Title: Application of Sensor Network for Supporting Fire Fighters
Authors: Patrick Y.H. Cheung, Nick Maxemchuk (Poster II, 5)

ABSTRACT

The objective of our work is to support fire-fighters in the rescue mission by providing real-time information, like fire size, spreading speed, chemical concentration, etc., collected from the fire scene. In order to save transmission power and prolong network lifetime, we propose a data compression scheme that removes redundancy in sensor data based on sampling theory. Advantages of our scheme include: (i) compression ratio close to the theoretic optimum value, (ii) "zoom-in" support, i.e. users can get an overall signal profile and then increase the resolution in the regions of interest, (iii) noise reduction through local interpolation, and (iv) freshness of data as a result of only one level of intermediate compression. Simulation results based on the temperature distribution of a simple fire scene generated by the NIST Fire Dynamics Simulator show that with a sensor density of 10/m2 and a SNR of 10dB, using a simple Gaussian low-pass filter for local compression, a percentage error of 1% can be achieved by sending only 11% of the raw data traffic to the destination.