Digital Water

The great water challenges of our time are climate change, population growth and increasing urbanization, ageing and overly stressed infrastructure. These factors inflict significant pressure on our urban water systems as well as water reservoir systems. Water resources are under stress from over-allocation, increased water demand, pollution, climate change, and outdated water related policies. Traditional approaches for delivering water for human consumption, industrial production, agriculture, power generation, and ecosystems are no longer adequate to meet emerging demands. More precisely, the water industry, and in particular water utilities, needs to adapt in order to meet the emerging demands of a dynamic, highly deregulated and competitive environment under changing climate conditions. In such an environment, water industries need to continue to deliver essential services including safe and secure drinking water, stormwater management, wastewater management, flood management, hydroenergy generation, and improvement of ecosystems.

Digital water is based on data gathered from sensor networks and smart management systems installed across the different water service sectors with the aim to create enhanced insight and thus improve decision-making processes. As in the case of smart digital systems in other industries, digital water applications bring an exponential increase in the amount of data generated within different water sectors. Digital water applications enable rapid and decisive analysis that, in turn, provide an increase in system visibility. Therefore, maintenance of the water systems will be provided based on the data collected, alongside expert knowledge. By redefining our water systems and integrating them into smart network systems, benefits will be guaranteed to consumers and also to environment at a pace that was previously impossible to imagine.

In fact, these challenges and their increasing complexity necessitate a paradigm shift to the next generation of water systems beyond traditional water infrastructure.  Cyber-physical systems, digitalization and big data where software, sensors, processors, communication and control technologies are increasingly integrated, offer great potential for the water industry and enable it to make informed decisions in an increasingly changing, complex and uncertain environment.

Authors are encouraged to submit papers on topics relevant to this session that include, but are not limited to the following:

• Data acquisition (e.g., satellite imagery, drones, and on-the-ground sensors and smart meters), 
• Big data analytics, 
• High performance computing,
• Artificial intelligence, 
• Digital twins and real-time controls,
• Advanced statistical methods,
• Blockchain. 

The track chairs invite scientists, engineers, and decision-makers from government, industry, and academia to contribute with theoretical and applied research papers.

Co-chairs:

• Milivojević, Nikola (nikola.milivojevic@gmail.com). Jaroslav Černi Institute, Belgrade, Serbia
• Stojanović, Boban (boban.stojanovic@vodena.rs). Department of Mathematics and Informatics, Faculty of Science, University of Kragujevac, Kragujevac, Serbia
• Stojković, Milan (milan.stojkovic@ivi.ac.rs). The Institute for Artificial Intelligence Research and Development of Serbia, Novi Sad, Serbia

AI techniques for the future cyber-physical enterprise

The cyber-physical enterprise is a digital business innovation concept making Internet of Things, Service Oriented Architectures and Advanced Human Computer Interactions converge for increased awareness of one enterprise business ecosystem and more agile, flexible, and proactive management of unexpected events in the global value networks of today. In essence, it concerns the adoption of Future Internet technologies in the frame of the Factory of the Future paradigm, for the virtual enterprise and its value network. Translating the same concept to production systems in manufacturing enterprises, and moreover to smart systems in general (smart manufacturing, smart cities, smart logistics …), the capability of the next generation systems to sense, model and interpret the signals from the real world is a pre-requisite for a more flexible and agile reconfiguration of those smart systems. Intuitively, a sensing system requires resources and machineries to be constantly monitored, configured, and easily interacted by blue collar workers. All these functions, and much more, are now facilitated by so-called Cyber Physical Systems (CPS). Cyber-physical systems (CPS) enable the physical world to merge with the virtual one, leading to the Internet of things, data and services. One example of CPS is an intelligent manufacturing line, where the machine can perform many work processes by communicating with the components. Using sensors, the embedded systems monitor and collect data from physical processes, like steering of a vehicle, energy consumption or human health functions. The systems are networked making the data globally available. Cyber-physical systems make it possible for software applications to directly interact with events in the physical world, for example to measure peaks in energy consumption.
Artificial Intelligence techniques are now well-known; their application in the enterprises, and more particularly in the future architecture design of cyber-physical systems and cloud-based systems, will increase globally and will then enable a new generation of industrial organizations to function in an information centric manner. The potential of such technologies in fostering a leaner and more agile approach towards engineering is also very high. Engineers and engineering organizations no longer have to be restricted to the availability of advanced processing capabilities but can adopt a ‘pay as you go’ approach which will enable them to access and use software resources for engineering activities from any remote location in the world.   
The topics of interest of this track include, but are not limited to: 

