Charilaos Akasiadis

Developing complex services in an IoT ecosystem

Recent advancements in single board computers, communications technologies and protocols, as well as the concepts of service-oriented architectures (SoA) and everything as a service (EaaS), constitute a prelude to the Internet of Things (IoT) revolution. Billions of devices are interconnected and integrated as modular web services, which can be used and re-used by developers making the building and realization of complex applications easier. In this work, we take advantage of the SYNAISTHISI platform, which is able to interface and integrate devices, services and humans, and expose their capabilities as virtualized semantically annotated services that can be mashed into applications. We analyze the development process from a developers perspective, present an ontology for smart meeting rooms and focus on a real-world case, that is delivering a complex application for counting the persons in the interior of a smart meeting room, using technologies that support IoT.

Exploiting future internet technologies: the smart room case

In this paper we present SYNAISTHISI, i.e., a cloud-based platform, that provides the necessary infrastructure in order to interconnect heterogeneous devices and services over heterogeneous networks. SYNAISTHISI facilitates the orchestration of a collective functionality allowing several services to be managed through agents that dynamically allocate the available resources.. In a smart room use-case, multiple sensing and actuation units have been developed and deployed in a lecture room. Maintaining comfort levels for room occupants is achieved through automatic decision making that exploits information from a complex event recognition engine. Our goal is to improve the overall working environment, while minimizing energy losses.

Stochastic filtering methods for predicting agent performance in the Smart Grid

A variety of multiagent systems methods has been proposed for forming cooperatives of interconnected agents representing electricity producers or consumers in the Smart Grid. One major problem that arises in this domain is assessing participating agents uncertainty, and correctly predicting their future behaviour. In this paper, we adopt two stochastic filtering techniques —the Unscented Kalman Filter equipped with Gaussian Processes, and the Histogram Filter— and use these to effectively monitor the trustworthiness of agent statements regarding their final actions. The methods are incorporated within a directly applicable scheme for providing electricity demand management services. Simulation results confirm that these techniques provide tangible benefits regarding enhanced consumption reduction performance, and increased financial gains.

Providing a Scientific Arm to Renewable Energy Cooperatives

Renewable Energy-Supplying cooperatives (REScoops) are cooperatives of renewable energy producers and/or consumers, which are under formulation in the emerging European smart grid. Their emergence highlights the importance of proconsuming green energy and simultaneously puts forward principles such as energy democracy and self-consumption, assists the fight against energy poverty, and helps reduce GHG emissions. To this end, the incorporation of scientific and technological solutions into the REScoops’ everyday business and practices, is key for improving these practices and assessing their potential benefits, and as such for enabling them to deliver the maximum possible gains to their members and society at large. This chapter outlines three key axes of scientific research and solutions that can be used for REScoops, namely, (a) a statistical analysis, (b) an applied behavioural analysis, and (c) an artificial intelligence/machine learning one. Also presented are results and lessons learned from providing such solutions to European REScoops as part of the H2020 REScoop Plus project.