A stable election protocol for clustered heterogeneous. Distributed spatial correlationbased clustering for. International journal of distributed sensor networks equivalentimportance. 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. Thus we need our second componentin the routing scheme that complements the approximate distance oracle, which is our main contribution. Anisotropic networks are characterized by the properties that vary with the direction of measurement. Along the way, we present a few basic and illustrative distributed algorithms. Often, the ultimate objective is to derive an estimate of a parameter or function from these data. International journal of distributed spatiotemporal.
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. There have been some distributed clustering methods in sensor networks, such as elink 6 and dsic 8. Fernandes school of computer science university of manchester manchester m 9pl, uk email. Because of the multidimensional nature of the problem of monitoring spatial position, traditional skyline query strategies cause enormous computational costs and. This kind of data redundancy due to the spatial correlation between sensor observations inspires the. Lncs 3896 distributed spatial clustering in sensor networks. The algorithm uses the spatial correlation between the sensed data of the sensors to build the clusters.
S jamadagni 1centre for electronics design and technology, indian institute of science, bangalore, india. Because of the multidimensional nature of the problem of monitoring spatial position, traditional skyline query strategies cause enormous computational costs and energy consumption. 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. Clustering tasc algorithm is a distributed algorithm that partitions the network into a set of locally isotropic, non overlapping. A distributed and compact routing using spatial distributions. The sensor nodes can then be operated with distributed algorithms. To ensure the efficient use of sensor energy, a geometrybased distributed. Clustering is an effective topology control approach in wireless sensor networks, which can increase network scalability and lifetime. Such data characteristics can be efficiently extracted through spatial clustering, which partitions the network into a set of spatial regions with similar observations. Pdf data accuracy model for distributed clustering. We develop a distributed spatial index over the sensor nodes that is used in processing spatial queries in a distributed fashion. We address the problem of discovering spatial relationships in sensor data through the identification of clusters. Adaptive distributed indexing for spatial queries in. Index terms biological algorithms, clustering, spatial data dependency, sensor networks i.
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. Sensors free fulltext a data clustering algorithm for detecting. Data volume in wireless sensor networks tends to grow continuously in both input and output. Data accuracy model for distributed clustering algorithm based on. Distributed spatial clustering in sensor networks ucsb. 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. 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. 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. The clustered aggregation cag technique leveraging spatial. As opposd to communicating directly with the base station, the nodes can form clusters to. In clustering, the data values are partitioned into several clusters, and each clus. 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.
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. A heterogeneous energy wireless sensor network clustering. Collaborative beamforming for wireless sensor networks with gaussian distributed sensor nodes. Pdf a survey on clustering algorithms for wireless sensor. Distributed algorithms are an established tool for designing protocols for sensor networks. 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. A distributed energyefficient clustering protocol for. In this paper, we model the spatial distribution of sensor nodes in a cluster of wsn using gaussian pdf. Distributed spatial clustering in sensor networks 985.
Sensors free fulltext geometrybased distributed spatial. Keywords sensor networks, distributed clustering, robust aggregation 1 introduction to analyze large data sets, it is common practice to employ clustering 6. 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. Pdf distributed clustering algorithm for spacial data mining.
An energy efficient hierarchical clustering algorithm for. Distributed, hierarchical clustering and summarization in sensor networks 169 station after the sensory readings are collected there. 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. As the numbers of sensor nodes are more in the sensor field, the data. A concise way to extract thisstructure is through \\emph spatial clustering, which partitions thenetwork into a set of spatial regions with similar observations. A distributed clustering algorithm is proposed based on spatial data correlation among sensor nodes with data accuracy. In this paper, we propose a distributed clustering protocol for sensor networks, called energyef. Spatial clustering also serves to prolong network lifetime. Distributed spatial skyline query processing in wireless sensor networks. By choosing dynamic cluster head, this problem can be eliminated. Sensor networks monitor physical phenomena over large geographic regions. Centralized and distributed clustering methods for energy e. However, this centralized approach has two major drawbacks. Distributed spatial skyline query processing in wireless.
