Journal Information
Computer Communications
http://www.journals.elsevier.com/computer-communications/
Impact Factor:
2.816
Publisher:
Elsevier
ISSN:
0140-3664
Viewed:
20357
Tracked:
60
Call For Papers
Computer and Communications networks are key infrastructures of the information society with high socio-economic value as they contribute to the correct operations of many critical services (from healthcare to finance and transportation). Internet is the core of today's computer-communication infrastructures. This has transformed the Internet, from a robust network for data transfer between computers, to a global, content-rich, communication and information system where contents are increasingly generated by the users, and distributed according to human social relations. Next-generation network technologies, architectures and protocols are therefore required to overcome the limitations of the legacy Internet and add new capabilities and services. The future Internet should be ubiquitous, secure, resilient, and closer to human communication paradigms.

Computer Communications is a peer-reviewed international journal that publishes high-quality scientific articles (both theory and practice) and survey papers covering all aspects of future computer communication networks (on all layers, except the physical layer), with a special attention to the evolution of the Internet architecture, protocols, services, and applications. Topics include, but are not limited to:

    Emerging technologies for next generation network
    LAN/WAN/MAN
    Future Internet architecture, protocols and services
    Content- and service-centric architecture
    Mobile and ubiquitous networks
    Self organizing/autonomic networking
    Green networking
    Internet content search
    QoS and multimedia networking
    Opportunistic networking
    On-line social networks
    Internet of things
    Public safety communication networks
    Network applications (web, multimedia streaming, VoIP, gaming, etc.)
    Trust, security and privacy in computer and communication networks
    Modeling, measurement and simulation
    Complex network models
    Internet socio-economic models
    Experimental test-beds and research platforms
    Algorithmic aspects of communication networks
    Network scaling and limits
Last updated by Dou Sun in 2021-03-14
Special Issues
Special Issue on Deep Reinforcement Learning for Future Wireless Network Virtualization
Submission Date: 2021-06-15

Providing wireless network virtualization is a promising idea that has the potential to alleviate spectrum congestion and open up new network services. The paradigms differ with the degree of virtualization and sharing of resources. Each paradigm involves technological and non-technical challenges that must be resolved before a widespread technology becomes wireless virtualization. These problems require careful design and assessment for the virtualization of wireless networks to be a success. The design of future wireless networks needs to satisfy numerous criteria for Quality of Service (QoS). From wired to wireless networks, virtualization has been expanded. It enhances efficiency and utilisation and allows multi-tenancy and customised service with broader range of carrier frequencies. Due to the dynamic and unpredictable network status and heterogeneity of wireless users in IoT environment, network control problems are very challenging as the dimensionality and computational complexity rapidly increase. Also in the wireless domains such as WiFi, cellular network and wireless Internet of Things (IoT), new architectures involving virtualization have been evolving. In infrastructure-based wireless networks, relatively little virtualization has occurred, but the concept of virtualizing wireless access is gaining attention as it has the potential to enhance the use of spectrum and perhaps create new services. Virtualization of the wireless network requires both sharing of resources and spectrum sharing. Therefore, rational planning and resource allocation to provide entire network architecture, the QoS promised by each SP, mobility management and the spatial-temporal variations of traffic, cross INP signaling and location tracking contributes to vital research areas in Wireless Network Virtualization. Deep Reinforcement Learning (DRL) has been developed by the use of Deep Neural Networks (DNNs) as a potential approach to solve high-dimensional and continuous control issues effectively. Deep Reinforcement Learning techniques provide great potential in IoT, edge and SDN scenarios and are used in heterogeneous networks for IoT-based energy management based on the QoS required by each Software Defined Network (SDN) service. Current research focuses on the implementation and validation of the Software-Defined Networks and Network Function Virtualization in Global Edge Computing Architecture, developing intelligent mechanisms that allow automated and dynamic management of the virtual communications established in the SDNs by user nodes. While DRL has shown great potential to solve emerging problems in complex wireless network virtualization, there are still domain-specific challenges that require further study, including the design of adequate DNN architectures with 5G network optimization issues, resource discovery and allocation, developing intelligent mechanisms that allow the automated and dynamic management of the virtual communications established in the SDNs which is considered as research perspective The objective of this specific issue is to explore recent developments in DRL and address practical challenges in Wireless Network Virtualization that promote researchers to present their research on the innovative DRL system, network modelling and architecture, technical challenges in terms of instantiation, operation and management of wireless network virtualization. Original research and review articles in this area are encouraged in the following topic areas including, but are not limited to: Wireless Virtualization and dynamic spectrum management using DRL Experimentation and simulations of DRL in Network communications Channel allocation algorithms considering QoS for Mobile Network Virtualization Addressing Physical Layer issues Wireless communication in using DRL Improved Service Provisioning in Wireless Virtualization Enabled Networks Spectrum Sharing in Virtualized Networks DRL for Performance analysis in Dynamic spectrum resource allocation in Wireless Network Virtualization Software-Defined Networks and Edge Computing over IoT : Challenges and Issues Wireless Virtualization and evaluation of virtual architectures Application of DRL for power rate control, traffic shaping and Scheduling DRL for identifying network security threats and vulnerabilities in SDN Virtualization via Software Defined Radio (SDR) DRL in mobile edge computing, wireless caching, and mobile data offloading DRL in Fault detection, auto-diagnosing and Network forensic applications Performance Analysis on efficient network access and channel utilization using DRL Tentative SI Timeline: Manuscript Submission Deadline Date: 15, June 2021 Authors Notification Date: 20, August 2021 Revised Papers Due Date: 25, November 2021 Final notification Date: 05, February 2022 Guest Editor Details: Lead Guest Editor: Dr. Tu Nguyen Assistant Professor, Department of Computer Science, Purdue University Fort Wayne, Fort Wayne, USA. Email: nguyent@pfw.edu URL: https://users.pfw.edu/nguyent/
Last updated by Dou Sun in 2021-03-14
Special Issue on Network Management in Beyond 5G/6G Networks
Submission Date: 2021-06-20

