A Cyber-Physical Social System (CPSS) tightly integrates computer systems with the physical world and human activities. In this article, a three-level CPSS for early fire detection is presented to assist public authorities to promptly identify and act on emergency situations. At the bottom level, the system’s architecture involves IoT nodes enabled with sensing and forest monitoring capabilities. Additionally, in this level, the crowd sensing paradigm is exploited to aggregate environmental information collected by end user devices present in the area of interest. Since the IoT nodes suffer from limited computational energy resources, an Edge Computing Infrastructure, at the middle level, facilitates the offloaded data processing regarding possible fire incidents. At the top level, a decision-making service deployed on Cloud nodes integrates data from various sources, including users’ information on social media, and evaluates the situation criticality. In our work, a dynamic resource scaling mechanism for the Edge Computing Infrastructure is designed to address the demanding Quality of Service (QoS) requirements of this IoT-enabled time and mission critical application. The experimental results indicate that the vertical and horizontal scaling on the Edge Computing layer is beneficial for both the performance and the energy consumption of the IoT nodes.

One of the main obstacles towards the promotion of IoT adoption and innovation is data interoperability. Facilitating cross-domain interoperability is expected to be the core element for the realisation of the next generation of the IoT computing paradigm that is already taking shape under the name of Internet of Everything (IoE). In this article, an analysis of the current status on IoT semantic interoperability is presented that leads to the identification of a set of generic requirements that act as fundamental design principles for the specification of interoperability enabling solutions. In addition, an extension of NGSIv2 data model and API (de-facto) standards is proposed aiming to bridge the gap among IoT and social media and hence to integrate user communities with cyber-physical systems. These specifications have been utilised for the implementation of the IoT2Edge interoperability enabling mechanism which is evaluated within the context of a catastrophic wildfire incident that took place in Greece on July 2018. Weather data, social media activity, video recordings from the fire, sensor measurements and satellite data, linked to the location and the time of this fire incident have been collected, modeled in a uniform manner and fed to an early fire detection decision support system. The findings of the experiment certify that achieving minimum data interoperability with light-weight, plug-n-play mechanisms can be realised with significant benefits for our society.