A Hierarchical Representation for Recording Semantically Condensed Data from Physically Massive Data Out of Sensor Networks Geographically Dispersed

Authors:
Minhwan Ok
Affiliations:
Korea Railroad Research Institute, Uiwang, Korea
Venue:
OTM '09 Proceedings of the Confederated International Workshops and Posters on On the Move to Meaningful Internet Systems: ADI, CAMS, EI2N, ISDE, IWSSA, MONET, OnToContent, ODIS, ORM, OTM Academy, SWWS, SEMELS, Beyond SAWSDL, and COMBEK 2009
Year:
2009

Citing 7
Cited 2

Cache-and-query for wide area sensor databases

Proceedings of the 2003 ACM SIGMOD international conference on Management of data
IrisNet: an internet-scale architecture for multimedia sensors

Proceedings of the 13th annual ACM international conference on Multimedia
A survey of middleware for sensor networks: state-of-the-art and future directions

Proceedings of the international workshop on Middleware for sensor networks
Virtual Sensor Networks - A Resource Efficient Approach for Concurrent Applications

ITNG '07 Proceedings of the International Conference on Information Technology
Data Management in the Worldwide Sensor Web

IEEE Pervasive Computing
Infrastructure for Data Processing in Large-Scale Interconnected Sensor Networks

MDM '07 Proceedings of the 2007 International Conference on Mobile Data Management
Stream feeds: an abstraction for the world wide sensor web

IOT'08 Proceedings of the 1st international conference on The internet of things

An abbreviate representation for semantically indexing of physically massive data out of sensor networks on the rove

WISS'10 Proceedings of the 2010 international conference on Web information systems engineering
A classable indexing of data condensed semantically from physically massive data out of sensor networks on the rove

UCAmI'12 Proceedings of the 6th international conference on Ubiquitous Computing and Ambient Intelligence

Quantified Score

Hi-index	0.01

Visualization

Abstract

A number of sensor networks may produce a huge amount of data, and there has been a necessity the data are processed in a single system. However the data could early overwhelm the database of the system. This work introduces a condensing method to reduce the amount of data exploiting its semantics. The condensing reduces the amount of data to be transmitted and stored, by condensing the data according to semantics shared among servers. The briefed data could diminish the load of applications running on resource-constrained devices in pervasive computing.