Katzis, Konstantinos

Future smart healthcare systems – often referred to as Internet of Medical Things (IoMT) – will combine a plethora of wireless devices and applications that use wireless communication technologies to enable the exchange of healthcare data. Smart healthcare requires sufficient bandwidth, reliable and secure communication links, energy-efficient operations, and Quality of Service (QoS) support. The integration of Internet of Things (IoT) solutions into healthcare systems can significantly increase intelligence, flexibility, and interoperability. This work provides an extensive survey on emerging IoT communication standards and technologies suitable for smart healthcare applications. A particular emphasis has been given to low-power wireless technologies as a key enabler for energy-efficient IoT-based healthcare systems. Major challenges in privacy and security are also discussed. A particular attention is devoted to crowdsourcing/crowdsensing, envisaged as tools for the rapid collection of massive quantities of medical data. Finally, open research challenges and future perspectives of IoMT are presented.

The MedSecurance project focus on identifying new challenges in cyber security with focus on hardware and software medical devices in the context of emerging healthcare architectures. In addition, the project will review best practice and identify gaps in the guidance, particularly the guidance stipulated by the medical device regulation and directives. Finally, the project will develop comprehensive methodology and tooling for the engineering of trustworthy networks of inter-operating medical devices, that shall have security-for-safety by design, with a strategy for device certification and certifiable dynamic network composition, ensuring that patient safety is safeguarded from malicious cyber actors and technology 'accidents'.

The uptake of urban city monitoring systems has been synonymous with the rise of Internet of Things (IoT) technologies since their conception. These systems are increasingly incorporated in a wide range of domains, such as disaster awareness, management, and prevention by contextualizing events. In this study, we have developed a platform capable of capturing and associating data from both social media and sensing devices, towards early discovery of disaster-related events by authorities and citizens alike. Establishing these points of association, which we dub as the 'Feedback Loop', forms the basis of our research endeavors. In this paper, we present the architectural components put in place to realize this, at the hardware and software level, as well as the challenges we had to overcome in deploying these at scale within the Engomi Business Region of Nicosia, Cyprus. Pivotal to its final form involved the utilization of Big Data processing technologies to allow for flexibility of sensor deployment and high availability throughout its lifetime. Coupled with state-of-the-art Natural Language Processing (NLP) and anomaly detection algorithms, geared towards spatiotemporal sensor data and natural language processing, our eco-system features a holistic solution towards enhanced decision support.