Article

Internet of Things Mobile - Air Pollution Monitoring System (IoT-Mobair)

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Abstract

Internet of Things (IoT) is a worldwide system of “smart devices” that can sense and connect with their surroundings and interact with users and other systems. Global air pollution is one of the major concerns of our era. Existing monitoring systems have inferior precision, low sensitivity, and require laboratory analysis. Therefore, improved monitoring systems are needed. To overcome the problems of existing systems, we propose a three-phase air pollution monitoring system. An IoT kit was prepared using gas sensors, Arduino IDE (Integrated Development Environment), and a Wi-Fi module. This kit can be physically placed in various cities to monitoring air pollution. The gas sensors gather data from air and forward the data to the Arduino IDE. The Arduino IDE transmits the data to the cloud via the Wi-Fi module. We also developed an Android application termed IoT-Mobair so that users can access relevant air quality data from the cloud. If a user is traveling to a destination, the pollution level of the entire route is predicted, and a warning is displayed if the pollution level is too high. The proposed system is analogous to Google Traffic or the Navigation application of Google Maps. Furthermore, air quality data can be used to predict future air quality index (AQI) levels.

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... In fact, the IoT is an information carrier based on the internet, traditional telecommunications networks, which is a way for generally physical stuffs capable of being independently addressed to create the inter-web. It is also a system made of intelligent devices which senses its surroundings and interacts with the user or other systems [13]. Thus, the IoT is intelligent, with only addressable things or sensors depending on its communication protocol. ...
... In general, the hardware consisting of sensor nodes, interface circuits and embedded communication, the middleware for data storage and handling and a representation layer that consists of efficient visualization tools can form a complete IoT architecture. For the IoT model, there are three designed layers, the application layer, the network layer and the sensor layer, as indicated in Fig.2 [13]. In the sensor layer, data is collected using IP cameras, readers and pollution sensors [13]. ...
... For the IoT model, there are three designed layers, the application layer, the network layer and the sensor layer, as indicated in Fig.2 [13]. In the sensor layer, data is collected using IP cameras, readers and pollution sensors [13]. Information is transmitted at the network layer which acts as middleware between the sensor layer and the application layer using cellular, remote and broadcast networks [13]. ...
Article
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Air pollution seriously damages the ecological environment and affects the health of humans, animals and plants. In order to prevent and control air pollution, air quality monitoring has been proposed to accurately monitor the concentration of pollutants and abnormal conditions in a region, which can purify the air. However, conventional fixed monitoring stations are complex, expensive and power hungry, these characteristics lead to monitoring stations being unsuitable for most areas. Here an internet of things (IoT)-based air pollution monitoring system is reported including architecture, principles and monitoring objects, providing detailed information for effective environmental management. It manages the current status and trends of regional air pollution in a simple, portable and cost-effective way. The technologies and applications involved in IoT-based air pollution monitoring systems are also presented, contributing to the development of IoT technologies, cloud computing technologies and smart city. The rapid spread of such monitoring systems would add greenhouse gases, photochemical smog and light detection and ranging (LIDAR) gases to the group of objects to be monitored in the future.
... However it is a challenging task to obtain security in IoT due to its inherent features and heterogeneous sensors. Many research works [5][6][7][8][9] have been presented to realize different security schemes in WSN for IoT applications. The proposed security schemes should provide con dentiality, data integrity, accuracy and should function against internal and external attacks. ...
... In those works, the security concerns in data transmission have not been discussed. Consequently in works [5][6][7][8][9][10], the security added routing protocols for data transmission is presented. Various types of secure routing and energy conservation mechanisms are presented in those papers. ...
... In [5], the air pollution monitoring application using WSN is designed. The data transmission in this application is done by allotting a unique identi er (id) to each sensor node. ...
Preprint
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In the developed field of information and communication systems, the utilization of wireless sensor networks (WSN) is greatly increasing. In WSN applications, data gathering with limited energy consumption and good security is essentially needed nowadays. The network is energy limited as the sensor nodes are operated using battery power. The nodes in WSN are vulnerable to several attacks and the proposed data transmission scheme should provide network security. The security is a main concern in data aggregation. In this work, a secured data aggregation scheme with compression technique and blockchain encryption added routing is presented. The existing methods on secured data aggregation give protection against attacks; still they suffer from issues such as energy, memory size and transmission costs. The devices in the network transmit data to the base station/server in the network through gateways. The data transmitted by the device is added with a hash key generated using spider web based dynamic key (SWDK) generation process. Then, compression based data aggregation is utilized to reduce the data size and sequentially the transmission cost. The security added compressed data is transmitted using block chain encryption routing. The simulation results prove that the proposed work gives reduced transmission cost and energy consumption comparing with the existing works. The network throughput increases due to the sharing security keys, as well as network latency and packet dropping are reduced in the proposed work.
... The gases taken into account are Sulfur Dioxide (SO 2 ), Nitrogen Dioxide (NO x ), Ozone (O 3 ), and Carbon Monoxide (CO) [1,10,[19][20][21]. However, as has been witnessed in several papers [14,22,23], there is no metric of ambient air pollution that includes related factors such as temperature, relative humidity, and ultraviolet (UV) exposure. In addition, having daily samples of the above-mentioned variables allows government entities to find similarities within human activities. ...
... Subsequently, ref. [5] focuses on sensor calibration techniques in different wireless sensor network nodes for a correct data acquisition process. In addition, in [2,9,22] the authors focused on the design of electronic systems that are part of IoT architectures, and can send data using edge computing platforms for data preprocessing (cleaning) stages. Furthermore, these electronic systems have interfaces to cloud servers for constant data monitoring and sending messages in the event of unexpected events. ...
... Therefore, it is necessary to work with lightweight network protocols and services, which are also oriented to the type of information being sent [18]. For this reason, the message sending protocol is the Message Queuing Telemetry Transport (MQTT), because it has a variable light payload messaging service with a publisher/subscriber model [22]. Consequently, Mosquitto (https://mosquitto.org/ ...
Article
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Nowadays, increasing air-pollution levels are a public health concern that affects all living beings, with the most polluting gases being present in urban environments. For this reason, this research presents portable Internet of Things (IoT) environmental monitoring devices that can be installed in vehicles and that send message queuing telemetry transport (MQTT) messages to a server, with a time series database allocated in edge computing. The visualization stage is performed in cloud computing to determine the city air-pollution concentration using three different labels: low, normal, and high. To determine the environmental conditions in Ibarra, Ecuador, a data analysis scheme is used with outlier detection and supervised classification stages. In terms of relevant results, the performance percentage of the IoT nodes used to infer air quality was greater than 90%. In addition, the memory consumption was 14 Kbytes in a flash and 3 Kbytes in a RAM, reducing the power consumption and bandwidth needed in traditional air-pollution measuring stations.
... Dhingra et al. [7] conducted another research on air quality monitoring systems that used the internet of things (IoT) based smart devices. They suggested a three-phase system that comprises an IoT kit with multiple gas sensors (MQ7, MQ2, and MQ135), an Arduino Uno microcontroller, and a Wi-Fi module. ...
... Specifications of the used air pollutants estimating sensors have been illustrated in Table II. This research employs these low-cost and readily available sensors because they have been used effectively in many published manuscripts [5][6][7]. Table II: Specifications of the used air pollutants measuring sensors. Fig. 1 demonstrates the project's primary circuit, designed in the EasyEDA software tool, and the final external components, which are connected to the drone and detect various contaminants in the air. ...
Article
Air pollution is now becoming a global issue because it directly affects human health, by creating cancer, respiratory and cardiac diseases, and the earth's ecosystem, by causing climate change and depletion of ozone layers. The rapid expansion of civilization and industrialization, leading to hazardous emissions from automobiles, chemical industries, and fossil fuel burning, plays a significant role in air and atmospheric pollution. Unmanned aerial systems and the internet of things-based sensing devices have provided new impetus for automatic air pollution monitoring and analysis. In this paper, an automatic indoor and outdoor air quality monitoring system has been developed using an unmanned aerial vehicle: a drone. The proposed device is constructed from an ultra-strength DJI F450 frame, various gas sensors, a particulate matter detector, and a camera to detect and distinguish air pollution data. The precision and sensitivity of the proposed device have been evaluated by collecting air emissions data from two places in Dhaka, Bangladesh. The drone has been navigated in Uttara and Bashundhara areas to measure the amount of CO, CO 2 , O 3 , SO 2 , NOx, and dust in indoor and outdoor environments. We obtained higher air pollution concentrations in Uttara than in Bashundhara, as it is an area with more industries and construction sites. The proposed system composed of an uncrewed aerial device and low-cost gas monitoring sensors is expected to create new possibilities in environmental and air pollution monitoring.
