An Internet of Things System for Sensing, Analysis & Forecasting Urban Air Quality
16 Mar 2018-pp 1-6
TL;DR: In this paper, an environment sensing, location aware, Internet of Things (IoT) system was developed to monitor, collect and analyze the presence of different environmental parameters in real time.
Abstract: Constant monitoring of air quality is required in a smart city to improve human health and quality of life. Major cities of the world measure and analyze air quality and pollutant concentration with the help of few static air quality monitoring stations. Roads are arteries of a city and used by majority of the population for commuting and transportation. A low cost air quality sensing system installed in a vehicle that commutes through different routes of the city gives a finegrained real time information about the state of pollutants and air quality in different parts of the city. In this work, we have developed an environment sensing, location aware, Internet of Things system to monitor, collect and analyze the presence of different environmental parameters in real time. Pollution route map of the routes traversed by the vehicle with sensor-setup has been created which can be accessed by mobile users in other vehicles. As air pollution is highly location dependent, there is a need to predict air quality at places for which air quality information is not known. Multiple Linear Regression has been used to used to predict AQI levels from historic data for such locations.
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TL;DR: In this paper , the authors present a comprehensive review of the main contributions in the field during the period 2011-2021, and they have searched the main scientific publications databases and, after a careful selection, they have considered a total of 155 papers.
Abstract: Air pollution is a risk factor for many diseases that can lead to death. Therefore, it is important to develop forecasting mechanisms that can be used by the authorities, so that they can anticipate measures when high concentrations of certain pollutants are expected in the near future. Machine Learning models, in particular, Deep Learning models, have been widely used to forecast air quality. In this paper we present a comprehensive review of the main contributions in the field during the period 2011-2021. We have searched the main scientific publications databases and, after a careful selection, we have considered a total of 155 papers. The papers are classified in terms of geographical distribution, predicted values, predictor variables, evaluation metrics and Machine Learning model.
12 citations
TL;DR: A systematic mapping study defined by a five-step methodology to identify and analyze the research status in terms of IoT-based air pollution monitoring systems for smart cities and highlight some challenges for air quality monitoring systems implementation into the smart city context.
Abstract: The increased level of air pollution in big cities has become a major concern for several organizations and authorities because of the risk it represents to human health In this context, the technology has become a very useful tool in the contamination monitoring and the possible mitigation of its impact Particularly, there are different proposals using the internet of things (IoT) paradigm that use interconnected sensors in order to measure different pollutants In this paper, we develop a systematic mapping study defined by a five-step methodology to identify and analyze the research status in terms of IoT-based air pollution monitoring systems for smart cities The study includes 55 proposals, some of which have been implemented in a real environment We analyze and compare these proposals in terms of different parameters defined in the mapping and highlight some challenges for air quality monitoring systems implementation into the smart city context
10 citations
TL;DR: In this article, an Internet of Things based system has been developed to monitor, analyze and forecast outdoor air quality, which is integrated with a vehicle, and collects data while the vehicle moves on the road.
Abstract: Rapid urbanization, vehicular emissions, rise in industrial activities, burning of crop residues and garbage in nearby areas, thermal power plants, emissions from diesel generators, dust from construction sites and household fuel use have been the cause of severe deterioration of urban air quality, resulting in a large number of deaths every year. In this work, an Internet of Things based system has been developed to monitor, analyze and forecast outdoor air quality. Air quality data is collected using our sensing system which is integrated with a vehicle, and collects data while the vehicle moves on the road. The sensed data is transferred and stored in cloud using an Android application. Stored data is used to forecast air quality with the help of statistical and stochastic forecasting models-quantile regression and ARMA/ARIMA. The forecast performance of these prediction models is measured using mean absolute deviation, mean percentage error, mean absolute percentage error, mean square error and root mean square error to find their efficacy.
7 citations
TL;DR: In this paper , a scalable deep learning framework for efficiently capturing fine-grained highway data and forecasting future concentration levels is proposed and validated using four different UK regions -Newport, Lewisham, Southwark, and Chepstow -to develop a REVIS system and validate the proposed framework.
7 citations
TL;DR: In this article , a Systematic Literature Review (SLR) is introduced in order to summarize the recent studies in the field under specific rules and constraints, and the analysis showed that many IoT-based prediction models have been applied in the previous years to 10 different environmental issues.
Abstract: Undoubtedly, during the last few years, climate change has alerted the research community of the natural environment sector. Furthermore, the advent of the Internet of Things (IoT) paradigm has enhanced the research activity in the environmental field by offering low-cost sensors. Moreover, artificial intelligence and more specifically, statistical and machine learning methodologies have proved their predictive power in many disciplines and various real-world problems. As a result of the aforementioned, many scientists in the environmental research field have performed prediction models exploiting the strength of IoT data. Hence, insightful information could be extracted from the review of these research works and for this reason, a Systematic Literature Review (SLR) is introduced in the present manuscript in order to summarize the recent studies in the field under specific rules and constraints. From the SLR, 54 primary studies have been extracted during 2017–2021. The analysis showed that many IoT-based prediction models have been applied in the previous years to 10 different environmental issues, with promising results in the majority of the primary studies.
