Journal Information
Computer Communications
http://www.journals.elsevier.com/computer-communications/
Impact Factor:
2.766
Publisher:
Elsevier
ISSN:
0140-3664
Viewed:
16870
Tracked:
52

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 2020-02-25
Special Issues
Special Issue on Optimization of Cross-layer Collaborative Resource Allocation for Mobile Edge Computing, Caching and Communication
Submission Date: 2020-05-31

With the rapid development of mobile communications and the explosive usage of mobile devices (i.e., smart phones, laptops, tablets, etc.), the mobile Internet facilitates us with a pervasive and powerful platform to provide more and more emerging applications. However, many mobile devices usually have limited computation capabilities and battery power. Migrating computational tasks from the distributed devices to the infrastructure-based cloud servers has the potential to address the aforementioned issues. The cloud servers are always located in the center of core network and far away from the users, which may cause delay fluctuation and additional transmission energy cost. Mobile Edge Computing (MEC) is an emerging paradigm which pursues to provide better services by moving infrastructure-based cloud resources (computation, storage, bandwidth and et al) to the edge of the network. MEC is rapidly becoming a key technology of 5G, which helps to achieve the key technical indicators of 5G business, such as ultra-low latency, ultra-high energy efficiency and ultra-high reliability. Differ from the traditional cloud, MEC is close to the mobile users, which can reduce the access delay and the cost of using the cloud service. However, we are facing many challenges for scheduling the limited and heterogeneous MEC resources (computation resource and network resource). Firstly, how to implement a cross-layer optimization policy for MEC that jointly optimizes the application layer, network layer, data link layer, as well as physical layer of the protocol stack using an application-oriented objective function while satisfying the different user service requirements (i.e., energy saving, reducing execution delay, reducing price, and et al) is very essential. Secondly, a theoretical framework of cross-layer optimization to balance the efficiency and fairness of resource allocation of MEC, as well as maximize the profit of MEC service providers needs to be proposed. Thirdly, how to design cross-layer collaborative distributed resource management systems that meet the harsh requirements of MEC such as latency, scalability and mobility support, also needs to be considered. In addition, it is also essential to jointly optimize the resource allocation of computation and communication of both the mobile users and the MEC service provider to minimize the total energy consumption subject to the users’ latency constraint. It is highly expected that “mobile edge computing (MEC)” will be a key technology playing the most important role in 5G and future network. More importantly, it can improve user experience and user service quality. In addition, as pointed out in the section 1, the topic of joint cross-layer collaborative resource optimization for mobile edge computing is not only important but also faces many challenges. Therefore it is necessary to address them to come true the MEC based evolution in our life. This topic is very promising and will attract great interests from readers, including researchers from academia and industry, general readers, mobile application developer as well as students who are engaged in this study. This Special Section in Computer Communications is inviting researchers to report the-stated-of-the-art advances in joint cross-layer collaborative resource optimization. Authors are invited to submit original practical work and survey papers. Topics of interest include (but are not limited to): Theoretical modeling and performance analysis of resource optimization for MEC Joint cross-layer resource allocation for MEC New integration resource management architecture of cloud, MEC and user Cross-layer service discovery and service recommendations for MEC Multi-user computation offloading for MEC Multi-edge-server collaboration for MEC Delay minimization service provision for MEC Cross-layer collaborative distributed systems for MEC Cross-layer collaborative MEC Applications, such as smart city, smart grid, and Intelligent Transportation Systems Software-defined MEC Software-defined offloading for MEC Mobility management for MEC Security, privacy, and trust of MEC MEC for vehicular networks We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.
Last updated by Dou Sun in 2019-11-24
Special Issue on Scalable and Secure Platforms for UAV networks
Submission Date: 2020-06-30