... For example, services such as healthcare systems [35], virtual reality [3], and autonomous and connected cars [30] are time-sensitive. In contrast, big data analysis [36], pollution monitoring [9], and scientific computations [22] may be delay-tolerant. ...
Preprint
Full-text available
Provisioning services for Internet of Things (IoT) devices leads to severalchallenges: heterogeneity of IoT devices, varying Quality of Services (QoS)requirements, and increasing availability of both Cloud and Fog resources. Thelast of these is most significant to cope with the limitations of Cloud infrastructureproviders (CIPs) for latency-sensitive services. Many Fog infrastructure providers(FIPs) have recently emerged and their number is increasing continually. FLEX isproposed in this work as a platform for selecting a location for service placementin a multi-Fog and multi-Cloud environment. For each service, FLEX broadcastsservice requirements to the resource managers (RMs) of the available Fog andCloud service providers and then selects the most suitable provider for that service.FLEX is scalable and flexible as it leaves it up to the RMs to have their ownpolicy for the placement of submitted services. Service placement and resource selectionhas been formulated as an optimization problem and an efficient heuristicalgorithm is proposed to solve it. Results show that the proposed algorithm canbe used across both Cloud and Fog-based providers.
... [14] IoT-Mobair App also used MQ135 for air quality measurement which helps the user in predicting the pollution level of their entire route. [10] The study of output characteristics of MQ135 is very important as it is used in a variety of applications. ...
Conference Paper
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MQ135 is a cheap CO2 gas sensor used frequently for air pollution monitoring. CO2 gas concentration sensed by MQ135 sensor is a function of its load resistance. The research is carried out to compare the sensor characteristics of three MQ135 gas sensors having 1KΩ,22KΩ and 47KΩ load resistances. The study is done in the open environment as well as in a closed chamber. The comparative study of output characteristics of MQ135 sensor clarifies the effect of load resistance on the measurement of CO2 gas concentration. The conclusion of the paper will help the researchers to use MQ135 gas sensor in more effective way in research of air pollution monitoring. Keywords: MQ135; load Resistance; CO2; pollution monitoring
... Internet of Things (IoT) is a worldwide system of "smart devices" that can sense and connect with its surroundings and interact with users and other systems (Dhingra, Madda, Gandomi, Patan, & Daneshmand, 2019). IoTs are used as the medium to communicate with humans and non-living objects using sensors to navigate, sense and collect data before loading to the cloud storage. ...
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Currently, the department of Jabatan Kerja Raya (JKR) in Malaysia depends on the notification from public’s complaints or on-site spot checks when there are tilted road sign boards. This has caused delays in repairs and could put public safety in jeaopardy. In this paper, an IoT-based alert notification for tilted road sign boards using solar energy is proposed. A Rapid Application Development methodology (RAD) is used to produce a working prototype of the proposed system with NodeMCU ESP8266 microcontroller and Arduino IDE. The results of our implementation show that we are able to detect tilted sign boards and send email alert notifications to the responsible parties accordingly.
... The system is designed to monitor liquified petroleum gas (LPG) using an MQ6 sensor connected to a data transmission laptop. Dhingra et al. [72], Huang et al. [60], and Sun and Zhu [73] proposed designs for wireless mobile air-pollution monitoring applications using cloud-based services to acquire data cost-effectively with low-cost sensors. In India, the Central Pollution Control Board has implemented a nationwide program known as the National Air Quality Monitoring Programme (NAMP Reports, http://cpcbenvis.nic.in/airpollution/finding.html, accessed on 4 September 2022) for ambient air-quality monitoring. ...
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Air pollution has been an vital issue throughout the 21st century, and has also significantly impacted the agricultural community, especially farmers and yield crops. This work aims to review air-pollution research to understand its impacts on the agricultural community and yield crops, specifically in developing countries, such as India. The present work highlights various aspects of agricultural damage caused by the impacts of air pollution. Furthermore, in the undertaken study, a rigorous and detailed discussion of state-wise and city-wise yield-crop losses caused by air pollution in India and its impacts has been performed. To represent air-pollution impacts, the color-coding-based AQI (Air Quality Index) risk-classification metrics have been used to represent AQI variations in India’s agrarian states and cities. Finally, recent impacts of air pollution concerning AQI variations for May 2019 to February 2020, Seasonal AQI variations, impacts of PM2.5, and PM10 in various agrarian states and India cities are presented using various tabular and graphical representations.
... To overcome the problems of existing systems (that is, low precision, low sensitivity, and require laboratory analysis), S. Dhingra et al., [19] a three-phase air pollution monitoring system. An IoT kit was prepared using gas sensors, Arduino integrated development environment (IDE), and a Wi-Fi module. ...
Article
In the last years, the smart city paradigm has been deeply studied to support sustainable mobility and to improve human living conditions. In this context, a new smart city based on Social Internet of Things paradigm is presented in this article. Starting from the tracking of all vehicles (that is, private and public) and pedestrians, integrated with air quality measurements (that is, in real time by mobile and fixed sensors), the system aims to improve the viability of the city, both for pedestrian and vehicular users. A monitoring network based on sensors and devices hosted on board in local public transport allows real time monitoring of the most sensitive areas both from traffic congestion and from an environmental point of view. The proposed solution is equipped with an appropriate intelligence that takes into account instantaneous speed, type of traffic, and instantaneous pollution data, allowing to evaluate the congestion and pollution condition in a specific moment. Moreover, specific tools support the decisions of public administration facilitating the identification of the most appropriate actions for the implementation of effective policies relating to mobility. All collected data are elaborated in real time to improve traffic viability suggesting new directions and information to citizens to better organize how to live in the city.
... The IAQD allows users to remotely track the IAQ status and supports various communication scenarios such as wired communications, short-range wireless communications, and remote transmission to the cloud. Dhingra et al. [14] proposed an IoT-based mobile air pollution detection system, which mainly aimed at the detection of outdoor air quality, not indoor air. By using distributed deep reinforcement learning, Hu et al. [15] proposed a mobile robots-assisted cooperative indoor air quality sensing system, called AirScope, which can effectively reduce the data latency. ...
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In recent years, more and more occupants have suffered from respiratory illness due to poor indoor air quality (IAQ). In order to address this issue, this paper presents a method to achieve efficient monitoring and adaptive control of IAQ. Firstly, an indoor air quality monitoring and control system (IAQMCS) is developed using IoT technology. Then, based on fuzzy inference, a novel fuzzy air quality index (FAQI) model is proposed to effectively assess IAQ. Furthermore, a simple adaptive control mechanism, called SACM, is designed to automatically control the IAQMCS according to a real-time FAQI value. Finally, extensive experiments are performed by comparing with regular control (time-based control), which show that our proposed method effectively measures various air parameters (CO2, VOC, HCHO, PM2.5, PM10, etc.) and has good performance in terms of evaluation accuracy, average FAQI value, and overall IAQ.
... Zakaria et al. [37] came up with an innovative wireless IoT framework for air quality monitoring indoors by adopting low-cost sensors, Rasberry Pi, cloud storage, and smart algorithms, which yield notifications during unhealthy conditions to make a living better. Recently, Dhingra et al. [38] developed an Android application along with gas sensors, Wi-Fi technology, and Aurdino for air pollution prediction during travel with the help of pollution data. Arano et al. [39] put forward a decision system for determining personal exposure to air pollution in Madrid by utilizing spatiotemporal data from sensors. ...
