Aims and Scope

Concurrency and Computation: Practice and Experience (CCPE) publishes high-quality, original research papers, and authoritative research review papers, in the overlapping fields of:

    Parallel and distributed computing;
    High-performance computing;
    Computational and data science;
    Artificial intelligence and machine learning;
    Big data applications, algorithms, and systems;
    Network science;
    Ontologies and semantics;
    Security and privacy;
    Cloud/edge/fog computing;
    Green computing; and
    Quantum computing.

Emphasis on novel research related to practice and experience in these areas should be an essential aspect of contributions, rather than addressing theoretical aspects. Submissions should involve or imply significant concurrency and/or computational issues. Within these broad areas, the scope of CCPE includes the design, implementation, and optimization of compute and data-intensive applications for parallel and distributed systems. This includes the development of novel concurrent algorithms and applications, their parallel performance analysis and modelling, and new programming or modelling languages and relevant methodologies for composing them. Areas relevant to compute and data-intensive applications include, but are not limited to, large-scale computational science, artificial intelligence, and the processing of voluminous datasets from satellites, scientific experiments, sensor networks, medical instruments, and other sources. Techniques for resource management in the context of parallel and distributed systems, and energy-aware computing are also topics of interest.

Special Issue on IDC 2024
提出日: 2024-12-31

IDC 2024 provides an open forum for enhancing the collaboration between researchers, lecturers, and students from Intelligent Computing and Distributed Computing communities. Intelligent Computing covers a hybrid palette of methods, techniques and their applications ranging from classical artificial intelligence, information and data sciences, multi-agent technologies or computational intelligence to more recent trends such as swarm intelligence, bio-inspired computation, cloud computing, machine learning or social/cyber-physical trust and security. Distributed Computing develops methods and technology to build complex computational systems composed of collaborating software components scattered across diverse computational elements.

The scope of the current special issue is broad and representative of the multidisciplinary nature of Intelligent, Distributed Computing, including methodological, technology, application, and system papers. The topics covered include, theoretical advances, methodological advances based on formal models and architecture framework, knowledge compilation, problem encodings and reformulations, practical advances in distributed algorithms and tools, case studies based on reproducible simulations, laboratory and field tests, standardization issues in intelligent distributed systems applications. This special issue will be based on extended versions of the best papers selected from the 17th International Symposium on Intelligent Distributed Computing (IDC2024), Brighton, United Kingdom, September 9-11, 2024, https://blogs.brighton.ac.uk/idc2024/

It is expected that the submissions will address the topic of the conference and contain at least 50% new content regarding any previously published paper. Authors are encouraged to submit high-quality, original work that has neither appeared in, nor is under consideration by, other journals. Authors not attending the conference are also welcome.

Topics for this call for papers include but not restricted to:

    Distributed swarm robotics systems
    Distributed / Decentralized / Federated Machine Learning
    Ephemeral and Unreliable computing
    Intelligent Distributed Applications
    Intelligent Distributed and High-Performance Architectures
    Intelligent Distributed Knowledge Representation and Processing
    Intelligent Distributed Ledgers, Blockchains and AI
    Intelligent Energy Systems
    Intelligent Production Systems, Intelligent Transportation Systems
    Machine Learning Methods for Distributed Systems
    Nature-inspired methods for supervised and unsupervised data mining
    Networked Intelligence, Organization and Management
    Parallel metaheuristics for optimization
    Smart City Applications, Smart Grid Applications

Guest Editors:

Nikolaos Polatidis
University of Brighton
United Kingdom

Elias Pimenidis
University of the West of England
United Kingdom

Keywords: Intelligent Distributed Applications; Intelligent Distributed and High-Performance Architectures; Intelligent Distributed Ledgers; Blockchains and AI; Machine Learning Methods for Distributed Systems