Stacking Regression Algorithms to Predict PM2.5 in the Smart City Using Internet of Things

2020 ◽  
Vol 13 ◽  
Author(s):  
Alisha Banga ◽  
Ravinder Ahuja ◽  
Subhash Chander Sharma
Keyword(s):  
Air Quality ◽  
Internet Of Things ◽  
Urban Areas ◽  
Smart Cities ◽  
Absolute Error ◽  
Dew Point ◽  
Beijing City ◽  
Data Set ◽  
Regression Algorithms ◽  
Ensemble Algorithms

Background and Objective: With the increase in populations in urban areas, there is an increase in pollution also. Air pollution is one of the challenging environmental issues in smart cities. Real-time monitoring of air quality can help the administration to take appropriate decisions on time. Development in the Internet of Things based sensors has changed the way to monitor air quality. Methods: In this paper, we have applied two-stage regressions. In the first stage, ten regression algorithms (Decision Tree, Random Forest, Elastic Net, Adaboost, Extra Tree, Linear Regression, Lasso, XGBoost, Light GBM, AdaBoost, and Multi-Layer Perceptron) is applied and in second stage best four algorithms are picked and stacking ensemble algorithms is applied using python to predict the PM2.5 pollutants in air. Data set of five Chinese cities (Beijing, Chengdu, Guangzhou, Shanghai, and Shenyang) has taken into consideration and compared based on MAE (Mean Absolute Error), RMSE (Root Mean Square Error), and R2 parameters. Results and Conclusion: We observed that out of ten regression algorithms applied extra tree algorithm is giving the highest performance on all the five datasets, and stacking further improves the performance. Feature importance for Sheyang, and Beijing city is computed using three regression algorithms, and we found the four most important features are Humidity, wind speed, wind direction, and dew point.

scholarly journals Smart cities IOT & Digital network connectivity devices: Optimization on business in Africa

2021 ◽  
pp. 064-071
Author(s):  
Makeri Yakubu Ajiji ◽  
Xi’an Jiaotong Victor Chang ◽  
Targio Hashem Ibrahim Abaker ◽  
Uzorka Afam ◽  
T Cirella Giuseppe
Keyword(s):  
Internet Of Things ◽  
Urban Communities ◽  
Information And Communication Technologies ◽  
Smart City ◽  
Urban Areas ◽  
Smart Cities ◽  
The Internet ◽  
City Development ◽  
Short Article ◽  
The Internet Of Things

Today the world is becoming connected. The number of devices that are connected are increasing day by day. Many studies reveal that about 50 billion devices would be connected by 2020 indicating that Internet of things have a very big role to play in the future to come Considering the perplexing engineering of Smart City conditions, it ought not to be failed to remember that their establishment lies in correspondence advancements that permit availability and information move between the components in Smart City conditions. Remote interchanges with their capacities speak to Smart City empowering advancements that give the open door for their fast and effective execution and extension as well. The gigantic weight towards the proficient city the board has triggered various Smart City activities by both government and private area businesses to put resources into Information and Communication Technologies to discover feasible answers for the assorted chances and difficulties (e.g., waste the executives). A few specialists have endeavored to characterize a lot of shrewd urban areas and afterward recognize openings and difficulties in building brilliant urban communities. This short article likewise expresses the progressing movement of the Internet of Things and its relationship to keen urban communities. Advancement in ICT and data sharing innovation are the drivers of keen city degree and scale. This quick development is changing brilliant city development with the beginning of the Internet of Things (IoT). This transformation additionally speaks to difficulties in building (Kehua, Li, and Fu ,Su et al.1). By knowing the attributes of specific advances, the experts will have the occasion to create proficient, practical, and adaptable Smart City frameworks by actualizing the most reasonable one.

scholarly journals Big-But-Biased Data Analytics for Air Quality

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1551
Author(s):  
Laura Borrajo ◽  
Ricardo Cao
Keyword(s):  
Air Quality ◽  
Data Analytics ◽  
Mean Squared Error ◽  
Smart Cities ◽  
Real Data ◽  
Cumulative Distribution ◽  
Simple Random Sample ◽  
Urban Air ◽  
Data Set ◽  
The Mean

Air pollution is one of the big concerns for smart cities. The problem of applying big data analytics to sampling bias in the context of urban air quality is studied in this paper. A nonparametric estimator that incorporates kernel density estimation is used. When ignoring the biasing weight function, a small-sized simple random sample of the real population is assumed to be additionally observed. The general parameter considered is the mean of a transformation of the random variable of interest. A new bootstrap algorithm is used to approximate the mean squared error of the new estimator. Its minimization leads to an automatic bandwidth selector. The method is applied to a real data set concerning the levels of different pollutants in the urban air of the city of A Coruña (Galicia, NW Spain). Estimations for the mean and the cumulative distribution function of the level of ozone and nitrogen dioxide when the temperature is greater than or equal to 30 ∘C based on 15 years of biased data are obtained.

