There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

WHO Guidelines for Indoor Air Quality: Selected pollutants.

Occupational health can be strongly influenced by the indoor environment as people spend 90% of their time indoors. Although indoor air quality (IAQ) is not typically monitored, IAQ parameters could be in many instances very different from those defined as healthy values. Particulate matter (PM), a complex mixture of solid and liquid particles of organic and inorganic substances suspended in the air, is considered the pollutant that affects more people. The most health-damaging particles are the ≤PM10 (diameter of 10 microns or less), which can penetrate and lodge deep inside the lungs, contributing to the risk of developing cardiovascular and respiratory diseases, as well as of lung cancer. This paper presents an Internet of Things (IoT) system for real-time PM monitoring named iDust. This system is based on a WEMOS D1 mini microcontroller and a PMS5003 PM sensor that incorporates scattering principle to measure the value of particles suspended in the air (PM10, PM2.5, and PM1.0). Through a Web dashboard for data visualization and remote notifications, the building manager can plan interventions for enhanced IAQ and ambient assisted living (AAL). Compared to other solutions the iDust is based on open-source technologies, providing a total Wi-Fi system, with several advantages such as its modularity, scalability, low cost, and easy installation. The results obtained are very promising, representing a meaningful tool on the contribution to IAQ and occupational health.

https://www.mdpi.com/1660-4601/15/4/821/pdf

A System Based on the Internet of Things for Real-Time Particle Monitoring in Buildings

Data are essential in all areas of geophysics. They are used to better understand and manage systems, either directly or via models. Given the complexity and spatiotemporal variability of geophysical systems (e.g., precipitation), a lack of sufficient data is a perennial problem, which is exacerbated by various drivers, such as climate change and urbanization. In recent years, crowdsourcing has become increasingly prominent as a means of supplementing data obtained from more traditional sources, particularly due to its relatively low implementation cost and ability to increase the spatial and/or temporal resolution of data significantly. Given the proliferation of different crowdsourcing methods in geophysics and the promise they have shown, it is timely to assess the state of the art in this field, to identify potential issues and map out a way forward. In this paper, crowdsourcing-based data acquisition methods that have been used in seven domains of geophysics, including weather, precipitation, air pollution, geography, ecology, surface water, and natural hazard management, are discussed based on a review of 162 papers. In addition, a novel framework for categorizing these methods is introduced and applied to the methods used in the seven domains of geophysics considered in this review. This paper also features a review of 93 papers dealing with issues that are common to data acquisition methods in different domains of geophysics, including the management of crowdsourcing projects, data quality, data processing, and data privacy. In each of these areas, the current status is discussed and challenges and future directions are outlined.

https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2018RG000616

Crowdsourcing Methods for Data Collection in Geophysics : State of the Art, Issues, and Future Directions

Road accidents are a leading cause of death and disability among youth. Contemporary research on accident detection systems is focused on either decreasing the reporting time or improving the accuracy of accident detection. Internet-of-Things (IoT) platforms have been utilized considerably in recent times to reduce the time required for rescue after an accident. This work presents an IoT-based automotive accident detection and classification (ADC) system, which uses the fusion of smartphone’s built-in and connected sensors not only to detect but also to report the type of accident. This novel technique improves the rescue efficacy of various emergency services, such as emergency medical services (EMSs), fire stations, towing services, etc., as knowledge about the type of accident is extremely valuable in planning and executing rescue and relief operations. The emergency assistance providers can better equip themselves according to the situation after making an inference about the injuries sustained by the victims and the damage to the vehicle. In this work, three machine learning models based on Naive Bayes (NB), Gaussian mixture model (GMM), and decision tree (DT) techniques are compared to identify the best ADC model. Five physical parameters related to vehicle movement, i.e., speed, absolute linear acceleration (ALA), change-in-altitude, pitch, and roll, have been used to train and test each candidate ADC model to identify the correct class of accident among collision, rollover, falloff, and no accident. NB-based ADC model is found to be highly accurate with 0.95 mean F1-score.

