Summary This paper presents a contribution in the development of a wireless sensor network, which can be used for building, in real time, dense air pollution maps, for compact urban areas. Such system may be useful for cyclists and pedestrians moving through the city. Based on such data, they can select the route in such a way, as to avoid the most polluted areas. An important step here may be development of miniaturized and cheap intelligent sensors, capable not only of data recording and transmitting, but also of some on‐site data processing and prediction. Such sensors require a development of small and power efficient circuit, including data processing unit integrated with an artificial neural network (ANN) in a single chip. We present a prototype chip that contains main components of such sensors, which include a programmable 10‐bit analog‐to‐digital converter, a programmable clock generator, and selected blocks of the ANN. The chip is a reconfigurable device, with many testing abilities. For this reason, one of the main challenges was a fast and efficient programming and testing tool. Such tool has been developed by us and is described in this work in detail along with selected measurement results. The presented work is an extended version of our conference paper (“Novel solutions for smart cities—creating air pollution maps based on intelligent sensors”).

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This paper presents experimental results demonstrating a positive effect of using conscience mechanism in on chip learning Kohonen networks. Application of this mechanism allows to eliminate so called "dead neurons", which do not take part in the competition during the learning phase, thus increasing the quantization error of the network. Both Matlab simulations and measurement results are shown.

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