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System-level Design – methodologies & tools for reconfigurable platforms (programmable logic, partial reconfiguration); FPGA architectures, design, and CAD; robust & power aware computing and systems; energy management; data integration and fusion; communication modes; compensation mechanisms for aging and temperature; methodologies & tools for fault-tolerant designs; hardware attacks detection, threat modeling & defense; hardware-based security design; trusted design automation & tools; adaptive and compositional architectures.
Networked Embedded Systems – design, dependability and tooling for networked systems including real-time, safety, security, maintainability aspects; middleware solutions for distributed and networked systems; network protocols for distributed systems including resource control, admission control, self-aware adjustments, time synchronization, routing and energy consumption.
Cognitive Computing in Cyber-Physical Systems – hardware and architectures, software and algorithmic approaches for artificial intelligence, hardware accelerators for machine/deep learning algorithms; algorithmic optimizations for general purpose computing; modeling, design and analysis of networked control, switched control, and distributed control systems.
Embedded Systems and Architectures – design methodologies and tools; parallel and distributed embedded systems; multi/many-core embedded systems; timing and schedulability analysis; quality of service control; distributed and system-on-chip architectures; real-time and dependable mechanisms and architectures; static and dynamic reconfigurable systems; embedded systems case studies.
Massive Internet of Things, Smart Factories, Smart Building and Smart Transportation Systems are some of the emerging concepts and visions that are expected to have a dramatic societal impact in the near future. In such inherently distributed scenarios, communication technologies play a crucial role, by providing the necessary connectivity. Therefore, it is of utmost importance to look at the new challenges posed by Societal Automation and identify the requirements that Communication Systems shall have to fulfill, in order to identify the limitations of current communication technologies and define a path for future R&D. It is in particular very important to address architectural aspects and service aspects, such as distributed architectures, service provisioning, digital twins, and the overall automation of cities.
Wireless Networks – WLAN; WPAN; LPWAN; 5G; 6G; Wireless coexistence and spectrum-sharing; Radio resource management in noisy environments; Internetworking and interoperability; Wireless Instrumentation and Wireless Sensor Network; Mesh, Relay, and Multi-hop Wireless Sensor Network; Software-defined and cognitive radio networks.
Protocols, Applications and Services – New protocols and enhancements to automation protocols (e.g. CoAP, MQTT); Massive Machine Type Communication (mMTC), Service based architectures; Service-Oriented Architectures (SoA); Use-cases and proofs of concept (PoCs).
Design Issues – Event-driven and Time-Triggered communications; Security and safety; Dependability and fault tolerance; Remote configuration and network management; Real-time communication and precise synchronization; Quality of Service (QoS) and performance indexes; Message schedulability analysis.
Specialized Communication Networks in Diverse Application Domains – Home & Building Automation; Automotive Networks; Rail Transportation Networks; Avionics Networks Networks.
Industrial Automation –Industrial Ethernet, Wireless Industrial Networks; Software Defined Networking (SDN); Network Function Virtualization; Industrial cloud computing; Industrial Internet of Things (IIoT); Power Systems – Protocols for Automatic Meter Reading, Protocols for Power System Automation.
Computing Models – Cloud, Fog, and Edge computing; Pervasive computing; High performance computing; Probabilistic computing; Bio-inspired computing
Collaborative Computing Models – Collaborative and cooperative applications of IoT; Social IoT in the age of Cloud, Edge and Big Data paradigms; Blockchain
Resource Management of Computing Models – Intelligent resource management of computing models and applications built for scalability, fault tolerance, and high availability
Big Data Analytics – High performance data analytics; Data search and representation; Architecture and system design
Security and Privacy Issues in Computing Models – Security and privacy challenges arising due to novel computing models and architectures including but not limited to; detection and protection against multi-stage attacks, privacy-preserving information sharing, privacy by design solutions, and collaborative approaches to information security
Algorithms; Natural Language Processing; Computer Vision and Speech Understanding; Pattern Recognition; Data Mining and Information Retrieval; Soft Computing and tools – Fuzzy Logic, Neural Networks, Heuristic and AI Planning Strategies, Knowledge-based Systems; Automated Decision Systems; Computational Theories of Learning; Knowledge Representation; Reasoning and Evolution; Machine Learning – Supervised Learning, Unsupervised Learning, Neural Networks & Deep Learning, Reinforcement Learning; Ambient intelligence – contextual awareness, natural interaction, adaptability, robustness, fault-tolerance, security; Ambient intelligence and IoT – case studies; Semantic Web ; Hybrid Intelligent Systems; Software & Hardware Architectures & Accelerators; AI Systems in Societal Automation – Cars & Automotive and Service Industries; Home & Building automation; Transportation Systems; Smart Cities & Urban Automation; Energy Systems: Case Studies – experience emerging trends.