Scientists can gain valuable insight into these phenomena, if they understand the underlying data distribution. Innovative techniques that improve energy efficiency to prolong the network lifetime are highly required. Centralized and distributed clustering methods for energy. In wireless sensor networks, it is already noted that nearby sensor nodes monitoring an environmental feature typically register similar values. Distributed positioning and tracking in clusterbased.
Pdf a wireless sensor network wsnconsisting of a large number of tiny sensors can be an effective. A biologicallyinspired clustering algorithm dependent on. Using these spatial clusters, we show that rangequeries canprune large portions of network leading to huge communicationgains. The wellliked criterion for clustering method 5 is to select a cluster head. Distributed spatial analysis in wireless sensor networks a thesis submitted to the university of manchester for the degree of doctor of philosophy. Given this hypothesis, this article proposed an adaptive sampling algorithm based on temporal and spatial correlation of sensory data for clustered wsns. 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.
An energy efficient hierarchical clustering algorithm for wireless sensor networks seema bandyopadhyay and edward j. 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. Distributed robust data clustering in wireless sensor. Research article hierarchical spatial clustering 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. 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. 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. Abstract wireless sensor networks are capable of collecting an enormous amount of data over space and time. In this paper we propose a new clustering approach for very large spatial datasets. Data gathering is a common but critical operation in many applications of wireless sensor networks. Distributed consensus estimation is a valuable resource for researchers and professionals working in wireless communications, networks and distributed computing.
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. Pdf wireless sensor networks wsns are resource constrained systems that needs efficient. 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. Geometrybased distributed spatial skyline queries in wireless sensor networks yan wang 1,2, baoyan song 1, junlu wang 1. Distributed energyefficient clustering algorithm for. Apart from sensor networks, clustering has been applied tremendously in fields like vlsicad and data mining 10. Dhcs is distributed, thus can fully utilize nodes computation ability. Such data characteristics can be efficiently extracted through spatial clustering, which partitions the network into a set of spatial regions with similar.
In order to understand the sentinel sets, we begin with a decomposition of the sensor network. Distributed spatial clustering in sensor networks springerlink. To alleviate this drawback, evolutionary based robust distributed clustering techniques are proposed in. Pdf optimal number of clusters in wireless sensor networks.
Algorithms for skyline querying based on wireless sensor networks wsns have been widely used in the field of environmental monitoring. To alleviate this drawback, evolutionary based robust distributed clustering techniques are proposed in this paper. Collaborative beamforming for wireless sensor networks. Department of computer science, university of california, santa barbara, santa barbara, ca. We investigate that due to deployment of high density of sensor nodes in the sensor field, spatial data are highly correlated. Distributed data clustering in sensor networks sets. Distributed spatial analysis in wireless sensor networks. Abstract sensor networks have opened new horizons and opportunities for a variety of environmental monitoring, surveillance and healthcare applications. Distributed clustering based aggregation algorithm for spatial correlated sensor networks abstract. Clustering is an efficient topology control approach, which can extend the lifetime and raise the scalability of these sensor networks. 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. Distributed, hierarchical clustering in sensor networks article pdf available in journal of computer science and technology 264. Citeseerx distributed spatial clustering in sensor networks.
Advancedlevel students studying computer science and electrical engineering will also find the content helpful. The total forwarding rates produce randomly as listed in table 4. 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. Unfortunately, these traditional clustering methods are infeasible in sensor networks, because they are mostly centralized. Distributed clusteringbased aggregation algorithm for. Duetothe adhoc natureandresource constraints of wsns, distributed spatial clustering would be abetterchoice. Distributed optimization in sensor networks michael rabbat and robert nowak. In this article we discuss the relation between distributed computing theory and sensor network applications. 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.