Article
Sustainable transport in cities has been gaining a lot of attraction recently and a core focus of engineering management. Internet of Things (IoT) is seen as a widely accepted technology that promotes sustainability through the interconnection of diverse computing sources for solving environmental problems. Previous studies on IoT have discussed interesting factors toward its adoption, but selecting a suitable IoT service provider (IoTSP) is an open challenge due to a diverse set of factors in practice. Driven by the challenge, in this article, a generalized fuzzy-based decision model is put forward for IoTSP selection, which is the prime objective of the study. Initially, a strength, weakness, opportunity, threat (SWOT) analysis is adopted to identify the crucial challenges in IoT adoption. Later, the relative significance of these challenges is calculated by adopting the regret/rejoice approach. Due to uncertainty, certain rating information of IoTSPs is missing that are rationally imputed by proposing an analytical approach. Rating matrices from experts are transformed into opinion vectors, and a prioritization algorithm is developed with query vector for rational personalized ordering of IoTSPs. Data for the study are acquired via questionnaire, which is filled by experts. The efficacy of the developed model is exemplified by using a real case study of IoTSP selection for pollution management in Chennai. Concerning the findings, mobility, security, and connectivity are the most vital factors for IoTSP selection. Results show that the proposed model is a viable tool for IoTSP selection and it is robust, unique, and stable compared to its counterparts.
... When it hits 2,000, the siren keeps going off and "Danger!" appears on the LCD on the website. The fresh air [11]. ...
Article
The Internet of Things (IoT) is a worldwide network of "smart gadgets" that can interact with people and other systems. It can sense and connect to their surroundings. Air pollution is a significant problem nowadays. The present monitoring techniques have poor accuracy, sensitivity and need for laboratory testing [1]. We have proposed a three-level air pollution monitoring system to address these issues with current regulations. Gas sensors, the Arduino Integrated Development Environment (IDE), and a Wi-Fi module form the foundation of the IoT kit. Gas sensors gather information from their surroundings and send it to the Arduino IDE. It uses the Arduino Wi-Fi module to send IDE data to the cloud. We’ve also created the Android application IoT-Mobair, which enables users to access the pertinent cloud-based air quality data [3]. When the user arrives at their location, the pollution level is graded throughout the trial, if it is too high an alarm is displayed. Air quality data may also be used to predict future air quality index (AQI) values
... This system acquires continuous video and transmits it using Wi-Fi capabilities of ESP32. Dhingra presented an air pollution monitoring system [6]. Many gas detecting units are connected to the Arduino board that is controlling ESP8266 devices. ...
Conference Paper
Full-text available
This paper describes process of designing and testing an Internet of Things (IoT) system for continuous receiving of the input data. The goal is to design a custom IoT system and to test the reliability of the designed system, but also to offer solutions for the improvement. Entire process of designing of both hardware part of the system and the software part of the system is explained and main tool used are described. Main characteristics of the designed systems that are tested are basic RF characteristics but also transmission of data of various waveforms. Implementation and analysis of this type of testing data is important, especially because properties that are tested are part of the majority of modern IoT systems.
... Although IoT finds its use in many industries and applications, its ability to monitor pollution levels within cities by using specific devices and sensors makes it a necessary technology for pollution control [17,18]. It works by placing the multiple stations in different city areas where the stations continuously upload and transmit data to the IoT cloud server [19]. At the time of data collection, sensors also generate noise in the data, which are essential to remove to increase the system's accuracy. ...
Article
Full-text available
Air pollution is one of the biggest concerns in the world but it has not been paid much attention in developing countries. It is necessary to design models and methods to understand air pollution in developing countries to reduce the rate of pollution. This paper proposes an Internet of Things (IoT) and Artificial Intelligence (AI)‐based hybrid model to predict the Air Quality Index (AQI) with a practical case study of the public data sets. The sensor node is deployed in the city to collect air quality data. Moreover, this sensor node connects to the cloud server for collecting data at the firebase real‐time database through a WiFi/5G network embedded in the raspberry controller. Carbon monoxide (CO) and fine particular matter PM2.5 sensors are integrated within a sensor node to monitor the AQI of the regions. A Kalman fis also applied to remove unwanted noise from the data collected through the sensor node. Models namely Artificial Neural Network (ANN), Support Vector Machine (SVM), k‐nearest neighbour (k‐NN), Convolutional Neural Networks (CNN), Long Short Term Memory (LSTM), CNN‐LSTM, ensemble model, and a proposed model, that is, CNN‐LSTM‐Bayesian optimization algorithm (BOA) model, have been utilised to predict the AQI. The performance evaluation of models is done through statistical parameters, such as mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R²), and accuracy score on two different public data sets and compared with the baseline models. The performance of the CNN‐LSTM‐BOA model is better than baseline models in terms of above‐mentioned statistical parameters as the accuracy reported is more than 97 %.This study can help predict the Air Quality Index and provide sufficient time to generate warning signals in the location.
... Consequently many cities in SSA and other LMICs are characterised by sparse and limited spatial resolution of air pollution data, inadequate for modelling and analysis to inform decision making and mitigation actions. Recently, Internet of Things (IoT) platforms are being leveraged as a low-cost air quality monitoring approach 13,14,15 . Smart portable and low-cost air quality monitors can be deployed in diverse locations of the city allowing for measurement of pollution levels in near real-time. ...
Article
Full-text available
Low‐cost air quality monitoring networks can potentially increase the availability of high‐resolution monitoring to inform analytic and evidence‐informed approaches to better manage air quality. This is particularly relevant in low and middle‐income settings where access to traditional reference‐grade monitoring networks remains a challenge. However, low‐cost air quality sensors are impacted by ambient conditions which could lead to over‐ or underestimation of pollution concentrations and thus require field calibration to improve their accuracy and reliability. In this paper, we demonstrate the feasibility of using machine learning methods for large‐scale calibration of AirQo sensors, low‐cost PM sensors custom‐designed for and deployed in Sub‐Saharan urban settings. The performance of various machine learning methods is assessed by comparing model corrected PM using k‐nearest neighbours, support vector regression, multivariate linear regression, ridge regression, lasso regression, elastic net regression, XGBoost, multilayer perceptron, random forest and gradient boosting with collocated reference PM concentrations from a Beta Attenuation Monitor (BAM). To this end, random forest and lasso regression models were superior for PM2.5 and PM10 calibration respectively. Employing the random forest model decreased RMSE of raw data from 18.6𝜇g/m3 to 7.2 𝜇g/m3 with an average BAM PM2.5 concentration of 37.8𝜇g/m3 while the lasso regression model decreased RMSE from 13.4 𝜇g/m3 to 7.9 𝜇g/m3 with an average BAM PM10 concentration of 51.1 𝜇g/m3. We validate our models through cross‐unit and cross‐site validation, allowing analysis of AirQo devices' consistency. The resulting calibration models were deployed to the entire large‐scale air quality monitoring network consisting of over 120 AirQo devices, which demonstrates the use of machine learning systems to address practical challenges in a developing world setting.
... Their project envisions data being recovered from a standard not structured query language (NoSQL) database and structured according to specified requirements being uploaded to the ethereum blockchain on a daily basis, with the option to manually select the time period of interest. The author proposed a three-phase air quality surveillance system in [25]. Gas sensors, an arduino integrated development environment (IDE), and a wi-fi module was used to create an Internet of Things kit. ...
Article
Full-text available
According to United Nations (UN) 2030 agenda, the pollution detection system needs to be improved for the establishment of fresh air to obtain healthy life of living things. There are many reasons for the pollution and one of the reasons for pollution is from the emissions of the vehicles. Currently digital technologies such as the Internet of Things and Long-Range are showing significant impact on establishment of smart infrastructure for achieving the sustainability. Based on this motivation, this study implemented a sensor node and gateway-based Internet of Things architecture to monitor the air quality index value from any location through Long-Range communication, and Internet connectivity. To realize the proposed system, a customization of hardware is carried out and implemented the customized hardware i.e., sensor node and gateway in real-time. The sensor node is powered with node mapping to minimize the data redundancy. In this study, the evaluation metrics such as bit rate, receiver sensitivity, and time on air are evaluated by spreading factor (SF), code rate (CR), bandwidth, number of packets, payload size, preamble, and noise figure. The real-time sensor values are logged on the cloud server through sensor node and gateway. The sensor values recorded in the cloud server is compared with optimal values and concluded that the PM10, PM2.5 are high in the air and remaining values of NO2, O3, CO are optimal in the air. Along with this an architecture is proposed for interfacing the hardware with blockchain network through cloud server and API for node authentication.
