Abstract

Remote labs allow students from anywhere in the world to access and conduct experiments without the need to physically be present in a lab at anytime. This is extremely important for students with little to no access to proper science labs because of a lack of infrastructure or a pandemic. There is a potential for scaling up the process so that queue delay can be removed and people are able to access dedicated experiment setups. This paper proposes a way for cost-effective scaling of Remote Labs by miniaturization of setups, image processing techniques, and partial streaming (using single camera to stream multiple experiments). For this, a use-case of the Vanishing Glass Rods experiment is considered. An end-to-end remote lab is created, including hardware setups and a dashboard to demonstrate the efficacy of the proposed approach in comparison to the existing approach of using lab-scale setups and one camera per experiment.

Abstract

Carbon monoxide (CO) and Nitrogen dioxide (NO 2 ) are major air pollutants that have the potential to affect human health adversely. There is a lack of useful information regarding the spatial distribution and temporal variability of CO and NO 2 emissions in major metropolitan areas. The primary goal of this research is to provide a geospatial data methodology for detecting emission spikes of CO and NO 2 in polluted urban environments employing portable low-cost sensors. We propose that ephemeral identification of harmful gas concentrations can be achieved using different IoT device types mounted on a mobile platform. We propose that persistent identification of the CO and NO 2 emission spikes can be attained by driving through the city on different days. We applied this approach to Hyderabad, India, by fixing a mobile platform on a street car. We corrected the IoT device measurement errors by calibrating

Abstract

ABSTRACT Particulate Matter (PM) is a major air pollutant that has the potential for adversely affecting human health. Actionable data on the spatial distribution of temporal variability of PM2.5 emission hot spots in large cities are sparse. The main objective of this research is to provide a protocol for using search agents to hunt for PM2.5 emission hot spots in urban environments. We propose short range identification of variability of harmful PM2.5 concentrations can be achieved using IoT devices mounted on a mobile platform. We propose that long range identification the PM2.5 emission hot spots can attained by searching through the city on different days. We applied this approach to Hyderabad, India by fixing a mobile platform on a street car. We corrected the IoT device measurement errors by calibrating the sensing component data against a reference instrument co-located on the mobile platform. We identified that random forest regression was the most suitable technique to reduce the variability between the IoT devices. The spatial variability of PM2.5 harmful emission hot spots at industrial settings and congested roads were identified. The temporal variability based on image processing shows a weak correlation between PM2.5 concentrations and number of vehicles, and PM2.5 and visibility. The Hyderabad PM2.5 emission hot spots findings demonstrate a clear need to inform people with heart and lung conditions when it is unhealthy to be outside; and when it is unhealthy for children and elderly people to be outside for prolonged periods. Our emission hunting approach can be applied to any mobile platform carried by people walking, cycling or by drones and robots in any city

Abstract

Integrating scalability, interoperability, and security has become crucial with the widespread adoption of Internet of Things (IoT)-enabled smart city solutions. In this context, the oneM2M provides promising technical specifications for an interoperable and secure IoT/M2M system. This paper focuses on the potential threats and their impact on oneM2M standardbased smart city deployments. Further, configurations for baseline security of the oneM2M open-source implementation, called eclipse OM2M, are presented. Recommendations are proposed based on actual on-ground tests conducted on OM2M-based smart city deployment of IIIT Hyderabad (IIIT-H) in India. The tests cover passive eavesdropping, performing replay attacks, brute force on credentials, denial of service, and analysis of access control policies. Index Terms—Eclipse OM2M, Internet of Things (IoT) security, IoT security standardisation, oneM2M, smart city

Abstract

Air pollution is a concern to the health of all living beings. It is essential to check on the quality of air in the surroundings. This article presents an IoT-based real-time air quality index (AQI) estimation technique using images and weather sensors on Indian rods. A mixture of image features, i.e., traffic density, visibility, and sensor features, i.e., temperature and humidity, were used to predict the AQI. Object detection and localization-based Deep Learning (DL) method along with image processing techniques were used to extract image features while an Machine Learning (ML) model was trained on those features to estimate the AQI. In order to conduct this experiment, a dataset containing 5048 images along with co-located AQI values across different seasons was collected by driving on the roads of Hyderabad city in India. The experimental results report an overall accuracy of 82% for AQI prediction. Index Terms—Air Quality, CNN, Edge computing, Machine Learning.

