The world health organization (WHO) has declared the novel coronavirus disease (COVID-19 or 2019-nCoV) outbreak a pandemic, and quarantines are playing a vital role in containing its spread. But globally, the defections of the quarantined subjects are raising serious concerns. If COVID-19 positive, the absconding quarantine subjects can infect more people, which makes their timely detection vital. As the literature suggests, a wearable makes a subject more compliant towards healthcare routines/restrictions; thus, in this work, we have designed an IoT based wearable quarantine band (IoT-Q-Band) to detect the absconding. While designing it, we kept in mind the cost, global supply chain disruption, and COVID-19 quarantine duration, which the WHO recommends. This wearable prototype, with the bundled mobile app, reports and tracks the absconding quarantine subjects in real-time. IoT-Q-Band is an economical solution that could benefit lowincome regions to prevent the spread of COVID-19.

In a country with an extensive road network, it is very tough for authorities to identify and repair the potholes on time, which emerge due to casual wear and tear of the road. These potholes are dangerous for unsuspecting high-speed vehicles and results in multiple life-threatening accidents year-round. Apart from potholes, another severe concern about the time spent on roads is air pollution. Breathing the polluted air, mainly containing the particulate matter that has a diameter of fewer than 2.5 micrometers, is toxic to humans. In this work, we have judiciously designed an Internet of Things based smart helmet, which uses crowdsourcing to report potholes and collect crucial on-road air pollution data so that a person could avoid risk to life and health. We have also introduced the novel concept of remembrance factor and severity index, which could be useful in dealing with the stale and invalid pothole data in the database.

Over the past few decades, with rapid growth in infrastructure, there has been tremendous growth in energy consumption. Along with this, more and more electronic appliances are added to the existing infrastructure every day. Furthermore, the existing energy bills just provide an aggregate number of units consumed but fail to provide any actionable details of appliance level usage. With the quest for long-term energy sustainability and to reduce this ever-growing energy consumption, research groups across the globe have started looking into energy disaggregation as a means of providing feedback. Some promising techniques like Non-intrusive appliance load monitoring (NIALM) have been adopted to provide detailed energy breakdown to the end consumer. Despite all these efforts, energy attribution to the electrical activities still seems to be a farfetched goal, especially in shared spaces. In this work, we have analyzed the possibility of using RF emissions from electronic appliances to detect electrical activity. Besides their known operation, these appliances are known to radiate high-frequency noise in the ambient environment, also called radio frequency interference (or RFI). Hence by utilizing these RFI emissions from electronic appliances, electrical activity from the appliance can be detected in multiple frequency bands and at varying distances. An 8-fit Gaussian mixture model (GMM) and k-peak finder are used for feature extraction from RFI data, followed by appliance activity recognition using k-nearest neighbor based classification. Appliance detection is performed with a mean accuracy of 71.9% across 7-class classification problem. Finally, characteristic features of RFI observed from these appliances, are discussed.Index Terms-RFI (Radio frequency interference), kNN (knearest neighbor), NIALM (Non-intrusive appliance load monitoring), SMPS (Switched mode power supply), HF (High frequency), LF (Low frequency), UPS (Uninterruptable power supply), CFL (Compact fluorescent lamp). A 1530-437X (c)

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