-------- Original-Nachricht -------- Betreff: [computational.science] ACM Transactions on Autonomous and Adaptive Systems special issue on AC Datum: Tue, 24 Oct 2006 07:30:38 +0300 Von: Thanos Vasilakos vasilako@ath.forthnet.gr Organisation: "OptimaNumerics" An: Computational Science Mailing List computational.science@lists.optimanumerics.com
ACM TRANSACTIONS on AUTONOMOUS and ADAPTIVE SYSTEMS
SPECIAL ISSUE on ADAPTIVE LEARNING in AUTONOMIC COMMUNICATION
CALL FOR PAPERS
The ubiquitous explosion of the Internet and the fast proliferation of networked devices and applications such as sensor networks, mobile systems, Grids, and Peer-to-Peer communications create a unique challenge for network and system administrators. Future applications will involve mobile users, possibly with on-body sensors interacting with a pervasive computing environment that detects their activity, current context and adapts accordingly. Innovative solutions are required for managing the plethora of network devices and systems, multiple inter-connected networking technologies -- wired and wireless, and the complexity of distributed applications. The promise of pervasive computing environments will not be realized unless these systems can effectively "disappear"; and for this they need to become autonomous by managing their own evolution and configuration changes without explicit user or administrator action. The term Autonomic Communication (AC) and computing is used for this form of self-managing systems able to support self-configuration, self-healing and self-optimization. AC is a new paradigm to assist the design of the next generation networks: self-organizing, context-aware pervasive systems and service-oriented computing environments so as to better support highly dynamic and mobile users and virtual organizations.
Such data-intensive, unstructured environments with minimal or no centralized control present a challenge for traditional methods of analysis and design.
Adaptive learning methods, in general, include neural networks, fuzzy logic and granular computing, genetic algorithms and other data-driven methods and algorithms offer promising alternatives. Altogether they form a coherent platform of Computational Intelligence.
The objective of the proposed special issue is to highlight an ongoing research on adaptive learning systems and discuss their applicability to AC systems. This issue helps expose the networking and Computational Intelligence communities to a variety of new and highly challenging problems along with the feasible solutions designed within the realm of AC systems.
Within this context, original contributions are solicited in all relevant areas, including but not limited to:
a.. Tools and techniques of adaptive learning for designing, analyzing and building AC systems and networks b.. Adaptive security for self protection of AC systems and networks c.. Enabling technologies for self-managing systems and networks including Service-oriented Architectures, Web Services, Web personalization, Web content retrieval, XML, Peer-to-Peer and Open Grid Services. d.. Economic, biological and social models used for autonomic communications e.. Scalable routing algorithms and novel admission control f.. Network access technologies g.. New architectures for QoS guarantees in real time AC applications h.. Human interaction with AC systems and User-centric evaluation of AC systems i.. Advanced information processing techniques for AC including policies, context-awareness, machine learning, and optimization techniques j.. Sensing, monitoring and measurements for self-managing systems and networks k.. Securely programmable environments for autonomic communications systems l.. Policy-based communication and policy multiplexing in AC Guest Editors
Dr.Athanasios Vasilakos, Dr.Witold Pedrycz,
Submission deadline: Dec. 1, 2006 . Papers (pdf files) should be submitted electronically to both Guest Editors:
Athanasios Vasilakos vasilako@ath.forthnet.gr
Witold Pedrycz pedrycz@ee.ualberta.ca
Authors should prepare manuscripts, no longer than 12000 words, according to the ACM accepted manuscript preparation guidelines.