Distributed Network Configuration for Wavelet-Based Compression in Sensor Networks

Authors:
Paula Tarrío;Giuseppe Valenzise;Godwin Shen;Antonio Ortega
Affiliations:
Departamento de Señales, Sistemas y Radiocomunicaciones, Universidad Politécnica de Madrid, Madrid, Spain;Dipartimento di Elettronica e Informazione, Politecnico di Milano, Milan, Italy;Department of Electrical Engineering, University of Southern California, Los Angeles, USA;Department of Electrical Engineering, University of Southern California, Los Angeles, USA
Venue:
GSN '09 Proceedings of the 3rd International Conference on GeoSensor Networks
Year:
2009

Citing 10
Cited 0

Wireless Communications: Principles and Practice

Wireless Communications: Principles and Practice
The Impact of Data Aggregation in Wireless Sensor Networks

ICDCSW '02 Proceedings of the 22nd International Conference on Distributed Computing Systems
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
Universal distributed sensing via random projections

Proceedings of the 5th international conference on Information processing in sensor networks
Energy-efficient data representation and routing for wireless sensor networks based on a distributed wavelet compression algorithm

Proceedings of the 5th international conference on Information processing in sensor networks
On the interdependence of routing and data compression in multi-hop sensor networks

Wireless Networks
Joint Routing and 2D Transform Optimization for Irregular Sensor Network Grids Using Wavelet Lifting

IPSN '08 Proceedings of the 7th international conference on Information processing in sensor networks
Energy-efficient graph-based wavelets for distributed coding in Wireless Sensor Networks

ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
On the many-to-one transport capacity of a dense wireless sensor network and the compressibility of its data

IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Networked Slepian-Wolf: theory, algorithms, and scaling laws

IEEE Transactions on Information Theory

Quantified Score

Hi-index	0.00

Visualization

Abstract

En-route data compression is fundamental to reduce the power consumed for data gathering in sensor networks. Typical in-network compression schemes involve the distributed computation of some decorrelating transform on the data; the structure along which the transform is computed influences both coding performance and transmission cost of the computed coefficients, and has been widely explored in the literature. However, few works have studied this interaction in the practical case when the routing configuration of the network is also built in a distributed manner. In this paper we aim at expanding this understanding by specifically considering the impact of distributed routing tree initialization algorithms on coding and transmission costs, when a tree-based wavelet lifting transform is adopted. We propose a simple modification to the collection tree protocol (CTP) which can be tuned to account for a vast range of spatial correlations. In terms of costs and coding efficiency, our methods do not improve the performance of more sophisticated routing trees such as the shortest path tree, but they entail an easier manageability in case of node reconfigurations and update.