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Supporting spatial aggregation in sensor network databases

Mehdi Sharifzadeh, Cyrus Shahabi
2004 Proceedings of the 12th annual ACM international workshop on Geographic information systems - GIS '04  
In this paper, we introduce "spatial aggregations" that take into consideration the distribution of the values generated by the sensor nodes.  ...  To demonstrate the performance of in-network implementation of our algorithm, we have developed prototypes of two different approaches to distributed spatial aggregate processing.  ...  In this paper, we introduced the spatial aggregation for sensor network databases.  ... 
doi:10.1145/1032222.1032248 dblp:conf/gis/SharifzadehS04 fatcat:j3exti4yknf5va4zv7kobd4p4q

Cluster By

Chengyang Zhang, Yan Huang
2007 Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems - GIS '07  
In this paper, we aim at introducing Cluster By into spatial databases to allow a broad range of interesting queries to be posted on spatial clusters.  ...  These datasets necessitate the wide application of spatial databases.  ...  Our work is further related to sensor database systems due to the inherent georeferenced nature of data collected by sensor networks.  ... 
doi:10.1145/1341012.1341077 dblp:conf/gis/ZhangH07 fatcat:ewcwixbwpveuhcy7y7qf7jqpby

Visualizing large sensor network data sets in space and time with vizzly

Matthias Keller, Jan Beutel, Olga Saukh, Lothar Thiele
2012 37th Annual IEEE Conference on Local Computer Networks -- Workshops  
A user is completely free in selecting sensor data based on time and location, suitable levels of temporal and spatial detail are automatically chosen by the Vizzly server.  ...  This paper presents Vizzly, a middleware for the interactive browsing of large sensor network data sets.  ...  The work presented was supported by NCCR-MICS, a center supported by the Swiss National Science Foundation, under grant number 5005-67322.  ... 
doi:10.1109/lcnw.2012.6424084 dblp:conf/lcn/KellerBST12 fatcat:7ddelfhdgjb75ezey4jwyetevy

SAQA: Spatial and Attribute Based Query Aggregation in Wireless Sensor Networks [chapter]

Yang Jie, Yan Bo, Sungyoung Lee, Jinsung Cho
2006 Lecture Notes in Computer Science  
In most wireless sensor networks, applications submit their requests as queries and wireless sensor network transmits the requested data to the applications.  ...  In this paper, we design an effective query aggregation algorithm SAQA to reduce the number of duplicate/overlapping queries and save overall energy consumption in the wireless sensor networks.  ...  Acknowledgement This work was supported by IITA Professorship for Visiting Faculty Positions in Korea from Ministry of Information and Communications.  ... 
doi:10.1007/11802167_4 fatcat:3fqrwkp2dng7tjkdrmbkpchosa

Window Query Processing in Highly Dynamic GeoSensor Networks [chapter]

Wang-Chien Lee, Yingqi Xu
2004 GeoSensor Networks  
However, these existing works have not yet explored the spatial property and the dynamic characteristics of sensor networks.  ...  A database-style query interface is natural for development of applications and systems on sensor networks. There are projects pursuing this research direction [14, 15, 25] .  ...  We are particularly interested in window query because it is one of the most fundamental and important queries supported in spatial databases.  ... 
doi:10.1201/9780203356869.ch3 fatcat:7hyxooxcanewnh44fzyujh2pcq

Wireless Sensor Networks Fault Identification Using Data Association

Y.
2012 Journal of Computer Science  
In this study, it is proposed to investigate the association of data received in the sink from various nodes across the network.  ...  The existence of a large communication overhead affects sensor network performance negatively.  ...  Spatial association rules describe the implication of one or a set of features by another set of features in spatial databases.  ... 
doi:10.3844/jcssp.2012.1501.1505 fatcat:phfn63jvpra7hbxxhrtiiu6x7u

Geohashed Spatial Index Method for a Location-Aware WBAN Data Monitoring System Based on NoSQL

2016 Journal of Information Processing Systems  
In order to efficiently analyze WBAN data, including location information and support medical analysis services, we propose a geohash-based spatial index method for a location-aware WBAN data monitoring  ...  This type of spatial index method is a support cloud-based location-aware WBAN data monitoring system.  ...  In order to support location-aware services, we have proposed a new spatial index algorithm for a document-based NoSQL database.  ... 
doi:10.3745/jips.04.0025 fatcat:2gsuh5dutvfapieql4vmdbq3im

Data Management in the Worldwide Sensor Web

Magdalena Balazinska, Amol Deshpande, Michael J. Franklin, Phillip B. Gibbons, Jim Gray, Mark Hansen, Michael Liebhold, Suman Nath, Alexander Szalay, Vincent Tao
2007 IEEE pervasive computing  
Sensor data contain numerous sources of noise and uncertainty, some of which are assessable only after spatial or temporal aggregation.  ...  Can we design sensor networks with data quality in mind?  ...  Her research interests are broadly in the fields of databases and systems.  ... 
doi:10.1109/mprv.2007.27 fatcat:4csakiyjyvc33hl6slpbyvgciy

