You can find a PDF version of the Call for Papers here.
Through recent developments in the last years, especially in the Internet-of-Things (IoT) or Cyber-Physical Systems, systems become larger, more distributed, interconnected, and mobile. Consequently, not all possible system states and contexts can be foreseen at design-time. This results in the so-called ‘design-runtime gap’. Accordingly, those systems have the inherent requirement to learn new behavior at runtime and to cope with the changing context / situation.
With recent developments in the processing capacity of lightweight IoT devices and new paradigms such as Edge and Fog Computing, which simplifies the integration of the omnipresent computational power of Cloud Computing, proficient learning approaches find their way into those adaptive PerCom systems. Furthermore, reactions based on real-time analysis with machine learning seem feasible now.
The above described type of adaptive PerCom systems is inherently distributed and composed of possible hundreds of instances that might act autonomously. New approaches are required to achieve global goals while enabling local autonomicity. Relevant work can be found in different areas including autonomic computing, self-adaptive and self-organizing software and systems, multi-agent systems, organic computing, as well as context- and situation-aware systems.
More specifically, adaptive, learning PerCom systems can be understood as having two main properties. They
capture information about their internal structure as well as their context and apply learning techniques to reason on how to adapt to changing; and
act in the knowledge that they interact with other systems in a shared environment.
Often, they do so by applying nature-inspired approaches, especially for the learning and coordination tasks. Hence, we especially welcome researchers working in those research streams. This workshop covers all topics concerning adaptive PerCom systems. Possible topics for the ALPACA workshop include:
Fundamental science and theory of adaptive, learning PerCom systems
Levels and aspects of adaptive, learning PerCom systems
Architectures for individual and collective adaptive, learning PerCom systems
Measurements, quality assurance, and evaluation in adaptive, learning PerCom systems
Verification & validation and testing
Artefacts, test beds, simulations, demonstrators of adaptive, learning PerCom systems
Tool support for evaluation and measurements, and quality assurance;
Methods and algorithms for model learning (self-modeling) and reasoning
Self-adaptation in individual and collective adaptive, learning PerCom systems
Synthesis and verification metrics and benchmarks
Transition strategies for increasing adaptation in existing PerCom systems
Open challenges and future research directions
Ethical concerns in adaptive, learning PerCom systems
Applications and case studies: cloud computing, cyber-physical systems, data centers, dependable computing, industrial internet / industry 4.0, internet of things, mobile computing, service-oriented systems, smart buildings, smart city, smart grid / energy management, smart factory, smart health, intelligent traffic (management) systems, autonomous robotics, and space applications
Cross-domain contributions bringing Organic/Autonomic or self-learning technology to pervasive systems
Workshop paper submission deadline: November 14, 2022 November 28, 2022 (extended)
Planned Notifcation: January 5, 2023
Workshop Date: March 13 or 17, 2023
Paper Types:
Full workshop paper limited to 6 pages (double column, IEEE format)
Short workshop paper limited to 4 pages (double column, IEEE format)
Talk extended abstract limited to 2 pages (double column, IEEE format)
For all details, refer to the submission page.