In this paper we discuss the first results of the PLEASED project [1] which aims at employing plants as biosensors of a new generation of pervasive and organic wireless sensor networks.

Asymptotically optimal zero-delay vector quantization in the presence of channel noise is studied using random coding techniques. First, an upper bound is derived for the average rth-power distortion of channel optimized k-dimensional vector quantization at transmission rate R on a binary symmetric channel with bit error probability /spl epsiv/. The upper bound asymptotically equals 2/sup -rRg(/spl epsiv/,k,r/). where k/(k +r) [1 - log/sub 2/(l +2/spl radic/(/spl epsiv/(1-/spl epsiv/))] /spl les/g(/spl epsiv/,k,r)/spl les/1) for all /spl epsiv//spl ges/0, lim/sub /spl epsiv//spl rarr/0/g(/spl epsiv/,k,r)=1, and lim/sub k/spl rarr//spl infin//g(/spl epsiv/,k,r)=1. Numerical computations of g(/spl epsiv/,k,r) are also given. This result is analogous to Zador's (1982) asymptotic distortion rate of 2/sup -rR/ for quantization on noiseless channels. Next, using a random coding argument on nonredundant index assignments, a useful upper bound is derived in terms of point density functions, on the minimum mean squared error of high resolution, regular, vector quantizers in the presence of channel noise. The formula provides an accurate approximation to the distortion of a noisy channel quantizer whose codebook is arbitrarily ordered. Finally, it is shown that the minimum mean squared distortion of a regular, noisy channel VQ with a randomized nonredundant index assignment, is, in probability, asymptotically bounded away from zero.>