Digitization has strongly accelerated development and deployment of sensors in many areas. However, in many AI applications it became quickly obvious, that a priori Big Data is often unavailable in societal automation, e.g. in industrial contexts. Conclusively an important question for the future is: How can reliable data, which meet certain standards e.g. regarding accuracy, communicated meta-information and use case specific requirements, be provided economically. Well-defined ethical guidelines for sensing applications may also turn out to be a competitive advantage in societal automation. Sensor technology is at the basis of many ideas and developments in digitization and Industrial Internet of Things. Availability of specific sensing methods with sufficient accuracy is often decisive for the feasibility of automation concepts. Sensing defines the way how automation systems collect information from their environment – and often from us, as users. Future sensing solutions will be embedded in large and extremely complex automation systems, such as smart machines, buildings, factories, cities and infrastructure systems. Therefore, it is of great interest to take a look at visions of future automation solutions and find out which – perhaps qualitatively new – sensing solutions will be needed for that. Vice versa, recent rapid progress in sensor development and signal processing inspires previously unthinkable automation.
Conference topics, therefore, include the following:
IOT & Cyber-Physical Systems Design and Technology; IoT and Sensor systems; Industrial IoT Systems; Distributed Architectures for Adaptive Systems; Autonomous Cyber-Physical Systems; Self-Adaption and Self-Organization; Learning and Self-Optimizing Cyber-Physical Systems. Novel Protocols and Network Technology; Data Streaming Architectures; Application areas of IoT & CPS.
Novel research results, deployment, case studies, experience and emerging trends:
Areas – Digital Arts & Entertainment, Digital Lifestyle, Digital Education; Wearable Automation; Social Robotics: applications of robotics to human tasks and activity areas to assist or replace humans in diverse application areas: home, school, hospital, employment place, city – for instance in feeding infirm and elderly, exoskeletons; Smart and self driving cars; Smart Vehicles – ground, air and water based; Transportation Systems; Urban Automation and Systems; Smart Cities; Energy Systems; Health delivery and systems – consultations, patient’s monitoring, tele-surgery, etc; Military Logistic Systems, Space Station, Space Cities, Space Urban Automation, etc.
Smart Cities address sustainability in social, economic and environmental issues. They are challenged to exploit information and communication technology and make efficient data-driven use of our resources with the overall goal to reduce energy consumption and the size of our ecological footprint. Smart Cities should also enable the development of crowd-powered systems, where traditional algorithmic computing gives way to data flowing through machines and exchanged with people. Key areas of future cities include space management, construction and infrastructure, buildings and homes, urban manufacturing and industry, mobility, transportation and traffic management, energy networks (electricity, gas, water), waste and food management, healthcare and assisted living, governance and education. While all of those areas have their specific demands and needs, it is yet another challenge to integrate and combine them in a balanced way taking into account both technical and non-technical perspectives. It is evident that we need to design services that are acceptable, accessible, and adaptable, while an investment must be made in educating the citizens in Smart City technology so as to avoid exclusion. It is critical to consider standardization and design of procedures and measures that support risk prevention and resilience, while at the same time fostering Smart City business development and growth. The track is open for recent findings in research and development, both from academia and industry, including but not limited to:
Smart City: home and building automation; smart lighting and street; smart transportation system; wire and wireless communication; smart metering and grid; city monitoring, control, and optimization; early warning systems; software platforms and tools for participation and governance; edge/fog/mist computing; trust/security/privacy; experience design and evaluation; Design of accessible, adaptive, and adaptable Smart City services; AI role in Smart City operation; social issues.
City of the Future: main functions, functionality and ecosystems; physical infrastructure systems and interaction; design methodology, formalism, and tools; roadmaps; validation and testing platforms; simulators and virtual reality applications; city-wide communication system(s), WAN, LAN, PAN, and interaction; future transportation and mobility; energy distribution systems and underpinning technologies; city specific AI (CAI); citizen development, engagement, and inclusion.
Access control; Authentication; Trust management; Accountability; Anonymity and privacy; Formal methods and verification; Hardware-based security; Language-based security; Software security; Data and system integrity; Database security; Distributed systems security; Network security; Intrusion detection; Mobile security; Web security; Security metrics; Security protocols; Real-time implementations of security algorithms and protocols; Security issues specific to application domain: wearable automation, home and building automation, vehicles and transportation systems, smart city & urban automation, smart metering and power systems.
The future Large-Scale Ultra-Complex Engineering Systems are envisaged to be beyond the size of large-scale systems of today. A good example of such a system is in the concept of the City of the Future. Cities of the Future – unlike Smart Cities which are largely an ICT retrofit of the legacy constituent systems (electric energy grid, communication networks, transportation system, etc.) – are going to be predominantly designed from scratch to a specification. At least the kernel of the urban ecosystem; followed by a subsequent continuous growth at the fringes governed by somehow semi-chaotic and conflicting requirements of different stakeholders. Large-scale systems in operation today are mostly the result of engineering practice accumulated over decades of the development. Little science may have been involved in their development as systems. In this context, the scientific knowledge the design of large systems is based on may be of limited value for the design of future large-scale ultra complex engineering systems.
Some of topics are listed below. However, prospective authors are welcome to expand that list to account for the ideas which were not cast in anyone of the listed topics:
Human Behavior Modeling; Cultural Behavior Modeling; Behavior Prediction and Anticipation; Influence Measures; Cognitive Modeling; Social Network Modeling; Human-Machine/Robot Interaction; Blockchain & Applications; Digital Identity; Digital Currencies; Digital Learning; Digital Health; Digital Governance; Digital Arts; Digital Humanities.
