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As COVID-19 hounds the world, the common cause of finding a swift solution to manage the pandemic has brought together researchers, institutions, governments, and society at large. The Internet of Things (IoT), Artificial Intelligence (AI) — including Machine Learning (ML) and Big Data analytics — as well as Robotics and Blockchain, are the four decisive areas of technological innovation that have been ingenuity harnessed to fight this pandemic and future ones. While these highly interrelated smart and connected health technologies cannot resolve the pandemic overnight and may not be the only answer to the crisis, they can provide greater insight into the disease and support frontline efforts to prevent and control the pandemic. This paper provides a blend of discussions on the contribution of these digital technologies, propose several complementary and multidisciplinary techniques to combat COVID-19, offer opportunities for more holistic studies, and accelerate knowledge acquisition and scientific discoveries in pandemic research. First, four areas where IoT can contribute are discussed, namely, i) tracking and tracing, ii) Remote Patient Monitoring (RPM) by Wearable IoT (WIoT), iii) Personal Digital Twins (PDT), and iv) real-life use case: ICT/IoT solution in Korea. Second, the role and novel applications of AI are explained, namely: i) diagnosis and prognosis, ii) risk prediction, iii) vaccine and drug development, iv) research dataset, v) early warnings and alerts, vi) social control and fake news detection, and vii) communication and chatbot. Third, the main uses of robotics and drone technology are analyzed, including i) crowd surveillance, ii) public announcements, iii) screening and diagnosis, and iv) essential supply delivery. Finally, we discuss how Distributed Ledger Technologies (DLTs), of which blockchain is a common example, can be combined with other technologies for tackling COVID-19.

ABAC allows for high flexibility in access control over a system through the definition of policies based on attribute values. In the context of an IoT-based system, these data can be supplied through its sensors connected to the real world, allowing for context-awareness. However, the ABAC model alone does not include proposals for implementing security policies based on verified and/or meaningful values rather than on raw data flowing from the sensors. Nor does it allow to implement immediate action on the system when some security flaw is detected, while this possibility technically exists if the system is equipped with actuators next to its sensors. We show how to circumvent these limitations by adding a proactive engine to the ABAC components, that runs rule-based scenarios devoted to sensor data pre-processing, to higher-level information storage in the PIP, and to real-time, automatic reaction on the system through its actuators when required.

At the consumer level in the electrical grid, the increase in distributed power generation from renewable energy resources creates operational challenges for the DSOs. Nowadays, grid data is only used for billing purposes. Intelligent management tools can facilitate enhanced control of the power system, where the first step is the ability to monitor the grid state in near-real-time. Therefore, the concepts of smart grids and Internet of Things can enable future enhancements via the application of smart analytics. This paper introduces a use case for low voltage grid observability. The proposal involves a state estimation algorithm (DSSE) that aims to eliminate errors in the received meter data and provide an estimate of the actual grid state by replacing missing or insufficient data for the DSSE by pseudo-measurements acquired from historical data. A state of the art of historical and near-real-time analytics techniques is further presented. Based on the proposed study model and the survey, the team near-real-time is defined. The proposal concludes with an evaluation of the different analytical methods and a subsequent set of recommendations best suited for low voltage grid observability.

The study of human behavior must take into account the social context, and real-time, networked experiments with multiple participants is one increasingly popular way to achieve this. In this paper a framework based on Python and XMPP is presented that aims to make it easy to develop such behavioral experiments. An illustrative example of how the framework can be used is also presented. This example is a real experiment, which is currently gathering data in the lab.