MegaSense: Cyber-Physical System for Real-time Urban Air Quality Monitoring

Rahim

IEEE Trans. Intell. Transport. Syst.

et al. 2022

The dependency on vehicles is increasing tremendously due to its excellent transport capacity, fast, effcient, fexible, pleasant journey, minimal physical effort, and substantial economic impact. As a result, the demand for smart and intelligent feature enhancement is growing and becoming a prime concern for maximum productivity based on the current perspective. In this case, the Internet of Everything (IoE) is an emerging concept that can play an essential role in the automotive industry by integrating the stakeholders, process, data, and things via networked connections. But the unavailability of intelligent features leads to negligence about proper maintenance of vehicle vulnerable parts, reckless driving and severe accident, lack of instructive driving, and improper decision, which incurred extra expenses for maintenance besides hindering national economic growth. For this, we proposed a conceptual framework for a central VHMS exploiting IoE-driven Multi-Layer Heterogeneous Networks (HetNet) and a machine learning technique to oversee individual vehicle health conditions, notify the respective ownerdriver real-timely and store the information for further necessary action. This article transparently portrayed an overview of central VHMS and proposed the taxonomy to achieve such an objective. Subsequently, we unveiled the framework for central VHMS, IoE-driven Multi-tire HetNet, with a secure and trustworthy data collection and analytics system. Finally, anticipating this proposition's outcome is immense in the automotive sector. It may motivate the researcher to develop a central intelligent and secure vehicular condition diagnostic system to move this sector towards Industry 4.0.

Section: Infrastructure Concept For Vhmsmentioning

confidence: 99%

A Secure and Intelligent Framework for Vehicle Health Monitoring Exploiting Big-Data Analytics

Rahim

IEEE Trans. Intell. Transport. Syst.

et al. 2022

“…To operationalize the MegaSense cyber-physical system and provide citizens a convenient and affordable method to measure their exposure to air pollution, the University of Helsinki designed a sensing unit based on a BMD-340 system module and mobile phone app called HOPE sensor unit (Rebeiro-Hargrave et al, 2020). The HOPE unit connects to Android and iOS smartphones over Bluetooth Low Energy, and the smartphones report their readings further to the collecting server and to the MegaSense system.…”

Section: Hope Sensor Unitmentioning

confidence: 99%

“…With community-based monitoring stakeholders "Do-it-Together" and use sensors and mobile phones to address local air pollution (Mahajan et al, 2021). The fourth is to allow citizens open access to an automated system -a cyber-physical system -providing a feedback loop between their sensor measurements, time activity patterns and exposure to air pollution (Rebeiro-Hargrave et al, 2020). These participatory sensing approaches help to influence local government city planning investments (Van Brussel and Huyse, 2019), sometimes through confrontation (Carton and Ache, 2017), and other times triggering behavioral change.…”

Section: Introductionmentioning

confidence: 99%

City Wide Participatory Sensing of Air Quality

Rebeiro‐Hargrave

Fung

Varjonen

et al. 2021

Front. Environ. Sci.

Self Cite

Air pollution is a contributor to approximately one in every nine deaths annually. Air quality monitoring is being carried out extensively in urban environments. Currently, however, city air quality stations are expensive to maintain resulting in sparse coverage and data is not readily available to citizens. This can be resolved by city-wide participatory sensing of air quality fluctuations using low-cost sensors. We introduce new concepts for participatory sensing: a voluntary community-based monitoring data forum for stakeholders to manage air pollution interventions; an automated system (cyber-physical system) for monitoring outdoor air quality and indoor air quality; programmable platform for calibration and generating virtual sensors using data from low-cost sensors and city monitoring stations. To test our concepts, we developed a low-cost sensor to measure particulate matter (PM2.5), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3) with GPS. We validated our approach in Helsinki, Finland, with participants carrying the sensor for 3 months during six data campaigns between 2019 and 2021. We demonstrate good correspondence between the calibrated low-cost sensor data and city’s monitoring station measurements. Data analysis of their personal exposure was made available to the participants and stored as historical data for later use. Combining the location of low cost sensor data with participants public profile, we generate proxy concentrations for black carbon and lung deposition of particles between districts, by age groups and by the weekday.

“…Megasense system [6] consists of three main parts, the Sensing System, Edge Layer and Cloud Layer as shown in Figure 2. The Sensing system includes the sensing sources deployed in urban areas as well as the radio communication interfaces.…”

Section: Deployment Of Megasense Over a Network Slice A Megasense Systemmentioning

confidence: 99%

“…Megasense is a project that is developed with the sole aim of providing real-time massive scale air quality sensing in urban areas by integrating a large number of air quality sensors at the scale of for example tens of thousands or even millions of air quality sensors [5]. Megasense enables air pollution hotspot detection outdoors; air quality sensing indoors; and offers creation of real-time air pollution map in urban areas [6]. To obtain real-time air quality data from local variations with high resolution at the resolution of few meters, Megasense uses any existing air quality infrastructure in the city such as city air quality stations, crowd-sensing approaches, and citizens personal air quality sensors willing to share their sensed data.…”

Section: Introductionmentioning

confidence: 99%

mMTC Deployment over Sliceable Infrastructure: The Megasense Scenario

et al. 2021

Self Cite

Massive Machine Type Communication (mMTC) has long been identified as a major vertical sector and enabler of the industry 4.0 technological evolution that will seamlessly ease the dynamics of machine-to-machine communications while leveraging the 5G technology. To advance this concept, we have developed and tested an mMTC network slice called Megasense. Megasense is a complete framework that consists of multiple software modules, which is used for collecting and analyzing air pollution data that emanates from a massive amount of air pollution sensors. Taking advantage of the 5G networks, the Megasense will significantly benefit from an underlying communication network that is traditionally elastic and can accommodate the on-demand changes in requirements of such a use case. As a result, deploying the sensor nodes over a sliceable 5G system is deemed the most appropriate in satisfying the resource requirements of such a use case scenario. In this light, in order to verify how 5G-ready our Megasense solution is, we deployed it over a network slice that is totally composed of virtual resources. We have also evaluated the impact of the network slicing platform on the Megasense in terms of bandwidth and resource utilization. We further tested the performances of the Megasense system and come up with different deployment recommendations based on which the Megasense system would function optimally.