scholarly journals Internet of Things and Enhanced Living Environments: Measuring and Mapping Air Quality Using Cyber-physical Systems and Mobile Computing Technologies

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 720 ◽  
Author(s):  
Gonçalo Marques ◽  
Nuno Miranda ◽  
Akash Kumar Bhoi ◽  
Begonya Garcia-Zapirain ◽  
Sofiane Hamrioui ◽  
...  
Keyword(s):  
Air Quality ◽  
Internet Of Things ◽  
Mobile Computing ◽  
Real Time ◽  
Smart Cities ◽  
Cost Effective ◽  
Well Being ◽  
World Health ◽  
Computing Technologies ◽  
Living Environments

This paper presents a real-time air quality monitoring system based on Internet of Things. Air quality is particularly relevant for enhanced living environments and well-being. The Environmental Protection Agency and the World Health Organization have acknowledged the material impact of air quality on public health and defined standards and policies to regulate and improve air quality. However, there is a significant need for cost-effective methods to monitor and control air quality which provide modularity, scalability, portability, easy installation and configuration features, and mobile computing technologies integration. The proposed method allows the measuring and mapping of air quality levels considering the spatial-temporal information. This system incorporates a cyber-physical system for data collection and mobile computing software for data consulting. Moreover, this method provides a cost-effective and efficient solution for air quality supervision and can be installed in vehicles to monitor air quality while travelling. The results obtained confirm the implementation of the system and present a relevant contribution to enhanced living environments in smart cities. This supervision solution provides real-time identification of unhealthy behaviours and supports the planning of possible interventions to increase air quality.

Air Quality Monitoring Using Internet of Things (IoT) in Smart Cities

2019 ◽  
pp. 317-333
Author(s):  
Gayatri Doctor ◽  
Payal Patel
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Internet Of Things ◽  
Environmental Monitoring ◽  
Real Time ◽  
Smart Cities ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Monitoring Applications ◽  
Environmental Health Problem

Air pollution is a major environmental health problem affecting everyone. An air quality index (AQI) helps disseminate air quality information (almost in real time) about pollutants like PM10, PM2.5, NO2, SO2, CO, O3, etc. In the 2018 environmental performance index (EPI), India ranks 177 out of 180 countries, which indicates a need for awareness about air pollution and air quality monitoring. Out of the 100 smart cities in the Indian Smart City Mission, which is an urban renewal program, many cities have considered the inclusion of smart environment sensors or smart poles with environment sensors as part of their proposals. Internet of things (IoT) environmental monitoring applications can monitor (in near real time) the quality of the air in crowded areas, parks, or any location in the city, and its data can be made publicly available to citizens. The chapter describes some IoT environmental monitoring applications being implemented in some of the smart cities like Surat, Kakinada.

Techno-managerial implications towards communication in internet of things for smart cities

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Avinash Pawar ◽  
Ashutosh Kolte ◽  
Balkrishan Sangvikar
Keyword(s):  
Internet Of Things ◽  
Urban Areas ◽  
Design Methodology ◽  
Smart Cities ◽  
The Internet ◽  
The Novel ◽  
Content Type ◽  
The People ◽  
Managerial Implications ◽  
The Internet Of Things

Purpose The purpose of this paper is to explore the significance of the internet of things (IoT) system for smart cities and deliberate on the technological aspects involved in developing smart cities along with the framework, impact and benefits of IoT for smart cities. Design/methodology/approach This research is based on the review and synthesis of the papers on the broader areas of IoT for the application and implication towards the smart cities. The prime focus of this paper is to realize the IoT systems for smart city’s development and implementation of various technologies in the context of the Indian environment. Findings The outcome of the paper explores the highlights of the importance of the IoT system, including the technological framework, impact and benefits for smart cities. The outcome also highlights the application of IoT for smart cities. This paper provides direction regarding future degrees, potential conceivable outcomes and issues concerning the technological side of smart cities. IoT can change the lives of the people and support evolving urban areas for developing smart cities in India. Originality/value The paper deliberates on the novel techno-managerial approach towards the endeavour of smart cities using the IoT.

scholarly journals FIViz: Forensics Investigation through Visualization for Malware in Internet of Things

2020 ◽  
Vol 12 (18) ◽  
pp. 7262
Author(s):  
Israr Ahmad ◽  
Munam Ali Shah ◽  
Hasan Ali Khattak ◽  
Zoobia Ameer ◽  
Murad Khan ◽  
...  
Keyword(s):  
Decision Support ◽  
Internet Of Things ◽  
Decision Support Systems ◽  
Support Systems ◽  
Smart Cities ◽  
Security And Privacy ◽  
The Internet ◽  
Data Set ◽  
Raw Data ◽  
Internet Of Medical Things