• AI-enabled Cyber-Physical Systems
• AI techniques applicable to the whole enterprises
• Machine Leaning vs. ontology-based knowledge analysis
• Industrial Internet-of-Thing (IIOT)
• Industrial information in Cyber-Physical Systems
• AI-based CPS for Control in Smart Cities Systems/Transportation/Energy/Water
• AI in the Industry 4.0
• AI-enhanced sensing and Smart Enterprises/Manufacturing Systems
• Ontology-based models for CPS/IIOT
• CPS for Future Internet Enterprise Systems
• Cloud-based CPS
• AI for Digital Twin, Cognitive Digital Twin

The track chairs invite scientists, engineers and decision makers from government, industry, and academia to contribute with theoretical and applied research papers. We welcome submission of extended abstracts presenting original unpublished work, which is not been submitted for publication elsewhere. Initial submissions must comply to the specific requirements that you can find on the conference web site.

Best papers from this track presented at the conference will be invited to be extended and submitted to a special issue of the Journal of Industrial Information Integration (JIII), Elsevier (I.F.11,718).

Co-chairs:

• Panetto, Hervé (herve.panetto@univ-lorraine.fr). Research Center for Automatic Control of Nancy (CRAN), Université de Lorraine, Nancy, France
• Weichhart, Georg (georg.weichhart@primetals.com). Primetals, , Austria
• Zdravković, Milan (milan.zdravkovic@gmail.com). Faculty of Mechanical Engineering, University of Niš, Niš, Serbia

ICT for health, well-being and sport

One of the greatest social challenges today is addressing many different concerns and issues related to healthcare and well-being. These challenges include, but are not restricted to, improving the understanding of the causes and mechanisms connected to health; ability to monitor, prevent, detect, and treat disease; personalize health and care delivery. For addressing such challenges, a new approach is needed, which will transform the health and care delivery. This approach is proactive rather than reactive. Healthcare should evolve mechanisms for early risk detection, shifting from the acute treatment to long-term health monitoring. Instead of focusing on single disease treatment, healthcare systems should become capable of handling multiple chronic conditions and comorbidities at the same time. Care delivery should become pervasive, not exclusively institutional. Finally, the generic treatment approach should be abandoned, and care should become more personalized.

Although health is an important, if not necessary, component of well-being, physical activities such as recreation and sports are a good way to maintain and improve it. With the rapid advancement and affordability of miniature, wearable sensor devices, new approaches to quantifying and qualifying our physical activities are possible. For example, innovative applications based on sensor data will contribute to faster, cheaper and safer physical rehabilitation programs, personalized recreational plans, and in-depth analysis of athletes' performance in different sports.

Advanced ICT technologies will play a crucial role in this transformation. Health-related data will grow and will have accurate and useful digital representations. Predictive computing, used by the care providers and customers, will use health-related data to facilitate early risk detection and self-management of health and disease. Health systems will become interoperable. Robotic systems, driven by artificial intelligence will enable assisted living, in smart, age-friendly homes. Biofeedback applications in healthcare, sport, recreation, and rehabilitation will enable an easier and faster way to achieving the desired results. They will assist individuals, healthcare personnel, and trainers in many ways; from influencing the physiological processes in the human body, to motor learning in sports and rehabilitation.

With this session, we aim to uncover the current research in the above mentioned topics and topics closely related to them. All relevant contributions are strongly welcomed.

Co-chairs:

• Trajanović, Miroslav (traja@masfak.ni.ac.rs). Faculty of Mechanical Engineering, University of Niš, Niš, Serbia
• Filipović, Nenad (fica@kg.ac.rs). Faculty of Engineering, University of Kragujevac, Kragujevac, Serbia BIOIRC Bioengineering Research and Development Center, Kragujevac, Serbia
• Kos, Anton (anton.kos@fe.uni-lj.si). Faculty of Electrical Engineering, University of Ljubljana, Slovenia, Ljubljana, Slovenia
• Canciglieri Jr., Osiris (osiris.canciglieri@pucpr.br). Pontifical Catholic University of Parana – PUCPR / PPGEPS, Curitiba, Brasil