... Sensors using Raspberry Pi/ Arduino and IoT devices can monitor the local air quality [175]. Dhingra et al. develop an application i.e. "IoT-Mobair", which is mobile-based use to monitor and detect the air pollution of the concerned area [76]. This mobile-based application has various features like air quality, daily forecasts, health-related tips, and risks, air quality map generations etc. ...
Article
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With the commencement of new technologies like IoT and the Cloud, the sources of data generation have increased exponentially. The use and processing of this generated data have motivated and given birth to many other domains. The concept of a smart city has also evolved from making use of this data in decision-making in the various aspects of daily life and also improvement in the traditional systems. In smart cities, various technologies work collaboratively; they include devices used for data collection, processing, storing, retrieval, analysis, and decision making. Big data storage, retrieval, and analysis play a vital role in smart city applications. Traditional data processing approaches face many challenges when dealing with such voluminous and high-speed generated data, such as semi-structured or unstructured data, data privacy, security, real-time responses, and so on. Probabilistic Data Structures (PDS) has been evolved as a potential solution for many applications in smart cities to complete this tedious task of handling big data with real-time response. PDS has been used in many smart city domains, including healthcare, transportation, the environment, energy, and industry. The goal of this paper is to provide a comprehensive review of PDS and its applications in the domains of smart cities. The prominent domain of the smart city has been explored in detail; origin, current research status, challenges, and existing application of PDS along with research gaps and future directions. The foremost aim of this paper is to provide a detailed survey of PDS in smart cities; for readers and researchers who want to explore this field; along with the research opportunities in the domains.
... An air pollution Geo-sensor network has been modelled by (Al-Ali et al., 2010) to obtain the AQI (Air Quality Index) where sensors are taking 24/7 real-time readings of CO, NO 2 and SO 2 components and transmit the pollutant data to a database through a server. (Dhingra et al., 2019) developed a system to observe the concentration of Carbon Monoxide (CO), Methane (CH 4 ) and Carbon dioxide (CO 2 ) gases to measure the AQI. A platform has been developed by (Zaldei et al., 2017) to monitor air pollution and traffic movements which captures CO, NO 2 , and C 2 concentrations using open-source Arduino technology. ...
Conference Paper
Air pollution is a major concern for countries around the world. According to World Health Organization (WHO), seven million people die worldwide every year caused by air pollution. Bangladesh has not only serious pollution problems but also it is ranked first among the world's most polluted countries with a PM2.5 reading of 76.9 microgrammes per cubic meter (μg/m³) in the year 2021 (AQI Bangladesh, 2021). In this paper, we propose to develop a data-driven software system for monitoring the air quality of Bangladesh. Our proposed system will provide atmospheric maps and charts for monitoring the current and future Air Quality Index (AQI) of any area. We conducted an experiment for 1-year time span to observe the concentration level and data patterns of PM2.5 in our country focusing on the transportation routes and industrial zones. The data is collected from the sensors and satellites of different stations covering multiple areas. The results are analyzed in the context of divisions, transportation stations, industrial zones, and time. For a variety of air quality indicators, the experimental results were compared to IQAir AirVisual Pro and showed good results, with very small differences between our obtained result and IQAir AirVisual Pro. Our goal is to mainly monitor the industrial zones, power plants, divisions, and transportation routes as most toxic compounds are formed there.
... Lobur et al. used the Atmel 8-bit AVR microcontroller to measure essential pollutants such as CO, PM 2.5, CO2, temperature, and humidity in real-time [26]. Dhingra et al. [27] proposed the gas sensor kit along with Arduino and Wi-Fi module to monitor the air pollution on the IoT networks. In this, the Arduino module fetches the sensor data and transmits it to the cloud via a Wi-Fi module. ...
Article
The atmospheric boundary layer (ABL) plays a significant role in defining the air-quality index of an environment. It determines the environmental capacity for the diffusion of atmospheric pollutants. The air-quality in a designated area is influenced by the local air pollution as well as the transported pollutants from remote locations. Estimation of mixing-height helps to determine the volume space in which the emitted pollutants are dispersed. The continuous and effective monitoring of mixing-height in real-time is a major concern for the research community. Sonic Detection and Ranging (sodar) is crucial for real-time and continuous determination of mixing-height. This paper proposes a novel Sodar-based meteorological sensor network (SMSN) with the Internet of Things (IoT) capability. In the SMSN, the temperature, relative humidity, and wind sensors are integrated with sodar and deployed to seven locations in Northern India. The sensors with IoT work as sensor nodes and provide accessibility to users for air-quality monitoring in real-time. The IoT-enabled SMSN displayed impressive standard uncertainty for data packet losses across all the sites and parameters. Additionally, correlation analysis is performed between the SMSN parameters and key air-pollutants of each sensor node. The correlation analysis shows good relevance between the regional parameters and Delhi's parameters. The integration of IoT with sodar and meteorological parameters is important for improving the overall decision-making and planning of Delhi's air quality.
Chapter
More than 212 billion devices will use the internet at the end of 2020. According to this information, more accurate artificial intelligence (AI) approaches are required for more efficient Internet of Things (IoT) usage. Wireless sensor networks (WSNs) contain energy-limited devices and calculating the minimum transmission power control (TPC) is a tackling process. Swarm intelligence is a subsection of AI and in the last four decades, many swarm intelligence algorithms are proposed for solving optimization problems. The minimization of energy usage and maximizing the network lifetime are many useful and essential for IoT. In this work, four different swarm intelligence algorithms—particle swarm optimization (PSO), artificial bee colony (ABC), salp swarm algorithm (SSA), and tree-seed algorithm (TSA)—are used for solving the minimum TPC optimization problem. The obtained results, convergence graphs, and standard deviations are showed that ABC is the best swarm intelligence algorithm, and the TSA is the most robust algorithm in this experimental environment.KeywordsTransmission power controlSwarm intelligenceInternet of thingsMetaheuristic algorithms
Chapter
The power system grid is expanding at an exponential rate. The load demand has been increasing followed with a rapid change in technology. The renewable energy sources are also increasing their percentage share in the total energy generated. Controlling the grid is a big challenge and it requires efforts not only from the generation side but also the consumers side. The consumers can control or adjust the pattern of consumption thus providing flexibility and improving the reliability of the grid. The participation of consumers in the optimal operation of the grid is termed as Demand Response. The demand response should as well optimized through the application of optimization techniques. In this chapter, the implementation of evolutionary optimization techniques for solving the complex demand response problem is presented. It also discusses various factors that affect the demand response and its problem formulation. In the end a list of publications are enlisted which have used evolutionary optimization techniques to solve demand response.KeywordsDemand responseOptimization techniquesSmart grid
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Internet-of-things (IoT) is a fast-growing technology and sustainable transportation is a crucial application area of IoTs. Selecting an appropriate IoT service provider (IoTSP) is cumbersome and viewed as a multi-criteria decision problem. Recent studies on IoTSP selection inferred that uncertainty and subjectivity during preference elicitation are not flexibly handled. Moreover, they mentioned hesitation and interrelationship are crucial behaviors to be modeled. Motivated by the inferences, this research aims to develop an integrated framework with q-rung orthopair fuzzy sets (q-ROFSs) to evaluate IoTSPs. In this line, the CRITIC method is presented for experts’ importance determination under the q-ROF context. Later, Cronbach’s measure is used for criteria weight calculation with q-ROFSs. Also, an algorithm for prioritization is developed by extending the CRADIS formulation. Lastly, the proposed framework is testified through a case example of IoTSP selection for smart city utilities in India. Results infer that availability, total cost, and security/privacy are crucial criteria for evaluating IoTSPs. Sensitivity and comparison analysis is further conducted to determine the framework’s robustness. This study can assist urban planners, politicians, and other stakeholders in selecting proper IoTSPs when forming sustainable smart cities.