Processing Multiple Aggregation Queries in Geo-Sensor Networks [chapter]

Ken C. K. Lee, Wang-Chien Lee, Baihua Zheng, Julian Winter
2006 Lecture Notes in Computer Science  
To process aggregation queries issued through different sensors as access points in sensor networks, existing algorithms handle queries independently and perform in-network aggregation only at the query  ...  In this paper, we address this issue by examining strategies to maintain Materialized In-Network Views (MINVs) that pre-compute and store commonly used aggregation results in the sensor network.  ...  Acknowledgements In this research, Wang-Chien Lee and Ken C.K. Lee were supported in part by US National Science Foundation grant IIS-0328881.  ... 
doi:10.1007/11733836_4 fatcat:jhvlr3rbxrebblisux5jcf7zeu

Event Broker Grids with Filtering, Aggregation, and Correlation for Wireless Sensor Data [chapter]

Eiko Yoneki
2005 Lecture Notes in Computer Science  
To manage high volume and faulty sensor data, it requires more sophisticated event filtering, aggregation and correlation over time and space in heterogeneous network environments.  ...  A significant increase in real world event monitoring capability with wireless sensor networks brought a new challenge to ubiquitous computing.  ...  However, a mainstream of deployments of sensor networks is to collect all the data from the sensor networks and to store them in database. Data analysis is preceded from the data in the database.  ... 
doi:10.1007/11575863_50 fatcat:tqvrmsgs7bbkpo5jaww3micoci

A Multi-Stage Method for Connecting Participatory Sensing and Noise Simulations

Mingyuan Hu, Weitao Che, Qiuju Zhang, Qingli Luo, Hui Lin
2015 Sensors  
partitions at a more microscopic level of road networks; (2) multi-temporal scale noise estimations at the spatial level of virtual partitions; and (3) dynamic aggregation of virtual partitions by comparing  ...  Next, we explain how participatory noise OPEN ACCESS Sensors 2015, 15 2266 data can contribute to a simulation-based noise map by providing (1) spatial matching of the participatory noise data to the virtual  ...  Acknowledgments The work described in this study was supported by the National Natural Science Foundation of China .  ... 
doi:10.3390/s150202265 pmid:25621604 pmcid:PMC4367305 fatcat:3mvtssi6hbgtxgjf3dx7gdklju

Utilizing Voronoi Cells of Location Data Streams for Accurate Computation of Aggregate Functions in Sensor Networks

Mehdi Sharifzadeh, Cyrus Shahabi
2007 Geoinformatica  
We also propose AVC-SW, a novel algorithm to approximate this Voronoi cell over a sliding window that supports dynamism in the sensor network.  ...  Sensor networks are unattended deeply distributed systems whose database schema can be conceptualized using the relational model.  ...  Acknowledgments This research is based upon work supported in part by the National Science Foundation under award numbers IIS-0324955(ITR), EEC-9529152 (IMSC ERC) and IIS-0238560 (CAREER), in part by a  ... 
doi:10.1007/s10707-007-0024-1 fatcat:unjmk7rklbeebe6ktrbzix6bo4

Utilizing Voronoi Cells of Location Data Streams for Accurate Computation of Aggregate Functions in Sensor Networks

Mehdi Sharifzadeh, Cyrus Shahabi
2006 Geoinformatica  
We also propose AVC-SW, a novel algorithm to approximate this Voronoi cell over a sliding window that supports dynamism in the sensor network.  ...  Sensor networks are unattended deeply distributed systems whose database schema can be conceptualized using the relational model.  ...  Acknowledgments This research is based upon work supported in part by the National Science Foundation under award numbers IIS-0324955(ITR), EEC-9529152 (IMSC ERC) and IIS-0238560 (CAREER), in part by a  ... 
doi:10.1007/s10707-005-4884-y fatcat:l2yihv7bpfgllkiutc4x2tn3pm

A Distributed Spatial Index for Time-Efficient Aggregation Query Processing in Sensor Networks [chapter]

Soon-Young Park, Hae-Young Bae
2005 Lecture Notes in Computer Science  
In this paper, distributed spatial index structure in sensor networks for time-efficient aggregation query processing is proposed.  ...  Many applications using sensing data require the fast retrieval of aggregated information in sensor networks.  ...  In this paper, distributed spatial index structure in sensor networks is proposed for time-efficient aggregation query processing.  ... 
doi:10.1007/11428831_50 fatcat:ucwakr5gffdejayytw2fig6pcm

Query Processing in Sensor Networks [chapter]

Samuel Madden, Johannes Gehrke
2012 Distributed Sensor Networks, Second Edition  
We examine the main architectural components of such a query layer, concentrating on in-network aggregation, interaction of in-network aggregation with the wireless routing protocol, and distributed query  ...  In this paper, we evaluate the design of a query layer for sensor networks.  ...  We thank the DARPA Sen-sIT community for helpful discussions about the role of query processing in sensor networks.  ... 
doi:10.1201/b12988-20 fatcat:k44vrdclg5hk3fvgatdipgehim
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