5 citations
References
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TL;DR: This paper will present and discuss the technical solutions and best-practice guidelines adopted in the Padova Smart City project, a proof-of-concept deployment of an IoT island in the city of Padova, Italy, performed in collaboration with the city municipality.
Abstract: The Internet of Things (IoT) shall be able to incorporate transparently and seamlessly a large number of different and heterogeneous end systems, while providing open access to selected subsets of data for the development of a plethora of digital services. Building a general architecture for the IoT is hence a very complex task, mainly because of the extremely large variety of devices, link layer technologies, and services that may be involved in such a system. In this paper, we focus specifically to an urban IoT system that, while still being quite a broad category, are characterized by their specific application domain. Urban IoTs, in fact, are designed to support the Smart City vision, which aims at exploiting the most advanced communication technologies to support added-value services for the administration of the city and for the citizens. This paper hence provides a comprehensive survey of the enabling technologies, protocols, and architecture for an urban IoT. Furthermore, the paper will present and discuss the technical solutions and best-practice guidelines adopted in the Padova Smart City project, a proof-of-concept deployment of an IoT island in the city of Padova, Italy, performed in collaboration with the city municipality.
4,335 citations
TL;DR: A framework for the realization of smart cities through the Internet of Things (IoT), which encompasses the complete urban information system, from the sensory level and networking support structure through to data management and Cloud-based integration of respective systems and services, and forms a transformational part of the existing cyber-physical system.
Abstract: Increasing population density in urban centers demands adequate provision of services and infrastructure to meet the needs of city inhabitants, encompassing residents, workers, and visitors. The utilization of information and communications technologies to achieve this objective presents an opportunity for the development of smart cities, where city management and citizens are given access to a wealth of real-time information about the urban environment upon which to base decisions, actions, and future planning. This paper presents a framework for the realization of smart cities through the Internet of Things (IoT). The framework encompasses the complete urban information system, from the sensory level and networking support structure through to data management and Cloud-based integration of respective systems and services, and forms a transformational part of the existing cyber-physical system. This IoT vision for a smart city is applied to a noise mapping case study to illustrate a new method for existing operations that can be adapted for the enhancement and delivery of important city services.
1,178 citations
"An Internet of Things System for Se..." refers methods in this paper
...In [11], the authors have used the smart city test-bed and IoT setup to create a noise map of Melbourne city....
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TL;DR: The challenges and potential challenges to provide vehicle-to-x connectivity are discussed and the state-of-the-art wireless solutions for vehicle-To-sensor, vehicle- to-vehicle, motorway infrastructure connectivities are reviewed.
Abstract: Providing various wireless connectivities for vehicles enables the communication between vehicles and their internal and external environments. Such a connected vehicle solution is expected to be the next frontier for automotive revolution and the key to the evolution to next generation intelligent transportation systems (ITSs). Moreover, connected vehicles are also the building blocks of emerging Internet of Vehicles (IoV). Extensive research activities and numerous industrial initiatives have paved the way for the coming era of connected vehicles. In this paper, we focus on wireless technologies and potential challenges to provide vehicle-to-x connectivity. In particular, we discuss the challenges and review the state-of-the-art wireless solutions for vehicle-to-sensor, vehicle-to-vehicle, vehicle-to-Internet, and vehicle-to-road infrastructure connectivities. We also identify future research issues for building connected vehicles.
936 citations
"An Internet of Things System for Se..." refers background in this paper
...The authors in [16] explores the wireless technologies and potential challenges to provide vehicle to x connectivity....
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TL;DR: The IoT experimentation facility described in this paper is conceived to provide a suitable platform for large scale experimentation and evaluation of IoT concepts under real-life conditions to influence the definition and specification of Future Internet architecture design.
622 citations
TL;DR: These studies indicate that air pollution from these sources is a major preventable cause of increased incidence and exacerbation of respiratory disease and Physicians can help to reduce the risk of adverse respiratory effects of exposure to biomass and traffic air pollutants by promoting awareness and supporting individual and community-level interventions.
Abstract: Mounting evidence suggests that air pollution contributes to the large global burden of respiratory and allergic diseases, including asthma, chronic obstructive pulmonary disease, pneumonia, and possibly tuberculosis. Although associations between air pollution and respiratory disease are complex, recent epidemiologic studies have led to an increased recognition of the emerging importance of traffic-related air pollution in both developed and less-developed countries, as well as the continued importance of emissions from domestic fires burning biomass fuels, primarily in the less-developed world. Emissions from these sources lead to personal exposures to complex mixtures of air pollutants that change rapidly in space and time because of varying emission rates, distances from source, ventilation rates, and other factors. Although the high degree of variability in personal exposure to pollutants from these sources remains a challenge, newer methods for measuring and modeling these exposures are beginning to unravel complex associations with asthma and other respiratory tract diseases. These studies indicate that air pollution from these sources is a major preventable cause of increased incidence and exacerbation of respiratory disease. Physicians can help to reduce the risk of adverse respiratory effects of exposure to biomass and traffic air pollutants by promoting awareness and supporting individual and community-level interventions.
402 citations
"An Internet of Things System for Se..." refers background in this paper
...Increase in concentration of these gases may cause respiratory and allergic diseases, such as asthma, chronic obstructive pulmonary disease, pneumonia, tuberculosis and overall deterioration of human health [2][3]....
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