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Chapter
Air pollution in cities has become an important topic due to its adverse effects on humans and air quality. The aim of this paper is to monitor particles less than 1 \(\upmu \)m (PM\(_1\)), 2.5 \(\upmu \)m (PM\(_{2.5}\)), 4 \(\upmu \)m (PM\(_4\)), and 10 \(\upmu \)m (PM\(_{10}\)). The concentration of particulate matter highly changed with location and time. Particulate matter in the air is considered as the primary pollutant, and it affects the environment band the risk of mortality and morbidity of respiratory disease. To address this issue, the research presents the design and development of the low-cost network for monitoring particulate matter using sensiron sensor (SPS30). These devices are equipped with LoRaWAN to test the low-power wide-area network coverage. The designed network contains the sensors connected to the ESP32 microcontroller towards the processing of LoRa modules (sensor nodes), which send data to the gateway using the frequency band, using the ‘The Things Network (TTN)’. The sensor collects the different particle matter in the air. The proposed network design has been implemented at St. Paul Street Auckland. The designed network system allows the users to access the online dashboard to test and monitor the concentration levels of particulate matter in the air.KeywordsAir quality indexingLoRaParticulate matter
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Conference Paper
Air pollution has caused a vicious impact on the climate and human health. As a consequence, it has drawn the attention of researchers, policymakers, and citizens. Traditionally, air pollution is measured using heavy and expensive instruments at fixed monitoring stations. But the number of these monitoring stations is very few. Also, the scale of fire accidents due to flammable gas leakage is increasing rapidly. To control air pollution and to reduce fire accidents, a vastly installed device is a dire need. In the following research, we have proposed a low-cost sensor-based monitoring system with multiple network options, along with some preliminary data analysis results. Our system is capable of sharing quantitative data of air pollution globally using Global System for Mobile Communications (GSM), Wi-Fi, and Bluetooth. It also has a smart alarm system to alert authorized personals. The foreground of this research is implementing a feasible system that can benefit the 3rd world countries as well as the developed ones.KeywordsAir pollutionMobile sensingGas sensorsWireless communicationArduinoIoTSecurity System
Conference Paper
IoT idea allows things to exchange their data for communication purposes via wired or wireless connections. The Industrial Internet of Things (IoT) is an expanded IoT concept that includes integrating the acquiring, transmission and processing of temperature and humidity data in a real-time network. In many applications, IoT has now participated in the building of a smart environment. IoT’s real-time capacity is regarded as a vital element in environmental monitoring and control applications. Therefore, system operators may make better judgments both in technical and financial aspects using the real-time monitoring system. In this article, for environment surveillance a high-speed IoT-based monitoring system is created and deployed with recording features. Because field-programmable port arrays are highly reliable and process speed, a built-in micro controller is utilized in this system. The IoT platform gives system operators with remote visibility in real time. The major objective of this project is to deliver a realistic application that has been deployed and tested in a monitoring environment. The system integrates the capabilities of an IoT platform that meet the requirement of high-speed real time applications while the reference for both stable and transient situations is a single high-resolution time source.
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The Internet of Things (IoT) is a global system of “smart devices” which senses and connects with their environment and communicates with users as well as other systems. Air Pollution (AP) is one of the most significant global issues. PrevailingAPsystems have low accuracy and require laboratory-based analysis. Therefore, improved prediction systems are needed. To overcome such problems, this paper proposes an IoT based efficient APprediction system utilizing the Deep Learning Modified Neural Network (DLMNN)classifier. Initially, the faulty node detection is done in the sensor nodes using the H-ANFIS algorithm. Here, ANFIS is hybridized with the K-Medoid algorithm. After that, the features are extracted from the sensed data and the unnecessary features are reduced by using the MPCA algorithm. Next, based on the reduced features, the data are balanced by using Entropy-HOA. Then, the balanced sensed data are pre-processed using replacing of missing attributes and HDFS. Next, the pre-processed data are tested with an APprediction system employing the DLMNN classifier, where the Pity Beetle Algorithm (PBA) is used for weight optimization. Then, the predicted result is stored in the cloud server. Finally, the stored data is visualized. Experimental results have proved that the proposed system gives a better result than the other systems.
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Air pollution became the major problem in the world. The world is getting polluted because of emission of dangerous gases into air such as CO2, SO2, NO2, and CO. These toxic gases are dissolved in air and cannot be predicted. Hence a tool is required to check the air quality. The air pollution can be monitor by using internet based devices like IoT. Internet of thing (IoT) devices can collect the data and based on data can analysis for prediction i.e. quality of air is good or not. Thus, the air quality of a particular area can be monitored using IOT based devices and sensors using Arduino/Raspberry Pi. The purpose of this research study is to understand Information on environmental variables and also allowing easy integration into any other type of internet-based architecture (IoT) which allows the use of sensors capable of collect information on sensors related to smart city environment measurements, with a view to providing data on which environmental pollution-related information.
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The real-life deployments of air pollution monitoring systems are sparse, due to large size, high cost, and high-power consumption. Such sparsely deployed sensing stations are unable to provide a fine granular pollution mapping of a given geographical area. By deploying low cost, low power, miniature air pollution monitoring sensor nodes, the air pollution map of the whole area can be accurately measured. However, accuracy of the sensed data of the low cost miniature sensing nodes (MSNs) needs to be addressed. This paper presents an autocalibration method of low cost MSNs, with the help of sparsely deployed high cost sensing stations (HCSSs). The datasets from the HCSSs are collected and used to calibrate the MSN using a suitable learning-based regressor model at the nearby edge node. To this end, this paper proposes a cross-correlation based method of determining the optimum time to re-calibrate the low cost sensors in a multi-sensing node. This method eliminates the requirement of taking the MSNs offline to calibrate/re-calibrate them. To apply the proposed autocalibration method, this paper additionally presents the design of a low cost, low power particulate matter (PM) sensor. To validate the performance of the low cost PM sensor, the calibrated PM data are compared with the data collected from a colocated commercially available PM sensor, which is considered as reference. The low cost PM sensor is 91% more cost efficient and 57% more energy efficient compared to the commercial high cost PM sensor, while maintaining the sensing error within a given threshold.
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“Carbon neutrality, carbon peaking” is China’s national commitment to the whole world about its plans to manage global climate change. China faces many severe challenges in fulfilling its commitments to reduce emissions. China’s digital economy is currently booming, and whether it can provide opportunities for reducing regional carbon emissions is worth exploring. This study constructed a comprehensive system to evaluate the development of its digital economy based on China’s regional data and empirically tested the direct, indirect, and spatial effects of the comprehensive development of digital economy on regional carbon emissions. In addition, it examined the special stage characteristics using a Hansen threshold model. This study found the following: first, the digital economy significantly suppresses carbon emissions in general, notably with a spatial spillover effect to neighboring provinces. Secondly, an analysis of the mechanism shows that the comprehensive development of a digital economy can restrain regional carbon emissions through industrial progress and the optimization of energy consumption. Third, there are double thresholds, special driving trends and an “inverted N-type” relationship with development. Fourth, a spatial heterogeneity analysis revealed that significant “local” and “neighboring” impacts on the reduction of carbon emissions only exist in the central and eastern areas. This study has a reference value for releasing the dividend of digital economy development and reducing carbon emissions.
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IOT which means Internet of Things is a highly distributed network of smart devices. Every smart device is embedded with an electronic sensors, softwares and other technologies. The word “Things” means the one who is able to connect as well as exchange the data with other devices and systems over an internet, which are embedded with sensors, softwares and other technologies. Now a days air pollution is increasing day by day. The factors which produce air pollution are industrialization, increase in population, urbanization and so on. As the air pollution is increasing rapidly, it has become a major concern to work on. So we have tried to design and implement this project to detect the amount of gases present in air to reduce air pollution.
