New technologies for smart cities – high‐resolution air pollution maps based on intelligent sensors

Abstract

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”).

A full‐stack model proposal to remotely help the development of IoT sensor devices

“…Some of the problems that are addressed in this study were also partially discussed in [5,8,17,19] and [1]. In [1], the authors presented a contribution to the development, in real-time, of wireless sensor networks, capable to create dense air pollution maps for compact urban areas.…”

Leveraging big data in smart cities: A systematic review

“…Besides, communication facilities can allow health applications on joggers' devices to be connected to infrastructures. Thus, people can easily pick the healthiest path and be connected to their most preferred training applications 28,29 Noise monitoring : Noise can be considered a form of acoustic pollution for air.…”

A Novel, Low Computational Complexity, Parallel Swarm Algorithm for Application in Low-Energy Devices

“…A similar approach may also be applied to artificial intelligence (AI) algorithms, which are frequently used in the process of analysis and inference on the basis of data provided by a group of sensors in the WSN [8,9]. In our previous work, we proposed solutions that enable the development of low energy consumption miniature artificial neural networks in specialized integrated circuits realized in CMOS technology [6,10,11].…”

“…For this reason, a single LUT block may be required for the overall swarm. In this case, however, in the memory of each particle, it is necessary to store a specific value of the r base factor, used in Equations ( 10) and (11) . This factor is used to differentiate the values of resultant r variables for particular particles.…”