Patient care is a key element of healthcare practices. The use of Healthcare IoT could enhance the lives of patients and the quality of services provided by medical practitioners, nurses, clinicians, pharmaceutical companies and government in general. Wireless healthcare monitoring systems tremendously used in hospitals and other healthcare practices and witnessed as a revolution in the medical field. Nevertheless, security and privacy for interconnected things are mostly overlooked on the Internet of Things paradigm. In the context of healthcare and remote health monitoring, it is crucial a systematic approach for security and privacy measures must be used in device manufacturing, interconnecting things, communication, data handling and storage, and destruction of such devices and data. This paper examines the current state of security and privacy of the Internet of Things in the healthcare system, the challenges encountered in implementing security frameworks and advocates security and privacy solutions.

The emergence of the paradigm of the Internet of Things has underpinned the development of data-driven cyber-physical systems that collect and process data that is dense both in space and time. The application areas of such data-driven IoT systems are numerous and their socio-economic impact of great importance as they enable the monitoring and management of processes in sectors ranging from urban management to management of the natural environment. In this work, we introduce and detail an end-to-end technological framework for data-driven IoT systems for landslide monitoring. The framework is articulated in three tiers -namely data acquisition, data curation and data presentation. For each tier we present and detail its design and development aspects; from the IoT hardware design and the wireless communication technologies of choice, to how Big Data infrastructure and Machine Learning components can be combined to support a sophisticated presentation tier that delivers the true added value of a system to its final users. The framework is validated, extended and fine-tuned by means of two pilots at locations experiencing the impact of different landslide types and activity. This work qualitatively improves upon existing methods of landslide monitoring and showcases how data-driven IoT systems can pave new pathways for interdisciplinary research as well as generate positive impact on modern societies.