An IoT-Based Vehicle Accident Detection and Classification System Using Sensor Fusion

As humans generally spend most of their time indoors, having good indoor air quality and its real time information is critical for maintaining human health and productivity. According to EPA, indoor air even in centrally air-conditioned buildings is several times more polluted than outdoor air, primarily due to change in occupancy pattern, poor, old or ill maintained ventilation systems, dust, and presence of volatile organic compounds. Hence, it is necessary for humans to pervasively and ubiquitously measure air quality information in real time, specially whose health is sensitive to poor indoor air. In this work, we propose a novel context aware Android smart phone based mobile adhoc sensing system that senses various data from indoor environment around the user and analyses it in real time, thereby helping to maintain good health and better work productivity.

SmartVent: A Context Aware IoT System to Measure Indoor Air Quality and Ventilation Rate

Vehicle Indoor Air Quality (VIAQ) is a very important parameter that we often tend to ignore in our daily driving. VIAQ can worsen due to build-up of carbon dioxide (CO2), carbon monoxide (CO) and other harmful gases emitted by vehicles. Poor VIAQ can lead to short term health conditions like drowsiness, breathlessness, discomfort and headache. Drowsiness while driving is a predominant health condition that leads to large number of accidents and deaths all over the world. Standard equipment of air intake and recirculation in both low end and high end cars do not completely support pervasive maintenance of VIAQ. Due to unavailability of miniaturized sensors, a general purpose solution to this problem was rarely studied and addressed. In this work, we propose a novel mobile sensing system to monitor and understand change in VIAQ. Our mobile sensing system is general purpose, built by using an Android Smartphone and a mobile sensor, is independent of car age and model, and monitors VIAQ at all times.

Real time in-vehicle air quality monitoring using mobile sensing

As the proportion of time spent by humans in indoor environment increases, it becomes challenging to maintain good air quality for healthy and productive life. The need to develop a context aware, reliable system capable of providing real time information and alerts on indoor air quality is addressed in this article. The proposed Internet-of-Things (IoT) system serves to collect data, predict ventilation states, and provide alerts and recommendations to the end user. A novel method for determination of ventilation states using three indoor pollutants PM2.5, PM10, and CO is proposed. Multilevel logistic regression is first used to define indoor ventilation states using ventilation rate which is calculated with the help of indoor CO2 concentration. $K$ -NN classification technique then predicts indoor ventilation state with the help of three input attributes, PM2.5, PM10, and CO. Context-aware information about indoor environment and current ventilation state is conveyed to the end-user in form of an alert, through a smartphone application. The system is found to determine the poor ventilation state with accuracy, precision, recall and F1 score values of 94.34%, 0.91, 0.88, and 0.89, respectively.

Context-Aware Monitoring and Control of Ventilation Rate in Indoor Environments Using Internet of Things

Millions of people get injured, disabled or die in automotive accidents each year. Knowledge about the type of road accident is invaluable to the emergency medical services providers for optimal planning and execution of the rescue operation. An IoT based system has been developed in this work to report the occurrence, location as well as the type of road accident. The system uses in-built sensors of passenger smartphone to detect and classify the accident as head-on collision, rollover or fall-off. The accuracy of the proposed system, which uses Naive Bayes classifier for classification, has been evaluated using precision, recall, F1 score and ROC curve.

Divya Lohani

Papers

An IoT-Based Vehicle Accident Detection and Classification System Using Sensor Fusion

SmartVent: A Context Aware IoT System to Measure Indoor Air Quality and Ventilation Rate

Real time in-vehicle air quality monitoring using mobile sensing

Context-Aware Monitoring and Control of Ventilation Rate in Indoor Environments Using Internet of Things

Modeling IoT Enabled Automotive System for Accident Detection and Classification