Massive Internet of Things (IoT) is foretold to introduce surfeit of applications for a fully connected world. The increasingly degrading quality of air in several regions of the biosphere can be a reliable impact of human difficulties of increasing globalization and urbanization. Air pollution levels in many cities exceed legal and World Health Organization (WHO) limits for particulate matter and gaseous pollutants which can be found in concentrations that are hazardous to health. Regular acquaintance to high pollution intensities results in increase of humans affected from respiratory disorders such as asthma, chronic obstructive lung disease and increased mortality. In most of the cities, there is no chance for peoples to apprehend the levels of pollution they are undergoing in their day-to-day lives which is necessary to reduce their risks from poor air quality conditions. The quality of air can be measured by using a parameter named Air quality index. The Key pollutants to calculate Air quality index are particulate matter (PM2.5 and PM10), Ozone (O3), Nitrogen Dioxide (NO2), Sulphur Dioxide (SO2) and Carbon Monoxide (CO). This work deliberates the implementation of cloud based IoT system for air quality monitoring in which the sensors are used to calculate CO, PM2.5 and PM10, O3, SO2 and NOx pollution level with environmental condition like temperature and humidity. The obtained information can be updated in cloud platform using Lora nodes and Lora Gateway. The web-based application developed has a Google map API where the pollution status can be frequently updated. With the collected time series samples, the prediction analysis was done for PM with neural network Multi-Layer perceptron and support vector machine regression (SVMR) learning algorithm. This can helps a person to travel to any other places by automatically rerouting conditions in a pollution free environment.

https://iopscience.iop.org/article/10.1088/1757-899X/955/1/012005/pdf

IoT enabled Environmental Air Pollution Monitoring and Rerouting system using Machine learning algorithms

Air Pollution (AP) is one of the serious and major environmental problem worldwide. Many researchers have drawn attention and have focused about these problems keeping in mind human health. Air quality prediction information is one of the better ways through which people can be informed to be more vigilant about serious health issues and protect human health caused by air pollution. In many metropolitan cities air pollution is a major challenging environmental issue. To analyze the present traffic condition of the city, local authorities can be enabled by real time monitoring of pollution data which makes appropriate decisions. Hence an early system is required for monitoring and calculating the level of AP using Air Quality (AQ) which is essential for predicting exactly the pollutant concentrations. The prediction of AQ can be improved by deploying Internet of Things (IoT) based sensor which are considerably changing the prediction of AQ dynamically. The prediction of AP discussed and estimated using many existing techniques are very expensive and have very low accuracy. The technological advancement of Machine Learning (ML) algorithm can be very fast increasing and searching almost all fields and applications whereas AP prediction has not prohibited from those fields. This paper describes about various studies of ML algorithm relating to AP prediction and monitoring based on the IoT sensor data in the context of various cities. This paper also summaries real time and historical data based on the AQ prediction models tools and techniques and describes about recent research methodologies merits and demerits of AQ prediction, along with the challenges based on real time monitoring and prediction of AQ.

Air Quality Prediction and Monitoring using Machine Learning Algorithm based IoT sensor- A researcher's perspective

Over a billion people around the world are disabled, among whom 253 million are visually impaired or blind, and this number is greatly increasing due to ageing, chronic diseases, and poor environments and health. Despite many proposals, the current devices and systems lack maturity and do not completely fulfill user requirements and satisfaction. Increased research activity in this field is required in order to encourage the development, commercialization, and widespread acceptance of low-cost and affordable assistive technologies for visual impairment and other disabilities. This paper proposes a novel approach using a LiDAR with a servo motor and an ultrasonic sensor to collect data and predict objects using deep learning for environment perception and navigation. We adopted this approach using a pair of smart glasses, called LidSonic V2.0, to enable the identification of obstacles for the visually impaired. The LidSonic system consists of an Arduino Uno edge computing device integrated into the smart glasses and a smartphone app that transmits data via Bluetooth. Arduino gathers data, operates the sensors on the smart glasses, detects obstacles using simple data processing, and provides buzzer feedback to visually impaired users. The smartphone application collects data from Arduino, detects and classifies items in the spatial environment, and gives spoken feedback to the user on the detected objects. In comparison to image-processing-based glasses, LidSonic uses far less processing time and energy to classify obstacles using simple LiDAR data, according to several integer measurements. We comprehensively describe the proposed system’s hardware and software design, having constructed their prototype implementations and tested them in real-world environments. Using the open platforms, WEKA and TensorFlow, the entire LidSonic system is built with affordable off-the-shelf sensors and a microcontroller board costing less than USD 80. Essentially, we provide designs of an inexpensive, miniature green device that can be built into, or mounted on, any pair of glasses or even a wheelchair to help the visually impaired. Our approach enables faster inference and decision-making using relatively low energy with smaller data sizes, as well as faster communications for edge, fog, and cloud computing.

