Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Distributed construction of connected dominating set in wireless ad hoc networks
Mobile Networks and Applications - Discrete algorithms and methods for mobile computing and communications
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
The threshold join algorithm for top-k queries in distributed sensor networks
DMSN '05 Proceedings of the 2nd international workshop on Data management for sensor networks
A Sampling-Based Approach to Optimizing Top-k Queries in Sensor Networks
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Constraint chaining: on energy-efficient continuous monitoring in sensor networks
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Energy-efficient monitoring of extreme values in sensor networks
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Top-k Monitoring in Wireless Sensor Networks
IEEE Transactions on Knowledge and Data Engineering
Adaptive clustering for mobile wireless networks
IEEE Journal on Selected Areas in Communications
The core-assisted mesh protocol
IEEE Journal on Selected Areas in Communications
Power efficiency through tuple ranking in wireless sensor network monitoring
Distributed and Parallel Databases
Exploiting in-network processing for big data management
Proceedings of the 2013 Sigmod/PODS Ph.D. symposium on PhD symposium
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Finding an aggregation of observed values, in particular the maximum value, is an important type of query in wireless sensor networks. Previous proposals to find the maximum value, the so-called MAX query, relied on a given underlying logical tree topology for data aggregation/forwarding, but did not pay due attention to the role of such topology. Focusing on the MAX queries we first argue that the underlying tree topology plays a very important role in the query processing cost. We then propose the use of a particular tree topology, based on Dominating Sets that is well suited to explore the network's physical topology for processing MAX queries efficiently. Experimental results obtained using real and synthetic datasets confirm that by simply replacing the tree topologies used in previous proposals with the Dominating Set-based Tree (DST) one can reduce the transmission cost of MAX queries by up to 70% and overall energy consumption by up to 53%.