Wireless sensor networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Novelty detection: a review—part 1: statistical approaches
Signal Processing
TinyDB: an acquisitional query processing system for sensor networks
ACM Transactions on Database Systems (TODS) - Special Issue: SIGMOD/PODS 2003
Energy and quality aware query processing in wireless sensor database systems
Information Sciences: an International Journal
OLINDDA: a cluster-based approach for detecting novelty and concept drift in data streams
Proceedings of the 2007 ACM symposium on Applied computing
Information fusion for wireless sensor networks: Methods, models, and classifications
ACM Computing Surveys (CSUR)
Toward adaptive query processing in wireless sensor networks
Signal Processing
An adaptive in-network aggregation operator for query processing in wireless sensor networks
Journal of Systems and Software
Information fusion in wireless sensor networks
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Quality-of-service trade-off analysis for wireless sensor networks
Performance Evaluation
Novelty detection with application to data streams
Intelligent Data Analysis - Knowledge Discovery from Data Streams
Class-based continuous query scheduling for data streams
Proceedings of the Sixth International Workshop on Data Management for Sensor Networks
Ensuring high sensor data quality through use of online outlier detection techniques
International Journal of Sensor Networks
A Survey of Outlier Detection Methods in Network Anomaly Identification
The Computer Journal
Outlier Detection Techniques for Wireless Sensor Networks: A Survey
IEEE Communications Surveys & Tutorials
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This paper introduces the concept of quality of queries (QoQs) towards a more adaptive query processing in wireless sensor networks (WSNs). This approach aims at the intelligent consumption of the limited resources (energy and memory) available in these networks while still delivering a reasonable level of data quality as expected by client applications. In a nutshell, the concept of QoQ stipulates that the results of different queries injected into the same WSN can be tailored according to different criteria, in particular the levels of query result accuracy and energy consumption. For this purpose, four classes of QoQ (CoQoQ) are specified having in mind distinct requirements in terms of these criteria. To allow the implementation of these classes in a real WSN setting, a new novelty-detection based algorithm, referred to as AdaQuali (which stands for ''ADAptive QUALIty control for query processing in WSN''), is also proposed in a manner as to control the sensor node activities through the dynamic adjustment of their rates of data collection and transmission. In order to validate the novel approach, simulations with a prototype implemented in Sinalgo have been conducted over real temperature data. The results achieved evidence the suitability of the proposal and point to gains of up to 66.76%, for different CoQoQ, in terms of reduction in energy consumption.