Matrix analysis
Wavelets and subband coding
The lifting scheme: a construction of second generation wavelets
SIAM Journal on Mathematical Analysis
FOCS '02 Proceedings of the 43rd Symposium on Foundations of Computer Science
Dimensions: why do we need a new data handling architecture for sensor networks?
ACM SIGCOMM Computer Communication Review
An evaluation of multi-resolution storage for sensor networks
Proceedings of the 1st international conference on Embedded networked sensor systems
Locally constructed algorithms for distributed computations in ad-hoc networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
Optimization of in-network data reduction
DMSN '04 Proceeedings of the 1st international workshop on Data management for sensor networks: in conjunction with VLDB 2004
A scheme for robust distributed sensor fusion based on average consensus
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Beyond average: toward sophisticated sensing with queries
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Computing and communicating functions over sensor networks
IEEE Journal on Selected Areas in Communications
Distributed sparse random projections for refinable approximation
Proceedings of the 6th international conference on Information processing in sensor networks
Hierarchical spatial gossip for multi-resolution representations in sensor networks
Proceedings of the 6th international conference on Information processing in sensor networks
Hierarchical Spatial Gossip for Multiresolution Representations in Sensor Networks
ACM Transactions on Sensor Networks (TOSN)
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We propose random distributed multiresolution representations of sensor network data, so that the most significant encoding coefficients are easily accessible by querying a few sensors, anywhere in the network. Less significant encoding coefficients are available by querying a larger number of sensors, local to the region of interest. Significance can be defined in a multiresolution way, without any prior knowledge of the source data, as global summaries versus local details. Alternatively, significance can be defined in a data-adaptive way, as large differences between neighboring data values. We propose a distributed encoding algorithm that is robust to arbitrary wireless communication connectivity graphs, where links can fail or change with time. This randomized algorithm allows distributed computation that does not require strict global coordination or awareness of network connectivity at individual sensors. Because computations involve sensors in local neighborhoods of the communication graph, they are communication-efficient. Our framework uses local interaction among sensors to enable flexible information retrieval at the global level.