Ten lectures on wavelets
Next century challenges: scalable coordination in sensor networks
MobiCom '99 Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking
Geography-informed energy conservation for Ad Hoc routing
Proceedings of the 7th annual international conference on Mobile computing and networking
Data Gathering Algorithms in Sensor Networks Using Energy Metrics
IEEE Transactions on Parallel and Distributed Systems
Dimensions: why do we need a new data handling architecture for sensor networks?
ACM SIGCOMM Computer Communication Review
Energy-Efficient Communication Protocol for Wireless Microsensor Networks
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 8 - Volume 8
An evaluation of multi-resolution storage for sensor networks
Proceedings of the 1st international conference on Embedded networked sensor systems
A wireless sensor network For structural monitoring
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
An architecture for distributed wavelet analysis and processing in sensor networks
Proceedings of the 5th international conference on Information processing in sensor networks
CCS-MAC: Exploiting the overheard data for compression in wireless sensor networks
Computer Communications
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We propose an optimal-level distributed transform for wavelet-based spatiotemporal data compression in wireless sensor networks. Although distributed wavelet processing can efficiently decrease the amount of sensory data, it introduces additional communication overhead as the sensory data needs to be exchanged in order to calculate the wavelet coefficients. This tradeoff is explored in this paper with the optimal transforming level of wavelet transform. By employing a ring topology, our scheme is capable of supporting a broad scope of wavelets rather than specific ones, and the "border effect" generally encountered by wavelet-based schemes is also eliminated naturally. Furthermore, the scheme can simultaneously explore the spatial and temporal correlations among the sensory data. For data compression in wireless sensor networks, in addition to minimizing energy and consumption, it is also important to consider the delay and the quality of reconstructed sensory data, which is measured by the ratio of signal to noise (PSNR). We capture this with energy × delay/PSN R metric and using it to evaluate the performance of the proposed scheme. Theoretically and experimentally, we conclude that the proposed algorithm can effectively explore the spatial and temporal correlation in the sensory data and provide significant reduction in energy and delay cost while still preserving high PSNR compared to other schemes.