Random distributed multiresolution representations with significance querying

We propose random distributed multiresolution representations of sensor network data, so that the most significant encoding coefficients are easily accessible by querying a few sensors, anywhere in the network. Less significant encoding coefficients are available by querying a larger number of senso...

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Bibliographic Details
Published in:IPSN 2006 : the Fifth International Conference on Information Processing in Sensor Networks : April 19-21, 2006, Nashville, Tennessee, USA pp. 102 - 108
Main Authors: Wang, Wei, Ramchandran, Kannan
Format: Conference Proceeding
Language:English
Published: New York, NY, USA ACM 19.04.2006
IEEE
Series:ACM Conferences
Subjects:
arbitrary communication graphs
Computer networks
Computer systems organization > Architectures > Distributed architectures
Distributed computing
Encoding
Multiresolution representations
Permission
robust distributed algorithms
Robustness
Signal processing algorithms
Signal resolution
Software and its engineering > Software organization and properties > Software system structures > Distributed systems organizing principles
Spatial resolution
Wireless communication
Wireless sensor networks
wireless sensor networks
arbitrary communication graphs
multiresolution representations
robust distributed algorithms
ISBN:9781595933348, 1595933344
Online Access:Get full text
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Summary:We propose random distributed multiresolution representations of sensor network data, so that the most significant encoding coefficients are easily accessible by querying a few sensors, anywhere in the network. Less significant encoding coefficients are available by querying a larger number of sensors, local to the region of interest. Significance can be defined in a multiresolution way, without any prior knowledge of the source data, as global summaries versus local details. Alternatively, significance can be defined in a data-adaptive way, as large differences between neighboring data values. We propose a distributed encoding algorithm that is robust to arbitrary wireless communication connectivity graphs, where links can fail or change with time. This randomized algorithm allows distributed computation that does not require strict global coordination or awareness of network connectivity at individual sensors. Because computations involve sensors in local neighborhoods of the communication graph, they are communication-efficient. Our framework uses local interaction among sensors to enable flexible information retrieval at the global level.
Bibliography:SourceType-Conference Papers & Proceedings-1
ObjectType-Conference Paper-1
content type line 25
ISBN:9781595933348
1595933344
DOI:10.1145/1127777.1127796