Optimal energy aware clustering in sensor netw orks soheil ghiasi, ankur srivastava, xiaojian yang. Clustering in distributed incremental estimation in wireless sensor networks sunghyun son, mung chiang, sanjeev r. Research on scalable clustering algorithms has been fruitful 2, 10, 11. Spatial skyline queries can be used in wireless sensor networks for collaborative positioning of multiple objects. In this simulation, the total forwarding rates of normal chs are located.
Distributed compact routing using spatial distributions in wireless sensor networks a. 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. Clustering tasc algorithm is a distributed algorithm that partitions the network into a set of locally isotropic, nonoverlapping. Distributed spatial clustering in sensor networks anand meka and ambuj k.
A biologicallyinspired clustering algorithm dependent on spatial. One of the major tasks of sensor networks is the distributed collection and processing of sensor. Density gridbased clustering for wireless sensors networks. However, in applications where targets may frequently travel in the same section e. Topology adaptive spatial clustering for sensor networks. Singh department of computer science, university of california, santa barbara santa barbara, ca. Pdf an energy efficient clustering scheme in wireless. Pdf adaptive distributed indexing for spatial queries in. 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. A new algorithm for cluster head selection in leach. The goal of this paper is to perform such a spatial clustering, specifically. Wireless sensor networks wsns are special networks consist of devices sensor nodes in large numbers and spatial distribution.
Index terms biological algorithms, clustering, spatial data dependency, sensor. Current clustering approaches typically select nodes based on their distance to the target or detection capability 5. These characteristics are the result of geographic shape of the region, different node densities, irregular radio patterns and anisotropic terrain conditions. Research article hierarchical spatial clustering in multihop. In consideration of these drawbacks, we take a distributed approach to clus. Realworld applications of distributed clustering mechanism. We propose a modification to distributed localization. Geometrybased distributed spatial skyline queries in.
Wsns measure environmental conditions like temperature, sound, pollution levels, humidity, wind, and so on. Distributed spatial clustering in sensor networks core. Home browse by title proceedings edbt06 distributed spatial clustering in sensor networks. This paper focuses on extracting regular structures through the distributed clustering scheme and later on carrying out the localization procedure. Keywords sensor networks, distributed cluster ing, robust aggregation 1 introduction to analyze large data sets, it is common practice to employ clustering 6. On the optimal randomized clustering in distributed sensor. A distributed clustering algorithm guided by the base. 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. Data accuracy model for distributed clustering algorithm based on spatial data correlation in wireless sensor networks 1jyotirmoy karjee,2h. 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. Attributebased clustering for information dissemination.
Data accuracy model for distributed clustering algorithm. We show that the behavior of such sensor networks becomes very unstable once the. To simplify the discussion, we assume a square grid ofn nodes. Distributed data clustering in sensor netw orks sets.
Introduction lustering is a useful technique to adopt in sensor networks when collecting the data measured at a central base station. An energy efficient hierarchical clustering algorithm for wireless. Parallel clustering differs from distributed clustering in that all the data is available to all processes, or is carefully. Such data characteristics can be efficiently extracted through spatial clustering, which partitions the network into a set of spatial regions. In this paper, we propose a novel energy efficient. Scientists can gain valuable insight into these phenomena, if. Scientists can gain valuable insights intophysical phenomena if they understand the structure and shape inherentin the underlying data distribution. Classical clustering protocols assume that all the. International journal of distributed dynamic clustering and. 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. However, their clustering processes all depend on some communication infrastructure, such. This kind of data redundancy due to the spatial correlation between sensor observations inspires the research of in network data aggregation.
International journal of distributed dynamic clustering. Gaussian pdf is more suitable in many wsn applications than, for example, uniform pdf which is commonly used for flat ad hoc networks. Lncs 4505 distributed, hierarchical clustering and. Distributed spatial analysis in wireless sensor networks farhana jabeen and alvaro a. 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 distributed data mining techniques and mainly distributed clustering are widely used. More, dhcs considers both data similarity and spatial proximity. Distributed localization for anisotropic sensor networks.
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