Abstract

In this paper, four low-cost CO2 sensors are evaluated for IoT-based indoor air pollution monitoring. Specifically, CO2 sensors SCD30, Prana Air, MHZ14, and T6713 are evaluated against a standard reference Aeroqual S-500 device. The experiment was carried out in an indoor environment inside one of the labs in IIIT Hyderabad, India. It is shown that calibration is needed for some of these low-cost devices locally even though the sensors may be factory calibrated. For calibration, simple and widely-used machine learning algorithms are employed such as linear regression, least absolute deviation, random forest, support vector regression, and Gaussian regression. The parameters considered to assess the performance of these sensors are coefficient of determination (R 2 ), coefficient of variability (Cv), and root mean square error (RMSE). After calibration with a reference sensor, it is observed that these low-cost sensors operate well. Index Terms—IoT, Determination coefficient, Low-cost CO2 sensor, Coefficient of variability, Root mean square error.

Abstract

This paper proposes an IoT and machine learning (ML)-based novel method to estimate the air quality index (AQI) using traffic data in real-time. With the help of particulate matter (PM) monitoring nodes deployed in fifteen locations with diverse traffic scenarios of Indian roads, and using digital map service providers, a rich traffic dataset with approximately 210,000 samples has been collected. Three different ML models, namely random forest (RF), support vector machine (SVM), and multilayer perceptron (MLP), are trained on this dataset to predict the AQI category into five levels. The experimental results show an accuracy of 82.60% with the F1-score of 83.67% on the complete dataset. Apart from this, ML models were also trained on individual node datasets, and the behavior of AQI levels was observed. Index Terms—AQI Estimation, Traffic Data, Machine Learning, IoT

Abstract

This paper considers a low-earth-orbit (LEO) satellite-based topology for an internet-of-things (IoT) network, where multiple IoT devices broadcast the information to all the visible satellites over a shared channel using slotted ALOHA. The satellites selectively decode-and-forward (DF) the information from the IoT devices over orthogonal channels to the ground station (GS), which does maximal ratio combining (MRC). For decoding at the satellites, capture and successive interference cancellation (SIC) schemes are considered. For the considered topology, the closed-form expressions are derived for the end-to-end outage probability (OP) for an arbitrary number of IoT devices and satellites in the capture model and for the two-device, two satellite case in the case of the SIC model. The expressions are derived for both independent and non-identically distributed (inid) and independent and identically distributed (iid) uplink channels. The OP is analyzed as a function of the parameters like the number of satellites, the number of devices, and the desired data rate. The results demonstrate that the proposed approach leverages the benefits of mega-LEO satellites to make the topology feasible and attractive for low-powered and low-complexity IoT networks.

Abstract

This paper proposes a deep learning (DL)-based algorithm which is used for improving the performance of digit detection from internet-of-things (IoT)-based analog water meters. The DL algorithm is trained on a rich dataset of over 160,000 images collected from six water nodes deployed at locations with different environmental conditions. A detailed comparison between the proposed DL and machine learning (ML) algorithm is made based on detection accuracy, feature analysis, error analysis, and computational complexity analysis. It is observed that compared to the ML model, the proposed DL model maintained a higher detection accuracy and is more generalized in terms of feature extraction, which makes the algorithm robust.

Abstract

Remote Triggered Labs (RTL) are helpful for students to work on laboratory experiments virtually anytime, anywhere. Such setups can facilitate distance learning and are helpful during pandemics. In this paper, the use of Computer Vision (CV) is demonstrated for RTL experiments. For this, a use-case of the Conservation of Mechanical Energy experiment is considered. A CV-based approach is used to estimate an object’s velocity whose setup primarily consists of a microprocessor, a camera and infrared (IR) sensors. The experiment is recorded, and various CV techniques are employed to estimate the object’s velocity. This paper also compares a CV-based and an IR sensor-based approach to estimate the object’s velocity. Linear regression applied to the CV-based implementation resulted in an optimal mean-squared error (MSE), nearly 10 times better than IR-based implementation.