Dependable Internet of Things
Kay Römer, Graz University of Technology, Austria
Agent-based Modelling and Simulation
Ojaras Purvinis, Kaunas University of Technology, Lithuania
Dependable Internet of Things
Kay Römer
Graz University of Technology
Austria
https://www.tugraz.at/en/institutes/iti/home/
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Brief Bio
Kay Römer is professor at and director of the Institute for Technical Informatics and head of the Field of Expertise "Information, Communication & Computing" at TU Graz. He obtained his doctorate in computer science from ETH Zurich in 2005 with a thesis on wireless sensor networks.
Kay Römer is an internationally recognized expert on networked embedded systems, with research focus on wireless networking, fundamental services, operating systems, programming models, dependability, testbeds, and deployment methodology. He has co-chaired the program committees of leading conferences in the field such as SenSys or IPSN, he is also chairing the steering committee of the EWSN conference series. He is coordinator of the TU Graz Research Center "Dependable Internet of Things" and leads the research area "Cognitive Products" in the research center Pro2Future - Products and Production of the Future.
Abstract
Wireless networked embedded systems are increasingly used for safety-critical applications such as smart production or networked cars, where failures may have severe impact. Therefore, strict dependability requirements have to be met. This is difficult to achieve, however, as these applications often operate in harsh environments or are exposed to attacks. In this talk we present recent research results obtained in the Dependable Things research center at TU Graz which aims at increasing the dependability of the IoT for safety-critical applications.
Specifically, we present a single-anchor approach to robust and accurate localization using UWB, a method to analyze software for potential side-channel leakage, and an approach to automatically learn models of protocols used in the IoT in order to formally verify their correct implementation and interoperability.
Agent-based Modelling and Simulation
Ojaras Purvinis
Kaunas University of Technology
Lithuania
Brief Bio
Ojaras Purvinis is professor of Kaunas University of Technology since 1991. He graduated Vilnius University Mathematics Faculty in 1977. He obtained a doctor degree from the Science Council of Lithuania in 1993 for the research in the field of differential eguations. Ojaras Purvinis has served twice as a chair of the Interdisciplinary Department at his university. He also has a work experience in industry. His current research interests are mathematical modelling of engineering and economic problems, agent-based simulation and applications of fuzzy logic. He recently has participated in two international research projects supported by Baltisch-Deutsches Hochschulkontor and several applied projects in his university. Ojaras Purvinis has published over 100 scientific papers with co-authors and single-authored.
Abstract
Agent-based modelling (ABM) and simulation is a relatively new approach to modelling off complex problems and phenomena. It proved to be an effective tool to watch and investigate processes that can be complicated to model by mathematical or other modelling means. Agent-based models comprise relatively autonomous computational objects, which are called agents. Agents may be components of equipment, people, organizations or intangible things (projects, ideas, investments) etc. The performance and properties of modelled systems are determined by the interaction of the same type or different types of agents. Agents may have the following properties: intelligence, autonomy, reactivity, proactivity, adaptivity, robustness, goal-orientation mobility. Agents communicate by sending data and other types of messages to each other. Depending on the data or signals received, agents perform actions or change their state. The article presents a short overview of the ABM, the notion of software agent, the design of the agent-based models using the statecharts and examples of agent-based models.