Flooding-Assisted Threshold Assignment for Aggregate Monitoring in Sensor Networks
ICDCN '09 Proceedings of the 10th International Conference on Distributed Computing and Networking
Ranking distributed probabilistic data
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Cost-aware reactive monitoring in resource-constrained wireless sensor networks
WCNC'09 Proceedings of the 2009 IEEE conference on Wireless Communications & Networking Conference
Distributed threshold selection for aggregate threshold monitoring in sensor networks
CCNC'09 Proceedings of the 6th IEEE Conference on Consumer Communications and Networking Conference
Reactive monitoring of aggregates in Gaussian random field over wireless sensor networks
SpringSim '10 Proceedings of the 2010 Spring Simulation Multiconference
Mining frequent itemsets over distributed data streams by continuously maintaining a global synopsis
Data Mining and Knowledge Discovery
Efficient and scalable monitoring and summarization of large probabilistic data
Proceedings of the 2013 Sigmod/PODS Ph.D. symposium on PhD symposium
An integrated framework for optimizing automatic monitoring systems in large IT infrastructures
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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Detecting constraint violations in large-scale distributed systems has recently attracted plenty of attention from the research community due to its varied applications (security, network monitoring, etc.). Communication efficiency of these systems is a critical concern and determines their practicality. In this paper, we introduce a new set of methods called non-zero slack schemes to implement distributed SUM queries efficiently. We show, both analytically and empirically, that these methods can lead to a considerable reduction in the amount of communication. We propose three adaptive non-zero slack schemes that adapt to changing data distributions; our best scheme is a lightweight reactive scheme that probabilistically adjusts local constraints based on the occurrence of certain events (using only a periodic probability estimation). We conduct an extensive experimental study using real-life and synthetic data sets, and show that our non-zero slack schemes incur significantly less communication overhead compared to the state of the art zero slack scheme (over a 60% savings).