SASO 2017 - 2017 11th IEEE International Conference on Self-Adaptive and Self-Organizing Systems

The 2017 edition of the SASO conference series will be held at the University of Arizona, Tucson, AZ in the week of September 18-22, 2017. SASO is part of FAS*, a common umbrella for two closely related but independent conferences (SASO and ICCAC) with shared events including workshops, tutorials, doctoral symposia, etc.
The aim of the Self-Adaptive and Self-Organizing systems conference series (SASO) is to provide a forum for the foundations of a principled approach to engineering systems, networks and services based on self-adaptation and self-organization. The complexity of current and emerging networks, software and services, especially in dealing with dynamics in the environment and problem domain, has led the software engineering, distributed systems and management communities to look for inspiration in diverse fields (e.g., complex systems, control theory, artificial intelligence, sociology, and biology) to find new ways of designing and managing such computing systems. In this endeavor, self-organization and self-adaptation have emerged as two promising interrelated approaches. Many significant research problems exist related to self-adaptive or self-organizing systems. A challenge in self-adaptation is often to identify how to change specific behavior to achieve the desired improvement. Another major challenge is to predict and control the global system behavior resulting from self-organization. Yet more challenges arise from the confluence of self-adaptation with self-organization. For instance, how do self-* mechanisms that work well independently operate in combination? How are meso-level structures formed which leverage micro-level behavior to achieve desirable macro-level outcomes, and avoid undesirable ones?
The 11th edition of the SASO conference embraces the inter-disciplinarity and the scientific, empirical and application dimensions of self-* systems; it thus aims to attract participants with different backgrounds, to foster cross-pollination between research fields, and to expose and discuss innovative theories, design principles, frameworks, methodologies, tools, and applications.
Topics
The topics of interest to SASO include, but are not limited to:
Systems theory: theoretical frameworks and models; biologically- and socially-inspired paradigms; inter-operation of self-* mechanisms;
Systems techniques: techniques to specify and analyze self-* systems, like statistical physics, machine learning, multi-agent systems, or other novel techniques;
Systems engineering: reusable mechanisms, design patterns, architectures, methodologies; software and middleware development frameworks and methods, platforms
and toolkits; hardware; self-* materials; governance of self-* systems, emergent behavior in self-* systems;
System properties: robustness, resilience, and stability; emergence; computational awareness and self-awareness; reflection; anti-fragility;
Cyber-physical and socio-technical systems: human factors and visualization; self-* social computers; crowdsourcing and collective awareness; human-in-the-loop;
Data-driven approaches: data mining; machine learning; data science and other statistical techniques to analyze, understand, and manage behavior of complex systems;
Education: experience reports; curricula; innovative course concepts; methodological aspects of self-* systems education;
Ethics and Humanities in self-* systems;
Applications and experiences with self-* systems in any of the following domains:
Smart-*: application of self-* principles to smart-grids, smart-cities, smart-environments, smart-vehicles
Industrial automation: embedded self-* systems, adaptive industrial plants, smart industries (Industry 4.0)
Transportation: autonomous vehicles, coordination between vehicles, pedestrians, and infrastructure, and traffic optimization
Unmanned systems: aerial vehicles, undersea vehicles, other robotic platforms
Internet of Things: challenges, applications, and benefits; self-* for network management, self-* applied to Cybersecurity