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This paper considers the issue of the search and rescue operation of humans after natural or man-made disasters. This problem arises after several calamities, such as earthquakes, hurricanes, and explosions. It usually takes hours to locate the survivors in the debris. In most cases, it is dangerous for the rescue workers to visit and explore the whole area by themselves. Hence, there is a need for speeding up the whole process of locating survivors accurately and with less damage to human life. To tackle this challenge, we present a scalable solution. We plan to introduce the usage of robots for the initial exploration of the calamity site. The robots will explore the site and identify the location of human survivors by examining the video feed (with audio) captured by them. They will then stream the detected location of the survivor to a centralized cloud server. It will also monitor the associated air quality of the selected area to determine whether it is safe for rescue workers to enter the region or not. The human detection model for images that we have used has a mAP (mean average precision) of 70.2%. The proposed approach uses a speech detection technique which has an F1 score of 0.9186 and the overall accuracy of the architecture is 95.83%. To improve the detection accuracy, we have combined audio detection and image detection techniques.
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Breathing poor-quality air is a global threat at the same level as unhealthy diets or tobacco smoking, so the availability of affordable instrument for the measurement of air pollutant levels is highly relevant for human and environmental protection. We developed an air quality monitoring platform that comprises a wearable device embedding low-cost metal oxide semiconductor (MOS) gas sensors, a PM sensor, and a smartphone for collecting the data using Bluetooth Low Energy (BLE) communication. Our own developed app displays information about the air surrounding the user and sends the gathered geolocalized data to a cloud, where the users can map the air quality levels measured in the network. The resulting device is small-sized, light-weighted, compact, and belt-worn, with a user-friendly interface and a low cost. The data collected by the sensor array are validated in two experimental setups, first in laboratory-controlled conditions and then against referential pollutant concentrations measured by standard instruments in an outdoor environment. The performance of our air quality platform was tested in a field testing campaign in Barcelona with six moving devices acting as wireless sensor nodes. Devices were trained by means of machine learning algorithms to differentiate between air quality index (AQI) referential concentration values (97% success in the laboratory, 82.3% success in the field). Humidity correction was applied to all data.
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Seeing the degrading quality of air over the past few decades that have not only caused environmental problem but also biological problems with plants and animals alike. Over the years the air which was once fresh and clean has now become polluted. The exponential growth of industries combining with the amassing of toxic fume emissions has become a primary contributor for air pollution. Moreover, these fumes are the main reason behind numerous respiratory and medical problems. Furthermore, the rapidly increasing human activities like the burning of fossil fuels; deforestation is the major cause of air pollution. The proposed system seeks to implement Internet Of Thing (IOT) by gathering the amount of harmful pollutants present in the air such as CO2, SO2, NOX and such along with various other parameters such as temperature, pressure, humidity, altitude, geolocation and pushing them to a cloud platform via a microcontroller .The experimental results provide a greatly accurate readings with a really low least count.. Moreover logging the data into a cloud platform makes the process of visualizing data very seamless and easy in comparison to other methodologies present today. In addition to this, there is also a mobile dashboard so as to visualize and monitor the recorded data in near real time.
Chapter
Instead of indoor air pollution, outdoor air pollution usually gets more attention even though the level of indoor air pollution is much higher than outside air pollution and most individuals spend 70 to 80% of their lives in buildings with tight air control. Due to poor air quality, more than 6 million people die every year, which leads to significant financial loss due to a decrease in the productivity of employees, increased expenses of the healthcare system, and material damage. In indoor pollution many factors are involved, such as biological pollutants, particulate matter, and over almost 400 various inorganic and organic compounds, whose values are linked with various indoor and outdoor factors. It is not always feasible technically to prevent different pollutants, so there is a great requirement for implementing active and cost-effective reductions. Chemical and physical technologies haven't yet found a way to get rid of every pollutant in the home at the same time. This issue involves the employment of sequence-based technologies that require higher capital and operational expenditures. Indoor environments still restrict the efficacy of classic physical-chemical technologies due to a lack of concentrations, variation, and predictability. A catalyst is used in conjunction with several hybrid processes, including photolysis, plasma, and absorption, to reduce the amount of volatile organic compounds (VOCs) in the air. The mechanism of VOCs oxidation by various catalysts is elucidated by using different principles, e.g., Langmuir–Hinshelwood and Eley–Rideal mechanisms. Biotechnologies have evolved here as cost-effective, sustainable platforms that are able to meet these constraints based on plants, bacteria, fungus, and microalgae's biocatalytic activity. Biological filtration systems may enhance buildings’ energy efficiency while offering extra aesthetic and psychological advantages. In this chapter, in addition to recent progress in physical-chemical and biological technology for indoor pollutant reduction, a comprehensive assessment of indoor air pollution issues and methods for prevention are presented.
Chapter
Smart cities provide more benefits in terms of environment and increase the quality of life, efficiency in projects regarding smart cities, and solving urban problems (Benevolo, 2016). Internet of things (IoT), cloud computing, and big data collective intelligence would be three key elements to complete and perfect smart cities. Hence, with those technologies, smart cities would bring positive effects to our society. This chapter would illustrate the details of IoT, cloud computing and big data collection intelligence of smart cities.
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The rapid urbanization process in the last century has deeply changed the way we live and interact with each other. As most people now live in urban areas, cities are experiencing growing demands for more efficient and sustainable public services that may improve the perceived quality of life, specially with the anticipated impacts of climatic changes. In this already complex scenario with increasingly overcrowded urban areas, different types of emergency situations may happen anywhere and anytime, with unpredictable costs in human lives and economic losses. In order to cope with unexpected and potentially dangerous emergencies, smart cities initiatives have been developed in different cities, addressing multiple aspects of emergencies detection, alerting, and mitigation. In this context, this article surveys recent smart city solutions for crisis management, proposing definitions for emergencies-oriented systems and classifying them according to the employed technologies and provided services. Additionally, recent developments in the domains of Internet of Things, Artificial Intelligence and Big Data are also highlighted when associated to the management of urban emergencies, potentially paving the way for new developments while classifying and organizing them according to different criteria. Finally, open research challenges will be identified, indicating promising trends and research directions for the coming years.
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Mobile Crowdsourcing (MCS) is an arising field of interest and a trending topic in the domain of ubiquitous computing. It has been recently drawing substantial attention from the smart cities and urban computing communities because of its infrastructure-less and flexibility. MCS is usually managed by a cloud platform that enables a task requester to enlist the services of a large number of people, called workers, to perform tasks using their smart-phones, often in exchange for a reward. These requested tasks usually involve sensing services (e.g., temperature sensing, etc) meaning that workers are demanded to use the sensors equipped in their smart devices to complete a task.Because the built-in cameras are becoming the most common way for visual logging techniques and sensing in our daily lives, we propose, in this thesis, a photo-based MCS framework for event reporting. The proposed framework allows task requesters, aka, eventreport requesters, to recruit task workers,aka, event reporters, to take photos of on goingevents. We design and propose a full Event Reporting architecture in which, we solvethe MCS recruitment problem using different strategies, mainly Spatial MCS (SMCS) and Collaborative MCS (CMCS) strategies, in the presence of multiple events and event re-porters. Consequently, our proposed framework is not only, MCS compatible, but also SMCS and CMCS compatible. The photo collection is performed in a distributed way in which a large number of contributors upload photos whenever and wherever it is suitable.This inevitably leads to evolving picture streams, which possibly contain misleading and redundant information that affect the task result. To this end, once submissions are received and before forwarding final responses to the event requesters, we propose to leverage the Event Reporting platform by monitoring and enhancing the data quality of the returned responses. The proposed solution mainly incorporates: i) strategic and generic recruitment algorithms for recruiting and scheduling suitable reporters to travel to a task location in a SMCS context, ii) team formation and recruitment approaches using Social IoT (SIoT) in a CMCS context, and iii) a data quality monitoring technique that eliminates false/erroneous submissions, ensures photo’s credibility, and reduces information redundancy to provide maximum event coverage. To this end, we propose in this thesis: i) two SMCS recruitment and scheduling algorithms. The first one is an optimal Mixed Integer Linear Program (MILP) while the second one is based on bipartite graph matching algorithms. ii) Three team formation and CMCS recruitment algorithms. The first one is an optimal Integer Linear Program (ILP), the second one is a meta-heuristic stochastic algorithm that uses the optimal stopping strategies, and the third one is an ILP mixed with community detection techniques. Finally, iii) a data quality monitoring technique that uses a deep learning approach to filter and remove inaccurate and false submissions followed by an A-tree shape hierarchical data structure to remove redundant information.Simulation results investigate the performances of the proposed approaches and com-pare their behavior to the existing ones in the literature. Results for the recruitment algorithms in both, SMCS and CMCS contexts, show that our proposed low complexity solutions achieve close performances to the optimal NP-hard approaches. The performance results of the data quality experiments using our proposed photo filtering and aggregation model show that our framework can efficiently reduce incorrect submissions and remove data redundancies.