Current ongoing deployment of 5G is regularly exposing the integral limits of the system compared to what promised during the last decade such as Ultra-Reliable Low-Latency Communication (URLLC). This pushes the research community to focus on bringing in new, innovative, relevant technologies keeping the vision of 5G beyond/6G networks intact. Futuristic services such connected Robots, autonomous vehicles, e-health, trustable food supply chain mechanisms, entertainment services broadcasting (Netflix, Amazon Prime) etc. will be highly dependent on instantaneous, virtually limitless wireless connectivity. To bring in and make super-fast connectivity sustainable, a number of research issues such as traffic congestion, URLLC, secure and trustable platforms, interference management, integrating innovative technologies are some of key subjects that need to be explored. In this particular workshop, our focus would be using some core key technologies (Quantum Internet, Blockchain, Machine Learning, Artificial Intelligence etc.) to minimize traffic congestion in the 5G Beyond/6G networks as it is predicted that 10, 000 times traffic will Increase in the next decade. The proposed workshop is second in its series, and we expect a number of quality submissions due to the need of quality research in this direction. Topics will include diverse set of areas related to 5G/6G but not limited to: · Congestion Control using Network Slicing, SDN and NFV · Advanced Machine learning, Deep learning, and AI solutions to improve traffic congestion · Congestion control techniques in improving user experience in Internet of Things (IoT), Cloud, Edge Computing Networks · Quality of Service (QoS) issues such as Dynamic Resource Allocation, Spectrum Allocation, Energy Efficiency · Futuristic paradigms for advanced use cases; adopting blockchain, quantum communication etc. · Handling Traffic congestion in social networks · Parameters like Interoperability, heterogeneity, and bandwidth in congested networks · Unmanned Aerial Vehicle (UAVs)/Internet of Drones with focus on Traffic Congestion · Emerging cellular architectures for Traffic Congestion · Massive MIMO/Cell free Massive MIMO for Traffic Congestion · Internet Traffic Offloading Mechanisms using AI, Blockchain, Machine Learning · Zero Touch Network for beyond 5G/6G Network · Autonomous Networks for Beyond 5G/6G Networks
Last updated by Dou Sun in 2021-01-01
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