01674nas a2200205   4500008004100000022001300041245010200054210006900156260001400225300001400239490000700253520106500260653000801325653001901333653000701352653001901359100001801378700002101396856005101417       2007                        eng d  a0141933100aVehicle classification in Sensor Networks using time-domain signal processing and Neural Networks0 aVehicle classification in Sensor Networks using timedomain signa  cSeptember  a381–3920 v313 aVehicle classification is a demanding application of Wireless Sensor Networks. In many cases, sensor nodes detect and classify vehicles from their acoustic and/or seismic signature using spectral or wavelet based feature extraction methods. Such methods, while providing good results are quite demanding in computational power and energy and are difficult to implement on low-cost sensor nodes with limited resources. In this work, we investigate the use of a time-domain encoding and feature extraction method, to produce simple, fixed-size matrices from complex acoustic and seismic signatures of vehicles for classification purposes. Classification is accomplished using an Artificial Neural Network and a basic, L1 distance, archetype classifier. Hardware implementation issues on a prototype sensor node, based on an 8-bit microcontroller, are also discussed. For evaluation purposes we use real data from DARPA's SensIt project, which contains various acoustic and seismic signatures from two different vehicle types, a tracked vehicle and a heavy truck.10aasr10aclassification10ait10aneuralnetworks1 aMazarakis, G.1 aAvaritsiotis, J.  uhttp://dx.doi.org/10.1016/j.micpro.2007.02.005