Smart Communities and Technologies (SmartCT)


The Research Cluster "Smart Communities and Technologies (SmartCT) is an interdisciplinary effort towards creating the scientific underpinnings for designing, developing, and governing complex next-generation smart city and communities infrastructures. The research results of SmartCT can be seen as a collaborative effort of involved TU Wien research groups as well as dedicated personnel and part of an overall contribution of TU Wien to the Complexity Science Hub.  Smart cities and communities infrastructures are an ever-evolving and interwoven fabric of complex (1) cyber-physical systems and (2) cyber-human systems of systems covering a magnitude of different areas. By holistically approaching the challenges of creating and operating such applications, we will create methodologies, frameworks, and software systems enabling an Internet of Cities, i.e., a global complex network of Smart Cities and their applications that securely and collaboratively work to improve the quality of life of their citizens, as well as improve cost and energy efficiency of city operation and infrastructure.

(Research Division of Visual Analytics)
(Research Division of Distributed Systems)
(Research Division of Computer Graphics)
Silvia Miksch Schahram Dustdar Michael Wimmer
Alessio Arleo Christos Tsigkanos Chao Jia
Roger A. Leite Ilir Murturi Manfred Klaffenböck
Project Partners
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3 + 3 years


TU Wien


Cities are ever-evolving, complex cyber physical systems of systems covering a magnitude of different areas. The initial concept of a smart city started with cities utilizing communication technologies to deliver services to their citizens and evolved to using information technology to be smarter and more efficient about the utilization of their resources. In recent years however, information technology has changed significantly, and with it the resources and areas addressable by a smart city have broadened considerably. They now cover areas like smart buildings, smart traffic systems and roads, autonomous driving, energy hubs, electric car utilization, water/waste and pollution management, as well as concepts like urban farming. Therefore, we must enable an open adaptive interconnected approach that is able to incorporate new areas on demand overcoming potentially limiting compartmentalization. Smart cities exchanging their capabilities in an organic elastic manner, being able to address whatever challenge and opportunity they face with the expertise, data, and knowledge of a global network of expert stakeholders form the basis of our vision for an Internet of Cities. 

Our central focus is to create the scientific underpinnings for a future Internet of Cities infrastructure. We will create a comprehensive set of methodologies, models, and tools for design, development, management, and evolution of next-generation smart city applications.

Initially, SmartCT captures three research areas driven by the following research challenges, which will be expanded during the duration of the project:

(1) Cyber-Physical-Human Ecosystem (Research Division of Distributed Systems)

  • Enable open modeling and application environments that are able to seamlessly integrate real-world data into simulations from the physical environment at different stages during its lifecycle.
  • Provide a comprehensive methodology to assist stakeholders in designing, developing, and evolving reusable and maintainable smart city applications. By doing enabling the design and development of reusable and independently maintainable application components in close cooperation with expert stakeholders.
  • Provide a comprehensive middleware toolset to reliably operate and manage future smart city application, that will allow operators to seamlessly deploy and execute complex applications in an autonomic, repeatable, and observable way.

(2) Model Building and Visual Analytics  (Research Division of Visual Analytics)

  •  Conceptualize and design VA methods for particular users exploring and analyzing particular heterogeneous, multivariate (including spatial and temporal properties) data sets and performing particular tasks.
  • Capture the quality and uncertainty of the data sets in scalable and interactive VA methods.
  • Tackle unpredictable, unexpected, and unforeseeable events and patterns from the past, present, and future in the VA methods.
  • Utilize VA methods to guide and steer model generation and usage to achieve better predictions.
  • Support communication and interaction between the human and VA methods in a user appropriate way from a task-specific perspective.

(3) 3D Spatialization (Research Division of Computer Graphics)

  • Acquire 3D data in a smart city, based on different sources of input, at different times, including indoors.
  • Capture the dynamic aspect of the data, i.e., detecting changes between different acquisitions.
  • Spatial computation in the 3D smart city to derive new insights. This step can interact with sensor data obtained in traditional manners. 
  • Visualization of the 3D smart city in an efficient manner. 


Davide Ceneda, Alessio Arleo, Theresia Gschwandtner, Silvia Miksch, "Show Me Your Face: Towards an Automated Method to Provide Timely Guidance in Visual Analytics", IEEE Transactions on Visualization and Computer Graphics, vol. 28, pp. 12, 2022. paper
Alessio Arleo, Christos Tsigkanos, Roger Leite, Schahram Dustdar, Silvia Miksch, Johannes Sorger, "Visual Exploration of Financial Data with Incremental Domain Knowledge", Computer Graphics Forum, 2022. Source Code Supplemental Video
Roger Leite, Theresia Gschwandtner, Silvia Miksch, Erich Gstrein, Johannes Kuntner, "NEVA: Visual Analytics to Identify Fraudulent Networks", Computer Graphics Forum, vol. 39, 2020. paper
Teaser Image Alessio Arleo, Walter Didimo, Giuseppe Liotta, Silvia Miksch, Fabrizio Montecchiani, "VAIM: Visual Analytics for Influence Maximization", 28th International Symposium on Graph Drawing and Network Visualization, 2020. arXiv version YouTube
Roger Leite, Alessio Arleo, Johannes Sorger, Theresia Gschwandtner, Silvia Miksch, "Hermes: Guidance-enriched Visual Analytics for economic network exploration", Visual Informatics, Elsevier, vol. 4, 2020.
Christos Tsigkanos, Alessio Arleo, Johannes Sorger, Schahram Dustdar, "How do firms transact? Guesstimation and Validation of Financial Transaction Networks with Satisfiability", IEEE 20th International Conference on Information Reuse and Integration for Data Science, 2019.
Alessio Arleo, Christos Tsigkanos, Chao Jia, Roger Leite, Ilir Murturi, Manfred Klaffenböck, Schahram Dustdar, Silvia Miksch, Michael Wimmer, Johannes Sorger, "Sabrina: Modeling and Visualization of Economy Data with Incremental Domain Knowledge", IEEE VIS 2019, 2019. paper…
Velitchko Filipov, Alessio Arleo, Davide Ceneda, Silvia Miksch, "The Fabric of Heroes: an Infographic about Marvel Cinematic Universe", International Symposium on Graph Drawing and Network Visualization, 2019.
Velitchko Filipov, Alessio Arleo, Paolo Federico, Silvia Miksch, "CV3: Visual Exploration, Assessment, and Comparison of CVs", Computer Graphics Forum, vol. 38, pp. 11, 2019. paper
Johannes Sorger, Manuela Waldner, Wolfgang Knecht, Alessio Arleo, "Immersive Analytics of Large Dynamic Networks via Overview and Detail Navigation", IEEE AIVR 2019, 2019. paper
Velitchko Filipov, Davide Ceneda, Michael Koller, Alessio Arleo, Silvia Miksch, "The Circle Of Thrones: Conveying the Story of Game of Thrones Using Radial Infographics", , 2018. paper High Quality Poster