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Low-cost air pollution sensors are emerging and increasingly being deployed in densely distributed wireless networks that provide more spatial resolution than is typical in traditional monitoring of ambient air quality. However, a low-cost option to measure black carbon (BC)-a major component of particulate matter pollution associated with adverse human health risks-is missing. This paper presents a new BC sensor designed to fill this gap, the Aerosol Black Carbon Detector (ABCD), which incorporates a compact weatherproof enclosure, solar-powered rechargeable battery, and cellular communication to enable long-term, remote operation. This paper also demonstrates a data processing methodology that reduces the ABCD's sensitivity to ambient temperature fluctuations, and therefore improves measurement performance in unconditioned operating environments (e.g., outdoors). A fleet of over 100 ABCDs was operated outdoors in collocation with a commercial BC instrument (Magee Scientific, Model AE33) housed inside a regulatory air quality monitoring station. The measurement performance of the 105 ABCDs is comparable to the AE33. The fleet-average precision and accuracy, expressed in terms of mean absolute percentage error, are 9.2 ± 0.8% (relative to the fleet average data) and 24.6 ± 0.9% (relative to the AE33 data), respectively (fleet-average ± 90% confidence interval).
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Background: Exposure to ambient air pollution increases morbidity and mortality, and is a leading contributor to global disease burden. We explored spatial and temporal trends in mortality and burden of disease attributable to ambient air pollution from 1990 to 2015 at global, regional, and country levels. Methods: We estimated global population-weighted mean concentrations of particle mass with aerodynamic diameter less than 2·5 μm (PM2·5) and ozone at an approximate 11 km × 11 km resolution with satellite-based estimates, chemical transport models, and ground-level measurements. Using integrated exposure-response functions for each cause of death, we estimated the relative risk of mortality from ischaemic heart disease, cerebrovascular disease, chronic obstructive pulmonary disease, lung cancer, and lower respiratory infections from epidemiological studies using non-linear exposure-response functions spanning the global range of exposure. Findings: Ambient PM2·5 was the fifth-ranking mortality risk factor in 2015. Exposure to PM2·5 caused 4·2 million (95% uncertainty interval [UI] 3·7 million to 4·8 million) deaths and 103·1 million (90·8 million 115·1 million) disability-adjusted life-years (DALYs) in 2015, representing 7·6% of total global deaths and 4·2% of global DALYs, 59% of these in east and south Asia. Deaths attributable to ambient PM2·5 increased from 3·5 million (95% UI 3·0 million to 4·0 million) in 1990 to 4·2 million (3·7 million to 4·8 million) in 2015. Exposure to ozone caused an additional 254 000 (95% UI 97 000-422 000) deaths and a loss of 4·1 million (1·6 million to 6·8 million) DALYs from chronic obstructive pulmonary disease in 2015. Interpretation: Ambient air pollution contributed substantially to the global burden of disease in 2015, which increased over the past 25 years, due to population ageing, changes in non-communicable disease rates, and increasing air pollution in low-income and middle-income countries. Modest reductions in burden will occur in the most polluted countries unless PM2·5 values are decreased substantially, but there is potential for substantial health benefits from exposure reduction. Funding: Bill & Melinda Gates Foundation and Health Effects Institute.
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The emergence of low-cost, user-friendly and very compact air pollution platforms enable observations at high spatial resolution in near-real-time and provide new opportunities to simultaneously enhance existing monitoring systems, as well as engage citizens in active environmental monitoring. This provides a whole new set of capabilities in the assessment of human exposure to air pollution. However, the data generated by these platforms are often of questionable quality. We have conducted an exhaustive evaluation of 24 identical units of a commercial low-cost sensor platform against CEN (European Standardization Organization) reference analyzers, evaluating their measurement capability over time and a range of environmental conditions. Our results show that their performance varies spatially and temporally, as it depends on the atmospheric composition and the meteorological conditions. Our results show that the performance varies from unit to unit, which makes it necessary to examine the data quality of each node before its use. In general, guidance is lacking on how to test such sensor nodes and ensure adequate performance prior to marketing these platforms. We have implemented and tested diverse metrics in order to assess if the sensor can be employed for applications that require high accuracy (i.e., to meet the Data Quality Objectives defined in air quality legislation, epidemiological studies) or lower accuracy (i.e., to represent the pollution level on a coarse scale, for purposes such as awareness raising). Data quality is a pertinent concern, especially in citizen science applications, where citizens are collecting and interpreting the data. In general, while low-cost platforms present low accuracy for regulatory or health purposes they can provide relative and aggregated information about the observed air quality.
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In this work the performances of several field calibration methods for low-cost sensors, including linear/multi linear regression and supervised learning techniques, are compared. A cluster of either metal oxide or electrochemical sensors for nitrogen monoxide and carbon monoxide together with miniaturized infra-red carbon dioxide sensors was operated. Calibration was carried out during the two first weeks of evaluation against reference measurements. The accuracy of each regression method was evaluated on a five months field experiment at a semi-rural site using different indicators and techniques: orthogonal regression, target diagram, measurement uncertainty and drifts over time of sensor predictions. In addition to the analyses for ozone and nitrogen oxide already published in Part A [1], this work assessed if carbon monoxide sensors can reach the Data Quality Objective (DQOs) of 25% of uncertainty set in the European Air Quality Directive for indicative methods. As for ozone and nitrogen oxide, it was found for NO, CO and CO2 that the best agreement between sensors and reference measurements was observed for supervised learning techniques compared to linear and multilinear regression.
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Due to advances in technology there is trend in miniaturization of devices which demands to develop low cost sensor, low power and rugged devices .In view of this Wireless Sensor Networks (WSN) have gained importance in various applications: Business, Agricultural, Domestic, Industries, Traffic control, and environmental monitoring. The paper presents Wireless sensor network system used to monitor and control the air quality in Solapur city. Environmental air pollution monitoring system that measures, SPM (Suspended Particulate Matter), NOx, and SO2 are proposed. The traditional air quality monitoring system, controlled by the Pollution Control Department, is extremely expensive. Analytical measuring equipment is costly, time and power consuming, and can seldom be used for air quality reporting in real time. Attempt has been made to develop monitoring system using commercially available standard pollutant gas sensors and CC2530ZDK board that uses 2.4 GHz IEEE 802.15.4 standard, high performance low power 8051 core, which will serve as a node in a Wireless Sensor Network. A specific program made with LabVIEW is created to configure and supervise the operation and the sensing measurements on the network used.
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This paper presents an ongoing research project proposing a DIY environmental sensing approach that empowers citizens to reinvigorate their awareness of, and concern for, pollution. To test the thesis, PAIR, a prototype with interchangeable gas sensor, was developed. Our main focus was on sensing environment on-the-go to provide users with immediate feedback. Finally, we identify the main benefits amateur data collection and participatory sensing represent for urban dwellers. (C) Selection and peer-review under responsibility of FET11 conference organizers and published by Elsevier B.V.
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Many technical communities are vigorously pursuing research topics that contribute to the Internet of Things (IoT). Nowadays, as sensing, actuation, communication, and control become even more sophisticated and ubiquitous, there is a significant overlap in these communities, sometimes from slightly different perspectives. More cooperation between communities is encouraged. To provide a basis for discussing open research problems in IoT, a vision for how IoT could change the world in the distant future is first presented. Then, eight key research topics are enumerated and research problems within these topics are discussed.
