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Social distancing has been regarded as a key method by the authorities worldwide to manage the pandemic of COVID-19. Digital technologies play a crucial role to support the social, professional and economic activities when people are forced to stay locked-down in their homes. Internet of things (IoT) technologies have a track of providing high quality remote health care and automation services which could guarantee social distancing while maintaining health and well-being of populations. In this paper, we propose an end-to-end IoT architecture to support the social distancing in the event of pandemic. The architecture comprises of the major use cases of IoT in relevance with the COVID-19. Furthermore, we also present a short-term and long-term strategy to mange the social distancing methodology using the proposed IoT architecture. The challenges associated with each layer of architecture have been highlighted and design guidelines have been presented to deal with them.

Wearable and ambient sensors have been increasingly used for unique remote health monitoring applications. In this work, we propose an Internet of Medical Things (IoMT) architecture based on automated sensing of physiological and ambient parameters, to detect the risk of obesity in individuals. Timely assessment of obesity risk could improve the quality of life for patients in future as it would help to reduce the probability of chronic diseases such as diabetes. The architecture uses Body Mass Index (BMI) of patient as an input parameter which is a key measure to indicate obesity. Furthermore, we also propose to integrate the proposed IoMT architecture with the emerging scheme of Federated Learning (FL). FL has been used to recommend the appropriate fitness routine for the users. MATLAB simulations for the proposed FL algorithm has been performed and the variations in the loss functions have been observed.

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Most research done on the channel polling mechanisms for WSN MAC protocols has focused on achieving efficiency in terms of optimizing either delay or energy performance. This study introduces the mechanism of altering the polling interval distribution for the MAC protocol, based on the application's optimization objective (delay or energy). Two options have been considered for both packet interarrival and polling interval distributions: deterministic and exponential, and their impact on performance analysis has been studied. The packet aggregation and block acknowledgements transmission schemes have also been incorporated in the performance analysis. Simulations for energy consumption and delay have been performed using MATLAB, for various combinations of interarrival and polling interval distributions. Results show that the energy consumption improves for the exponentially distributed polling intervals, while deterministic polling intervals provide better delay performance. Changing values of traffic load and packet aggregation demonstrate the trade-off between delay and energy consumption. The impact of aggregation on the error probability and subsequently on delay and energy has also been illustrated in simulations. Finally, optimal operating parameters have been studied in order to combine delay and energy consumption as optimization criteria.