Compression techniques for wireless sensor networks

In wireless sensor network applications, an event is observed by a group of spatially distributed sensors which collaborate to make decisions. Since there is limited bandwidth in wireless sensor networks, it is important to reduce data bits communicated among sensor nodes to meet the application performance requirements. It also saves node energy since less bits are communicated between nodes. An approach is to compress sensor data before transmissions to reduce energy as some loss is acceptable without affecting the results of applications. Data collected by sensors that are in close proximity exhibit spatial correlation, further, since the samples collected over time are from the same source(s) also show temporal correlation. We shall first review state-of-the-art results on compression and issues in deploying these techniques in wireless sensor networks. We then present a new data compression scheme, called ESPIHT, that exploits this spatio-temporal correlation present in sensor networks to reduce the amount of data bits transmitted in a collaborative signal processing application. The proposed ESPIHT seamlessly embeds a distributed source coding (DSC) scheme with a SPIHT based iterative set partitioning scheme to exploit both spatial and temporal correlation. Instead of being a generic compression scheme, ESPIHT is coupled with application on which it adapts to application fidelity requirement so that application level false alarm rate is kept low. We evaluated the proposed scheme using dataset from an acoustic sensor based ATR application on a network of iPAQs with Wireless LAN connection. Our results show that ESPIHT reduces data rate by a factor of 8 and maintains Signal-to-Noise Ratio (SNR) gain of 20dB or better. The coding/decoding processing is simple and takes on the order of 10 msec on iPAQs. It is superior to known schemes in terms of SNR gain as shown in the experimental study based on field data when the sampling rate is relatively high and the network is dense.

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