Future UAV-based networks are required to provide high levels of data-rates, security, range, and dynamicity. In this context, operating UAVs using the upcoming tactile internet environment and low latency 5G networks can solve the problem of network coverage and data-rates. Furthermore, the use of Software Defined Networking (SDN), Network Function Virtualization (NFV), and Intent-based Networking (IBN) can solve the issue of dynamic network management. The applications of UAVs are rapidly increasing in almost all civilian domains. Air taxis, Food drones, drones for medicine delivery are some of the major civilian applications of UAVs. In such applications, it is imperative to verify the authenticity and operations of the UAVs in real-time for which Blockchain technology is a highly promising solution. The power-constraints of UAVs can be addressed by recharging batteries on-the-go with solutions such as solar panels or wireless charging. Additionally, better algorithms can be implemented to make the UAV computations more energy efficient. Most practical applications of UAVs would generally require a swarm of many drones rather than a single drone. Proper management, cooperation, and autonomy of such swarms would require various Artificial Intelligence and Machine Learning algorithms. The geographic range of operations, clearance of access to civilian and military airspaces, network coverage, duration of flight, security requirements, autonomy, among many others, are some of the issues which must be addressed before such applications can become a reality. Broadly, these fall under the category of scalability and security. This special issue is to explore application-specific UAV platforms that employ novel techniques for multiple new applications that are both scalable and secure. Potential topics of interest include, but are not limited, to the following ones: Secure 5G UAV communications Secure UAV-based edge-computing networks Software Defined 5G UAV networks Lightweight Blockchain Security for UAV networks Scalable Automation in UAV networks Securing Wireless Charging transactions in UAV networks Scalable Wireless Charging platforms for UAV networks Energy-aware SDN for UAV networks. Lightweight cryptographic primitives for UAV networks Intelligent resource management for UAV networks 5G dark-zone coverage using UAV networks UAV platforms for cellular blackouts Scalable solutions to physical UAV-tampering Unauthorized UAV detection and mitigation Battery and energy management in UAV-based networks
Last updated by Dou Sun in 2020-04-10
Special Issue on AI-Driven Sensing and Computing for Cyber-Physical Systems
Submission Date: 2020-07-01

The cyber-physical system (CPS) has been coming into our view and will be applied in our daily life and business process management. The emerging CPS must be robust and responsive for its implementation in coordinated, distributed, and connected ways. It is expected that future CPS will far exceed today’s systems on a variety of characteristics, for example, capability, adaptability, resiliency, safety, security, and usability. With the rapid development of computing and sensing technologies, such as ubiquitous wireless sensor networks, the amount of data from dissimilar sensors and social media has increased tremendously. Conventional data fusion algorithms such as registration, association, and fusion are not effective for massive datasets. New research opportunities and challenges for content analysis on CPS networks have arisen. Making sense of these volumes of Big Data requires cutting-edge tools that can analyze and extract useful knowledge from vast and diverse data streams. Current research in Intelligent Sensing addresses the following issues: AI-Driven Sensing as a novel methodology for user-centered research; development of new services and applications based on human sensing, computation, and problem solving; engineering of improved AI-Driven Sensing platforms including quality control mechanisms; incentive design of work; usage of Participatory Sensing for professional business; and theoretical frameworks for evaluation. This is opening a vast space of opportunities to extend the current networks, communications, and computer applications to more pervasive and mobile applications. The purpose of this SI is to provide a forum for researchers and practitioners to exchange ideas and progress in related areas. In this special issue, we invite articles on innovative research to address challenges of Analytics and Applications on AI-Driven Sensing and Computing for Cyber-Physical Systems. Topics of interests include, but are not limited to: Distributed processing for data sensor data in CPS networks Approximate reasoning and pattern recognition for CPS networks AI in mobile networking AI-Driven analytics for social media-sensor data integration AI platforms for efficient integration with CPS networks Virtualized and cloud-oriented resources for big data processing for CPS networks machine learning algorithms for CPS networks Visual analytics on CPS networks
Last updated by Dou Sun in 2020-03-06
Special Issue on Industrial communication networks in smart factory 4.0
Submission Date: 2020-08-30