/pdf/lidsonic-v2-0-a-lidar-and-deep-learning-based-green-10orvdqa.pdf

LidSonic V2.0: A LiDAR and Deep-Learning-Based Green Assistive Edge Device to Enhance Mobility for the Visually Impaired

Smart wearable technologies such as fitness trackers are creating many new opportunities to improve the quality of life for everyone. It is usually impossible for visually impaired people to orientate themselves in large spaces and navigate an unfamiliar area without external assistance. The design space for assistive technologies for the visually impaired is complex, involving many design parameters including reliability, transparent object detection, handsfree operations, high-speed real-time operations, low battery usage, low computation and memory requirements, ensuring that it is lightweight, and price affordability. State-of-the-art visually impaired devices lack maturity, and they do not fully meet user satisfaction, thus more effort is required to bring innovation to this field. In this work, we develop a pair of smart glasses called LidSonic that uses machine learning, LiDAR, and ultrasonic sensors to identify obstacles. The LidSonic system comprises an Arduino Uno device located in the smart glasses and a smartphone app that communicates data using Bluetooth. Arduino collects data, manages the sensors on smart glasses, detects objects using simple data processing, and provides buzzer warnings to visually impaired users. The smartphone app receives data from Arduino, detects and identifies objects in the spatial environment, and provides verbal feedback about the object to the user. Compared to image processing-based glasses, LidSonic requires much less processing time and energy to classify objects using simple LiDAR data containing 45-integer readings. We provide a detailed description of the system hardware and software design, and its evaluation using nine machine learning algorithms. The data for the training and validation of machine learning models are collected from real spatial environments. We developed the complete LidSonic system using off-the-shelf inexpensive sensors and a microcontroller board costing less than USD 80. The intention is to provide a design of an inexpensive, miniature, green device that can be built into, or mounted on, any pair of glasses or even a wheelchair to help the visually impaired. This work is expected to open new directions for smart glasses design using open software tools and off-the-shelf hardware.

/pdf/lidsonic-for-visually-impaired-green-machine-learning-based-17ar7tk6.pdf

LidSonic for Visually Impaired: Green Machine Learning-Based Assistive Smart Glasses with Smart App and Arduino

According to the World Health Organization, about 15% of the world’s population has some form of disability. Assistive Technology, in this context, contributes directly to the overcoming of difficulties encountered by people with disabilities in their daily lives, allowing them to receive education and become part of the labor market and society in a worthy manner. Assistive Technology has made great advances in its integration with Artificial Intelligence of Things (AIoT) devices. AIoT processes and analyzes the large amount of data generated by Internet of Things (IoT) devices and applies Artificial Intelligence models, specifically, machine learning, to discover patterns for generating insights and assisting in decision making. Based on a systematic literature review, this article aims to identify the machine-learning models used across different research on Artificial Intelligence of Things applied to Assistive Technology. The survey of the topics approached in this article also highlights the context of such research, their application, the IoT devices used, and gaps and opportunities for further development. The survey results show that 50% of the analyzed research address visual impairment, and, for this reason, most of the topics cover issues related to computational vision. Portable devices, wearables, and smartphones constitute the majority of IoT devices. Deep neural networks represent 81% of the machine-learning models applied in the reviewed research.

/pdf/artificial-intelligence-of-things-applied-to-assistive-igyl16em.pdf

Artificial Intelligence of Things Applied to Assistive Technology: A Systematic Literature Review

Air pollution is a growing threat towards society and various measures are being taken recently to control it. The problem of concern which remains is the efficient prediction of air pollution to work in the right direction for reducing the same. Since the AQI follows a periodic pattern, deep learning models can be used to effectively predict the future AQI values. LSTM being a prominent time series forecasting model can be integrated with a separate DNN model to effectively add the impact of weather, temperature and other factors that can affect the future AQI values. The paper also explores the impact of having a parallel DNN to the LSTM cell instead of using the cell alone.

Air Quality Index forecasting using parallel Dense Neural Network and LSTM cell

For people who are visually impaired, navigation is a challenge they encounter on a daily basis. For the same, use of walking sticks have become a common practice. Although, there are a lot of limitation of just relying on a blind stick. A more suitable method will alert the user about the nature of the obstacle and should also be of assistance in guiding the user to their location. In this paper, we propose an architecture of an assistance system revolving around a shoe that employees IoT devices and sensors along with computer vision algorithms to provide functionalities like obstacle detection, avoidance and navigation. The method uses a smartphone based voice assistance and guides the user with appropriate haptic feedback calculated using various sensors and actuators.

Computer Vision and Iot Based Smart System for Visually Impaired People

Multi-label classification is a generalization of a multi-class classification problem where one entity can belong to more than one class from the class set. Recent works have proposed multiple methods of solving this problem that involves both statistical and deep learning methods. While methods exist for using deep learning models for this problem, most of them require the model to have a high dimension output vector and the property of inter-dependency of classes has not been explored. An ensemble of statistical models called the chain classifiers can be used to address these issues. This study explores methods of using neural network classifiers in the classifier chain model and tries to address some problems with such architecture while compare their performance on different types of data using different metrics with each other and with other well performing multi-label classification methods.

Multi-label classification with classifier chains of ANN models

Multi-Target Regression refers to the problem where a set of n independent variables are used to predict the values of k target variables where both k and n are greater than 1. Most methods provide the provision for a regression problem with multiple targets including decision tree regressors and artificial neural networks. However, these methods end up making an assumption that their is no inter-dependency among the target variables. In numerous problems, this assumption turns out to be false which can be notably seen with the variance inflation factor and co-relation of these variables. This consideration was addressed in both classification and regression using chain models, which although mostly competent, possess the issue of a uni-directional dependency. In this work, the significance of directionality problem has been discussed and is addressed by proposing an ensemble based methodology. The comparative analysis of the proposed model is studied against the pre-existing models to explore the improvements in the performance of the model.

Vishwa Mohan Singh

Papers

Air Quality Index forecasting using parallel Dense Neural Network and LSTM cell

Computer Vision and Iot Based Smart System for Visually Impaired People

Multi-label classification with classifier chains of ANN models

Bi-Directional Chains of Neural Nets for Multi-Target Regression