Hardware Efficient Solutions for Wireless Air Pollution Sensors Dedicated to Dense Urban Areas

Abstract

This paper proposes novel solutions for the application of air pollution monitoring systems in so called 'smart cities'. A possibility of the implementation of a relatively dense network of wireless air pollution sensors that can collect and process data in real time was the motive behind our research and investigations. We discuss the concept of the wireless sensor network, taking into account the structure of the urban development in cities and we present a novel signal processing algorithm that may be used to control the communication scheme between particular sensors and an external network. We placed a special emphasis on the computational complexity to facilitate the implementation directly at the transistor level of particular sensors. The algorithm was verified using real data obtained from air pollution sensors installed in Krakow, Poland. To ensure sufficient robustness of the variability of input data, we artificially added high amplitude noise to the real data we obtained. This paper demonstrates the performance of the algorithm. This algorithm allows for the reduction of the noise amplitude by 23 dB and enables a reduction of the number of wireless communication sessions with a base station (BS) by 70%-80%. We also present selected measurement results of a prototype current-mode digital-to-analogue converter to be used in the sensors, for signal resolutions up to 7 bits.

The Use of Public Data from Low-Cost Sensors for the Geospatial Analysis of Air Pollution from Solid Fuel Heating during the COVID-19 Pandemic Spring Period in Krakow, Poland

“…The receivers were selected to cover both the city of Krakow and its surroundings. Airly sensors are one of the most popular in the Krakow area and can provide a high density of spatial data [ 33 ]. As a result of their convenience and relatively low price, they are also gaining popularity throughout the country [ 23 , 24 ] and around the world [ 34 ].…”

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

“…It can be realized by placing selected data processing tasks directly at the sensor level and by triggering communication with the base station only when needed. We proposed one solution for such a problem in [7]. 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 the next step of our investigations, we considered a possibility of a further modification of both variants of the algorithm described by Equations ( 6) and (7). The goal was to simplify their hardware implementation.…”