Vector quantization and signal compression
Vector quantization and signal compression
Introduction to Algorithms
Simultaneous optimization for concave costs: single sink aggregation or single source buy-at-bulk
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
Coping with irregular spatio-temporal sampling in sensor networks
ACM SIGCOMM Computer Communication Review
The impact of spatial correlation on routing with compression in wireless sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
Gathering correlated data in sensor networks
Proceedings of the 2004 joint workshop on Foundations of mobile computing
An architecture for distributed wavelet analysis and processing in sensor networks
Proceedings of the 5th international conference on Information processing in sensor networks
Proceedings of the 5th international conference on Information processing in sensor networks
Graphs, Networks and Algorithms (Algorithms and Computation in Mathematics)
Graphs, Networks and Algorithms (Algorithms and Computation in Mathematics)
Distributed wavelet compression algorithms for wireless sensor networks
Distributed wavelet compression algorithms for wireless sensor networks
The impact of spatial correlation on routing with compression in wireless sensor networks
ACM Transactions on Sensor Networks (TOSN)
Distributed Network Configuration for Wavelet-Based Compression in Sensor Networks
GSN '09 Proceedings of the 3rd International Conference on GeoSensor Networks
Spatially-Localized Compressed Sensing and Routing in Multi-hop Sensor Networks
GSN '09 Proceedings of the 3rd International Conference on GeoSensor Networks
Transform-based distributed data gathering
IEEE Transactions on Signal Processing
Practical data compression in wireless sensor networks: A survey
Journal of Network and Computer Applications
Compression in wireless sensor networks: A survey and comparative evaluation
ACM Transactions on Sensor Networks (TOSN)
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We address the joint optimization of routing and compression for wireless sensor networks using a lifting-based 2D transform that can be computed along arbitrary routing trees. The proposed 2D transform allows for unidirectional computation, thereby eliminating costly backward transmissions often required by existing 2D transforms. We also propose a framework for optimizing the transform by selecting among a different set of coding schemes (i.e., different levels in the wavelet decomposition). Since our transform can operate on arbitrary routing trees, we focus on the problem of jointly optimizing routing trees based on inter-node data correlation and inter-node distance. The two extreme solutions would be i) to route data along paths that maximize inter-node data correlation (at the risk of increasing transport costs), corresponding to a minimum spanning tree (MST), or ii) to follow shortest path tree (SPT) routing (where inter-node data correlation may not be as high). We propose an optimization technique that exhaustively searches for the optimal tree over a set of combinations of MST and SPT. We also propose a heuristic approximation algorithm that is amenable for use on larger networks and with which we observe total cost reductions close to 10% for some of the data.