COVID-19 has disrupted normal life and has enforced a substantial change in the policies, priorities and activities of individuals, organisations and governments. These changes are proving to be a catalyst for technology and innovation. In this paper, we discuss the pandemic’s potential impact on the adoption of the Internet of Things (IoT) in various broad sectors, namely healthcare, smart homes, smart buildings, smart cities, transportation and industrial IoT. Our perspective and forecast of this impact on IoT adoption is based on a thorough research literature review, a careful examination of reports from leading consulting firms and interactions with several industry experts. For each of these sectors, we also provide the details of notable IoT initiatives taken in the wake of COVID-19. We also highlight the challenges that need to be addressed and important research directions that will facilitate accelerated IoT adoption.

In the context of social media, the integrity of images is often dubious. To tackle this challenge, we introduce Exif2Vec , a novel framework specifically designed to discover modifications in social media images. The proposed framework leverages an image’s metadata to discover changes in an image. We use a service-oriented approach that considers discovery of changes in images as a service . A novel word-embedding-based approach is proposed to discover semantic inconsistencies in an image metadata that are reflective of the changes in an image. These inconsistencies are used to measure the severity of changes. The novelty of the approach resides in that it does not require the use of images to determine the underlying changes. We use a pretrained Word2Vec model to conduct experiments. The model is validated on two different fact-checked image datasets, i.e., images related to general context and a context-specific image dataset. Notably, our findings showcase the remarkable efficacy of our approach, yielding results of up to 80% accuracy. This underscores the potential of our framework.

Vaccination has been the most effective approach in the fight against COVID-19 pandemic. More than half of the world’s population has been vaccinated and sufficient data is available to analyze the impact of COVID-19 vaccines around the globe. In this paper, we present a correlation analysis between administered vaccine doses and COVID-19 cases/deaths in Europe. The correlation analysis is performed considering different types of vaccinations, different age groups and different COVID-19 variants (including the prevalent Delta and Omicron variants). We present a detailed analysis for 30 European countries giving various insights such as efficacy of six different vaccines, effect of vaccinating different age groups and how the correlation evolves as different COVID-19 variants emerge.