The design and analysis of spatial data structures
The design and analysis of spatial data structures
SIGGRAPH '92 Proceedings of the 19th annual conference on Computer graphics and interactive techniques
Computational geometry: algorithms and applications
Computational geometry: algorithms and applications
GPSR: greedy perimeter stateless routing for wireless networks
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
The cougar approach to in-network query processing in sensor networks
ACM SIGMOD Record
Supporting Aggregate Queries Over Ad-Hoc Wireless Sensor Networks
WMCSA '02 Proceedings of the Fourth IEEE Workshop on Mobile Computing Systems and Applications
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
The design of an acquisitional query processor for sensor networks
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Multi-dimensional range queries in sensor networks
Proceedings of the 1st international conference on Embedded networked sensor systems
An evaluation of multi-resolution storage for sensor networks
Proceedings of the 1st international conference on Embedded networked sensor systems
Supporting spatial aggregation in sensor network databases
Proceedings of the 12th annual ACM international workshop on Geographic information systems
Distributed Techniques for Area Computation in Sensor Networks
LCN '04 Proceedings of the 29th Annual IEEE International Conference on Local Computer Networks
Approximately uniform random sampling in sensor networks
DMSN '04 Proceeedings of the 1st international workshop on Data management for sensor networks: in conjunction with VLDB 2004
TAG: a Tiny AGgregation service for Ad-Hoc sensor networks
OSDI '02 Proceedings of the 5th symposium on Operating systems design and implementationCopyright restrictions prevent ACM from being able to make the PDFs for this conference available for downloading
Exact distributed Voronoi cell computation in sensor networks
Proceedings of the 6th international conference on Information processing in sensor networks
Supporting situation-aware services with virtual macro sensors
Proceedings of the 2007 Workshop on INnovative SERvice Technologies
A two round reporting approach to energy efficient interpolation of sensor fields
SSTD'07 Proceedings of the 10th international conference on Advances in spatial and temporal databases
Efficient tracking of 2D objects with spatiotemporal properties in wireless sensor networks
Distributed and Parallel Databases
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Recent research literature on sensor network databases has focused on finding ways to perform in-network aggregation of sensor readings to reduce the message cost. However, with these techniques information about the state at a particular location is lost. In many applications such as visualization, finite element analysis, and cartography, constructing a field from all sensor readings is very important. However, requiring all sensors to report their readings to a centralized station adversely impacts the life span of the sensor network. In this paper we focus on modeling sensor networks as a field deployed in a physical space and exploiting in-network surface simplification techniques to reduce the message cost. In particular, we propose two schemes for performing in-network surface simplification, namely (1) a hierarchical approach and (2) a triangulation based approach. We focus on a quad tree based method and a decimation method for the two approaches respectively. The quad tree based method employs an incremental refinement process during reconstruction using increasingly finer levels of detail sent by selected sensors. It has a guaranteed error bound. The decimation method starts with a triangulation of all sensors and probabilistically selects sensors not to report to prevent error accumulation. To demonstrate the performance, the two simplification techniques are compared with the naive approach of having all sensors report. Experimental results show that both techniques provide substantial message savings compared to the naivealgorithm, usually requiring less than 80% as many messages and less than 50% for some data sets. Furthermore, though the decimation algorithm does not provide a guaranteed error bound, for our experiments less than 4.5% of the interpolated values exceeded the given bound.