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Smart city article details
| Title | In-Cabin And Outdoor Environmental Monitoring In Vehicular Scenarios With Distributed Computing |
|---|---|
| ID_Doc | 31030 |
| Authors | Ramos-Sorroche E.; Rubio-Aparicio J.; Santa J.; Guardiola C.; Egea-Lopez E. |
| Year | 2024 |
| Published | Internet of Things (Netherlands), 25 |
| DOI | http://dx.doi.org/10.1016/j.iot.2023.101009 |
| Abstract | Accurate environmental monitoring is becoming the basis for assuring sustainable development in administrations at different levels, including cities and industry as key actors. However, current techniques rely on static stations that may not be representative of larger areas, for the case of outdoor scenarios, or even not considering indoor spaces where people can remain for long periods. This is the case of vehicles. The COVID-19 pandemic has remarked the importance of measuring air quality indoors, for instance. With the aim of solving this two-fold issue, this work proposes an in-cabin and outdoor air pollution monitoring system to assure healthy conditions when travelling, driving and operating vehicles, and to analyse the evolution of environmental parameters in cities. This effort is carried out exploiting distributed computing with micro-services, betting for an on-board hardware solution provided with sensors for measuring particulate matter, CO, CO2, NO2, O3, temperature and humidity. While basic data pre-processing is carried out in this acquisition unit, edge processing is performed on a single board computer aboard and intermediary communication nodes in the network path from the vehicle to the cloud. Vehicle connectivity is provided by 4G cellular and Low-Power Wide-Area (LPWAN) networks. Global environmental perception is acquired by cloud-based software powered by machine learning and time series analysis. The whole solution has been validated and tested in the city of Cartagena (Spain), with good performance in terms of data collection, communication links and service offered. © 2023 The Author(s) |
| Author Keywords | Crowdsensing; Edge computing; Environmental monitoring; Intelligent transportation systems; IoT; Smart cities |
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