High Density Real-Time Air Quality Derived Services from IoT Networks

Abstract

In recent years, there is an increasing attention on air quality derived services for the final users. A dense grid of measures is needed to implement services such as conditional routing, alerting on data values for personal usage, data heatmaps for Dashboards in control room for the operators, and for web and mobile applications for the city users. Therefore, the challenge consists of providing high density data and services starting from scattered data and regardless of the number of sensors and their position to a large number of users. To this aim, this paper is focused on providing an integrated solution addressing at the same time multiple aspects: To create and optimize algorithms for data interpolation (creating regular data from scattered), making it possible to cope with the scalability and providing support for on demand services to provide air quality data in any point of the city with dense data. To this end, the accuracy of different interpolation algorithms has been evaluated comparing the results with respect to real values. In addition, the trends of heatmaps interpolation errors have been exploited to detected devices’ dysfunctions. Such anomalies may often be useful to request a maintenance action. The solution proposed has been integrated as a Micro Services providing data analytics in a data flow real time process based on Node.JS Node-RED, called in the paper IoT Applications. The specific case presented in this paper refers to the data and the solution of Snap4City for Helsinki. Snap4City, which has been developed as a part of Select4Cities PCP of the European Commission, and it is presently used in a number of cities and areas in Europe.

A Deep Two-State Gated Recurrent Unit for Particulate Matter (PM2.5) Concentration Forecasting

“…, x i,T is an sequential observation of data at a one station S i , and x i,t = (t, lon, lat, v) represents one assessment from each station S i at a certain time step t. We can notice from the sample data that the range of raw information fluctuates greatly. Suppose, the [1,12] is the range features of the month, whereas the limit of PM 2.5 values is (0, 210]. As a result, we measure the informative features so that all values fall between 0 and 1.…”

“…A black box strategy, including the artificial intelligent algorithm, is a reasonable concept and an auspicious way to predict the spatiotemporal assessment parameter in such scenarios. Furthermore, Badli et al [12] presented an effective parallel machine learning model to address this challenge in order to regulate the appropriate spatiotemporal anisotropy parameters.…”

Advanced Technologies and Their Use in Smart City Management

“…The sensors collect data from the fields of transport, healthcare, social networks and media, limited resources (especially air) and the government. Data are aggregated through IoT applications, then analyzed and used in city management processes [65].…”

“…The city of Helsinki publicly shares the collected data from the sensory network on city dashboards. In cooperation with Nokia, an MEC (mobile edge computing) network with object tracking, camera surveillance and video analysis applications has been used since 2016 [65,66].…”

“…The collected data are evaluated with the Helsinki Region Environmental Services Authority. Statistics are further processed via the Snap4City architecture and shared on city dashboards and mobile applications [65]. To build and manage Smart City concepts, it is essential to create an environment based on the trust of citizens.…”