Adoption of the Internet of Things for the realization of smart cities in various domains has been pushed by the advancements in Information Communication and Technology. Transportation, power delivery, environmental monitoring, and medical applications are among the front runners when it comes to leveraging the benefits of IoT for improving services through modern decision support systems. Though with the enormous usage of the Internet of Medical Things, security and privacy become intrinsic issues, thus adversaries can exploit these devices or information on these devices for malicious intents. These devices generate and log large and complex raw data which are used by decision support systems to provide better care to patients. Investigation of these enormous and complicated data from a victim’s device is a daunting and time-consuming task for an investigator. Different feature-based frameworks have been proposed to resolve this problem to detect early and effectively the access logs to better assess the event. But the problem with the existing approaches is that it forces the investigator to manually comb through collected data which can contain a huge amount of irrelevant data. These data are provided normally in textual form to the investigators which are too time-consuming for the investigations even if they can utilize machine learning or natural language processing techniques. In this paper, we proposed a visualization-based approach to tackle the problem of investigating large and complex raw data sets from the Internet of Medical Things. Our contribution in this work is twofold. Firstly, we create a data set through a dynamic behavioral analysis of 400 malware samples. Secondly, the resultant and reduced data set were then visualized most feasibly. This is to investigate an incident easily. The experimental results show that an investigator can investigate large amounts of data in an easy and time-efficient manner through the effective use of visualization techniques.

scholarly journals A compact, modular and low-cost Internet of Things (IoT) platform for air quality monitoring in urban areas

2020 ◽  
Vol 1710 ◽  
pp. 012004
Author(s):  
Christos Spandonidis ◽  
Stefanos Tsantilas ◽  
Elias Sedikos ◽  
Nektarios Galiatsatos ◽  
Fotios Giannopoulos ◽  
...  
Keyword(s):  
Air Quality ◽  
Internet Of Things ◽  
Urban Areas ◽  
Low Cost ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Iot Platform

scholarly journals Investigating the two-component model of solid fuel organic aerosol in London: processes, PM<sub>1</sub> contributions, and seasonality

2014 ◽  
Vol 14 (14) ◽  
pp. 20845-20882 ◽  
Author(s):  
D. E. Young ◽  
J. D. Allan ◽  
P. I. Williams ◽  
D. C. Green ◽  
R. M. Harrison ◽  
...  
Keyword(s):  
Air Quality ◽  
Solid Fuel ◽  
Urban Areas ◽  
Anthropogenic Activities ◽  
Renewable Energy Sources ◽  
Time Of Flight ◽  
Organic Aerosol ◽  
Mitigation Strategies ◽  
Data Set ◽  
Two Factors

Abstract. Solid fuel emissions, including those from biomass burning, are increasing in urban areas across the European Union due to rising energy costs and government incentives to use renewable energy sources for heating. In order to help protect human health as well as to improve air quality and pollution abatement strategies, the sources of combustion aerosols, their contributions, and the processes they undergo need to be better understood. A high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS) was therefore deployed at an urban background site between January and February 2012 to investigate solid fuel organic aerosols (SFOA) in London. The variability of SFOA was examined and the factors governing the split between the two SFOA factors derived from positive matrix factorisation (PMF) were assessed. The concentrations of both factors were found to increase during the night and during cold periods, consistent with domestic space heating activities. The split between the two factors is likely governed predominantly by differences in burn conditions where SFOA1 best represents more efficient burns in the south and SFOA2 best represents less efficient burns in the east and west. The differences in efficiency may be due to burner types or burn phase, for example. Different fuel types and levels of atmospheric processing also likely contribute to the two factors. As the mass spectral profile of SFOA is highly variable, the findings from this study have implications for improving future source apportionment and factorisation analyses. During the winter, SFOA was found to contribute 38% to the total submicron organic aerosol (OA) mass, with SFOA2 contributing slightly more than SFOA1 (20% compared to 18%). A similar contribution of SFOA was derived for the same period from compact time-of-flight AMS (cToF-AMS), which measured for a full calendar year at the same site. The seasonality of SFOA was investigated using the year-long data set where concentrations were greatest in the autumn and winter. During the summer, SFOA contributed 11% to the organic fraction, where emissions resulted from different anthropogenic activities such as barbecues and domestic garden wood burning. The significant contribution of SFOA to total organic mass throughout the year suggests that the negative effects on health and air quality, as well as climate, are not just confined to winter as exposure to these aerosols and the associated black carbon can also occur during the summer, which may have significant implications for air-quality policies and mitigation strategies.

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