SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Analysis of the Clustering Properties of the Hilbert Space-Filling Curve
IEEE Transactions on Knowledge and Data Engineering
Directed diffusion for wireless sensor networking
IEEE/ACM Transactions on Networking (TON)
Approximate Query Processing Using Wavelets
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Multi-dimensional range queries in sensor networks
Proceedings of the 1st international conference on Embedded networked sensor systems
Distributed regression: an efficient framework for modeling sensor network data
Proceedings of the 3rd international symposium on Information processing in sensor networks
Compressing historical information in sensor networks
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Exploiting Correlated Attributes in Acquisitional Query Processing
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
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
Proceedings of the 3rd international conference on Embedded networked sensor systems
ExScal: Elements of an Extreme Scale Wireless Sensor Network
RTCSA '05 Proceedings of the 11th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications
Wavelet synopses for general error metrics
ACM Transactions on Database Systems (TODS) - Special Issue: SIGMOD/PODS 2004
Deploying a Wireless Sensor Network on an Active Volcano
IEEE Internet Computing
Approximate Data Collection in Sensor Networks using Probabilistic Models
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
ProcessingWindow Queries in Wireless Sensor Networks
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Online outlier detection in sensor data using non-parametric models
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Modeling spatially correlated data in sensor networks
ACM Transactions on Sensor Networks (TOSN)
Supporting Multi-Dimensional Range Query for Sensor Networks
ICDCS '07 Proceedings of the 27th International Conference on Distributed Computing Systems
Model-driven data acquisition in sensor networks
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Region Sampling: Continuous Adaptive Sampling on Sensor Networks
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
GAMPS: compressing multi sensor data by grouping and amplitude scaling
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Contour approximation in sensor networks
DCOSS'06 Proceedings of the Second IEEE international conference on Distributed Computing in Sensor Systems
| Hi-index | 0.00 |
Data approximation is a popular means to support energy-efficient query processing in sensor networks. Conventional data approximation methods require users to specify fixed error bounds a prior to address the trade-off between result accuracy and energy efficiency of queries. We argue that this can be infeasible and inefficient when, as in many real-world scenarios, users are unable to determine in advance what error bounds can lead to affordable cost in query processing. We envision ε-approximate querying (EAQ) to bridge the gap. EAQ is a uniform data access scheme underlying various queries in sensor networks. It allows users or query executors to incrementally 'refine' previously obtained approximate data to reach arbitrary accuracy. EAQ not only grants more flexibility to in-network query processing, but also minimizes energy consumption through communicating data upto a just-sufficient level. To enable the EAQ scheme, we propose a novel data shuffling algorithm. The algorithm converts sensed datasets into special representations called multi-version array (MVA). From prefixes of MVA, we can recover approximate versions of the entire dataset, where all individual data items have guaranteed error bounds. The EAQ scheme supports efficient and flexible processing of various queries including spatial window query, value range query, and queries with QoS constraints. The effectiveness and efficiency of the EAQ scheme are evaluated in a real sensor network testbed.