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With an increasing number of rich embedded sensors, like accelerometer and GPS, smartphone becomes a pervasive people-centric sensing platform for inferring user's daily activities and social contexts. Alternatively, wireless sensor network offers a comprehensive platform for capturing the surrounding environmental information using mobile sensing nodes, e.g., the OpenSense project [2] in Switzerland deploying air quality sensors like CO on public transports like buses and trams. The two sensing platforms are typically isolated from each other. In this paper, we build ExposureSense, a rich mobile participatory sensing infrastructure that integrates the two independent sensing paradigms. ExposureSense is able to monitor people's daily activities as well to compute a reasonable estimation of pollution exposure in their daily life. Besides using external sensor networks, ExposureSense also supports pluggable sensors (e.g., O3) to further enrich air quality data using mobile participatory sensing with smartphones.
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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.
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Although smartphone applications represent the most typical data consumer tool from the citizen perspective in environmental applications, they can also be used for in-situ data collection and production in varied scenarios, such as geological sciences and biodiversity. The use of standard protocols, such as SWE, to exchange information between smartphones and sensor infrastructures brings benefits such as interoperability and scalability, but their reliance on XML is a potential problem when large volumes of data are transferred, due to limited bandwidth and processing capabilities on mobile phones. In this article we present a performance analysis about the use of SWE standards in smartphone applications to consume and produce environmental sensor data, analysing to what extent the performance problems related to XML can be alleviated by using alternative uncompressed and compressed formats.
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Many of the initial developments towards the Internet of Things have focused on the combination of Auto-ID and networked infrastructures in businessto- business logistics and product life cycle applications. However, a future Internet of Things can provide a broader vision and also enable everyone to access and contribute rich information about things and locations. The success of social networks to share experience and personalised insights shows also great potential for integration with business-centric applications. The integration and interoperability with mainstream business software platforms can be enhanced and extended by real-time analytics, business intelligence and agent-based autonomous services. Information sharing may be rewarded through incentives, thus transforming the Internet of Things from a cost-focused experiment to a revenue-generating infrastructure to enable trading of enriched information and accelerate business innovation. Mash-ups and end-user programming will enable people to contribute to the Internet of Things with data, presentation and functionality. Things-generated physical world content and events from Auto-ID, sensors, actuators or meshed networks will be aggregated and combined with information from virtual worlds, such as business databases and Web 2.0 applications, and processed based on new business intelligence concepts. Direct action on the physical world will be supported through machine-interfaces and introduction of agile strategies. This chapter aims to provide a concept for a future architecture of the Internet of Things, including a definition, a review of developments, a list of key requirements and a technical design for possible implementation of the future Internet of Things. As open issues, the evaluation of usability by stakeholders in user-centric as well as business-centric scenarios is discussed and the need for quantifying costs and benefits for businesses, consumers, society and the environment is emphasised. Finally, guidelines are derived, for use by researchers as well as practitioners.
Article
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In the recent decade, several technology trends have influenced the field of geosciences in significant ways. The first trend is the more readily available technology of ubiquitous wireless communication networks and progress in the development of low-power, short-range radio-based communication networks, the miniaturization of computing and storage platforms as well as the development of novel microsensors and sensor materials. All three trends have changed the type of dynamic environmental phenomena that can be detected, monitored and reacted to. Another important aspect is the real-time data delivery of novel platforms today. In this paper, I will survey the field of geosensor networks, and mainly focus on the technology of small-scale geosensor networks, example applications and their feasibility and lessons learnt as well as the current research questions posed by using this technology today. Furthermore, my objective is to investigate how this technology can be embedded in the current landscape of intelligent sensor platforms in the geosciences and identify its place and purpose.
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Background: Male fertility is progressively declining in many developed countries, but the relationship between male infertility and environmental factors is still unclear. Objectives: To assess the influence of environmental temperature and air pollution on semen parameters, using a big-data approach. Methods: A big data analysis of parameters related to 5131 men, living in a province of Northern Italy and undergoing semen analyses between January 2010 and March 2016 was performed. Ambient temperature was recorded on the day of analysis and the 90 days prior to the analysis and the average value of particulate matter (PM) and NO2 in the year of the test. All data were acquired by geocoding patients residential address. A data warehouse containing 990,904,591 data was generated and analysed by multiple regressions. Results: 5573 semen analyses were collected. Both maximum and minimum temperatures registered on the day of collection were inversely related to total sperm number (p < .001), non-progressive motility (NPrM) (p < .005) and normal forms (p < .001). Results were confirmed considering temperature in the 30 and 60 days before collection, but not in the 90 days before collection. Total sperm number was lower in summer/autumn (p < .001) and was inversely related with daylight duration (p < .001). PM10 and PM2.5 were inversely related to PrM (p < .001 and p < .005) and abnormal forms (p < .001). Conclusions: This is the first evaluation of the relationship between male fertility-related parameters and environment using a big-data approach. A seasonal change in semen parameters was found, with a fluctuation related to both temperature and daylight duration. A negative correlation between air pollution and semen quality is suggested. Such seasonal and environmental associations should be considered when assessing changes of male fertility-related parameters over time.
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The quality of air is a major concern in modern cities as pollutants have been demonstrated to have significant impact on human health. Networks of fixed monitoring stations have been deployed in urban areas to provide authorities with data to define and enforce dynamically policies to reduce pollutants, for instance by issuing traffic regulation measures. However, fixed networks require careful placement of monitoring stations to be effective. Moreover, changes in urban arrangement, activities, or regulations may affect considerably the monitoring model, especially when budget constraints prevent from relocating stations or adding new ones to the network. In this chapter we discuss a different approach to environmental monitoring through mobile monitoring devices implementing a Vehicular Sensor Network (VSN) to be deployed on the public transport bus fleet of Palermo.
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Accuracy of any software release to the market depends on how efficiently it has been debugged. Debugging is a systematic procedure, used to identify and figure out the cause of defects or any anomaly that the software has and make the software behave as expected. The issues generated by the customer of any company are logged into a database, wherein issues are picked up selected, solved and reverted back to the customers. After solving an issue, it may happen that the issue affects other components which results into a greater number of bugs. The resultant issues are called regression issues. The objective of this paper is to propose and implement a client-server, object-oriented, multiple plat form supporting frame work called RATS Framework which automates the process of regression and thereby helps debug engineers to solve time-consuming regression issues at a faster rate. It automates the process with the help of web-scrapping algorithm (W-S-A) that includes HTML/XML parsing to extract the needed content in the form of GUI-Web Objects, than using Network-Binary Search Algorithm (N/W-BS-A) and Change Finder Algorithm, a variant of Binary Search method, RATS finds out the nearest pass/fail driver build and change in the driver build that cause the new defect in the driver respectively. Because the RATS Framework does this at runtime, client-server approach has to be followed making use of Remote Identification and Installation-Algorithm. Hence RATS framework is a cost effective and time efficient approach for regression issues. The present article has the discussion of few of the patents relevant to automation testing software.
Conference Paper
The strength of sensor networks is quite vast when it is applied for collecting physical data in real time and storing for further analysis. The impossible measurements by conventional methods have now become possible by using this technology. Air quality which is one of the most important factors for the sustainable development and is one of the challenging areas researchers dealt with long time. The major source of environment pollution happens to be Vehicular Pollution. The high influx of vehicles to urban areas, and conventional pollution control measures used by concerned agencies has led to the drastic increase of air pollution. Here we address this problem by introducing VehNode, a WSN based Vehicular pollution monitoring platform which is capable of measuring different types of pollutant concentrations contained in smoke produced by the vehicle and reports the status automatically whenever required to the concerned agencies. We assure the existence of Wireless Sensor Network platform for automobile pollution control focusing on an easy accessibility of real time data via the Web by following the Web of Things approach. This will form a basis to connect each vehicle as an entity for a web of things infrastructure enabling the direct interaction with existing web applications of the concerned agencies. The real time data will be available to three main groups of users: Owner of the Vehicle, Traffic department and national environmental agencies.
Book
Pervasive computing technologies have seen significant advances in the last few years. This has resulted in design and development of sensors, wearable technologies, smart places and homes, and wireless and mobile networks. In this chapter, we first discuss current and emerging trends in pervasive computing and then present how pervasive computing can lead to pervasive healthcare. More specifically, examples of pervasive health monitoring, mobile telemedicine, intelligent emergency management service, health-aware mobile device, pervasive access to health information, pervasive life style management, and medical inventory management system are presented. We also identify various requirements of pervasive healthcare and present open issues and challenges to spur more research in this area.
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