless sensor networks. International journal of distributed dynamic clustering and. Apart from sensor networks, clustering has been applied tremendously in fields like vlsicad and data mining 10.
Along the way, we present a few basic and illustrative distributed algorithms. In wireless sensor networks, it is already noted that nearby sensor nodes monitoring an environmental feature typically register similar values. Pdf a wireless sensor network wsnconsisting of a large number of tiny sensors can be an effective. Distributed positioning and tracking in cluster based wireless sensor networks chinder wann 1 and chihying lee2 1department of computer and communication engineering national kaohsiung first university of science and technology, kaohsiung 81164, taiwan 2macronix international co. Distributed spatial skyline query processing in wireless sensor networks. We investigate that due to deployment of high density of sensor nodes in the sensor field, spatial data are highly correlated. The algorithm uses the spatial correlation between the sensed data of the sensors to build the clusters. In this article we discuss the relation between distributed computing theory and sensor network applications. In this paper, we propose a distributed clustering protocol for sensor networks, called energyef.
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