Bayesian forecasting and dynamic models (2nd ed.)
Bayesian forecasting and dynamic models (2nd ed.)
Adaptive precision setting for cached approximate values
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Mining massively incomplete data sets by conceptual reconstruction
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Adaptive filters for continuous queries over distributed data streams
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Understanding packet delivery performance in dense wireless sensor networks
Proceedings of the 1st international conference on Embedded networked sensor systems
Taming the underlying challenges of reliable multihop routing in sensor networks
Proceedings of the 1st international conference on Embedded networked sensor systems
Approximate Aggregation Techniques for Sensor Databases
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Adaptive stream resource management using Kalman Filters
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Versatile low power media access for wireless sensor networks
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
Mitigating congestion in wireless sensor networks
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
Synopsis diffusion for robust aggregation in sensor networks
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
Tributaries and deltas: efficient and robust aggregation in sensor network streams
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Approximate Data Collection in Sensor Networks using Probabilistic Models
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Towards correcting input data errors probabilistically using integrity constraints
MobiDE '06 Proceedings of the 5th ACM international workshop on Data engineering for wireless and mobile access
MauveDB: supporting model-based user views in database systems
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Constraint chaining: on energy-efficient continuous monitoring in sensor networks
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Trio: a system for data, uncertainty, and lineage
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Model-driven data acquisition in sensor networks
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Efficient query evaluation on probabilistic databases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Declarative support for sensor data cleaning
PERVASIVE'06 Proceedings of the 4th international conference on Pervasive Computing
PAQ: time series forecasting for approximate query answering in sensor networks
EWSN'06 Proceedings of the Third European conference on Wireless Sensor Networks
Support Vector Machines, Data Reduction, and Approximate Kernel Matrices
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
A methodology for in-network evaluation of integrated logical-statistical models
Proceedings of the 6th ACM conference on Embedded network sensor systems
An Effective and Efficient Method for Handling Transmission Failures in Sensor Networks
DASFAA '09 Proceedings of the 14th International Conference on Database Systems for Advanced Applications
Large-scale uncertainty management systems: learning and exploiting your data
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Harnessing the strengths of anytime algorithms for constant data streams
Data Mining and Knowledge Discovery
EDGES: Efficient data gathering in sensor networks using temporal and spatial correlations
Journal of Systems and Software
Continuous monitoring of global events in sensor networks
International Journal of Sensor Networks
Data filtering and dynamic sensing for continuous monitoring in wireless sensor networks
International Journal of Autonomous and Adaptive Communications Systems
Sensor networks with secure public-key over GF (2m)
ICACT'10 Proceedings of the 12th international conference on Advanced communication technology
Bernoulli sampling based (ε, δ)-approximate aggregation in large-scale sensor networks
INFOCOM'10 Proceedings of the 29th conference on Information communications
Network imprecision: a new consistency metric for scalable monitoring
OSDI'08 Proceedings of the 8th USENIX conference on Operating systems design and implementation
Analyzing space-time sensor network data under suppression and failure in transmission
Statistics and Computing
An introduction to Bayesian techniques for sensor networks
WASA'10 Proceedings of the 5th international conference on Wireless algorithms, systems, and applications
Transparent runtime parallelization of the R scripting language
Journal of Parallel and Distributed Computing
Detecting proximity events in sensor networks
Information Systems
Self-adaptive routing in multi-hop sensor networks
Proceedings of the 7th International Conference on Network and Services Management
Data caching-based query processing in multi-sink wireless sensor networks
International Journal of Sensor Networks
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Sensor networks allow continuous data collection on unprecedented scales. The primary limiting factor of such networks is energy, of which communication is the dominant consumer. The default strategy of nodes continually reporting their data to the root results in too much messaging. Suppression stands to greatly alleviate this problem. The simplest such scheme is temporal suppression, in which a node transmits its reading only when it has changed beyond some e since last transmitted. In the absence of a report, the root can infer that the value remains within ±ε hence, it is still able to derive the history of readings produced at the node. The critical weakness of suppression is message failure, to which sensor networks are particularly vulnerable. Failure creates ambiguity: a non-report may either be a suppression or a failure. Inferring the correct values for missing data and learning the parameters of the underlying process model become quite challenging. We propose a novel solution, BaySail, that incorporates the knowledge of the suppression scheme and application-level redundancy in Bayesian inference. We investigate several redundancy schemes and evaluate them in terms of in-network transmission costs and out-of-network inference efficacy, and the trade-off between these. Our experimental evaluation shows application-level redundancy outperforms retransmissions and basic sampling in both cost and accuracy of inference. The BaySail framework shows suppression schemes are generally effective for data collection, despite the presence of failures.