With the rapid development of electronics, information technology and advanced manufacturing technology, the production mode of manufacturing enterprises is shifting from digital to intelligent. These exponentially growing developments have accelerated the emergence of a new era of manufacturing that combines virtual reality technology based on the Cyber-Physical Systems (CPSs). Emergence of such an intelligent manufacturing technology gives the ability to respond rapidly to design changes and innovation, which has a huge competitive advantage over traditional manufacturing processes. Europe 2020 strategy, Industry 4.0 strategy and China manufacturing 2025 have been proposed as a direct result of countries focusing their attention on this new technology. United States has gradually accelerated the speed of reindustrialization and manufacturing reflow. The transformation of intelligent manufacturing systems will have a profound and lasting worldwide impact on the future of manufacturing. Industry 4.0 settings need to handle challenges with ICT tools, cost efficiency, fault tolerance, autonomous decision-making, full lifecycle traceability control, business-intelligence capabilities, new forms of human-machine interaction, higher capacities to handle data processing, energy-efficiency demands, and cooperative tasks. The use of information communication technologies such as Internet of things (IoT), augmented and virtual reality, fog and edge computing, together with wireless sensor networks, will enable novel cyber-secured, resilient, human-centric, and context-aware applications to face these upcoming challenges. Potential topics include, but are not limited to: Architecture and protocol design for smart factory 4.0 Resource management in industrial IoT systems 5G for future industrial automation Security, safety and privacy issues in industrial wireless networks and applications Performance evaluation, simulation, RF measurements, and modeling of industrial IoT systems Intelligent Machine to machine communications in industrial IoT Cognitive Industrial systems Cloud-based industrial internet of things solutions
Last updated by Dou Sun in 2020-03-18
Special Issue on Secure Artificial Intelligence in Mobile Edge Computing
Submission Date: 2020-08-31

As a key 5G enabler technology, mobile edge computing (MEC) has emerged in recent years as a new computing paradigm that provides end-users with low latency in their access to applications deployed at the edge of the cloud. Many artificial intelligence (AI) applications powered by machine learning demand low latency, e.g., smart assistant, driverless cars, smart manufacturing, etc. The integration of mobile edge computing and AI unlocks unlimited possibilities in people’s daily lives. In the mobile edge computing environment, computing resources are provisioned and managed in a decentralized manner. Accordingly, artificial intelligence applications are deployed in a decentralized manner. However, unlike the centralized cloud computing environment, there is a lack of security techniques and mechanisms specifically designed for the devices and servers operating in the mobile edge computing environment. This further complicates many of the security issues in artificial intelligence applications, e.g., data privacy attacks, adversarial attacks, confidentiality attacks, etc. In this special issue, we look for significant findings in tackling new security issues that challenge artificial intelligence in the mobile edge computing environment. Specifically, we solicit novel contributions on secure artificial intelligence from a variety of perspectives, e.g., architecture, data, algorithms, etc. Potential topics include but are not limited to the following: Advanced AI algorithms for MEC security AI-powered privacy protection in MEC Intelligent data preprocess, communications and integration in MEC Attacks vs. Countermeasures in MEC applications AI-based MEC architecture design AI-based energy efficient networking techniques for MEC Smart sensor networks and IoT applications AI-based network resource allocation and optimization in MEC The design of AI-enabled hardware aspects in MEC Distributed and decentralized signal processing via AI algorithms Trust, reliability and dependability in MEC Encryption, Signature and Forensics for MEC applications Important Dates: Deadline of Submissions: 31 August 2020 Notification of First Round: 31 November 2020 Submission of revision: 1st March 2021 Acceptance: 1st May 2021 Guest Editors ● Prof. Lianyong Qi (Managing Guest Editor), Qufu Normal University, China. ● Prof. Qiang Ni, Lancaster University, UK. ● Prof. Shui Yu, University of Technology Sydney, Australia. ● A/Prof. Gautam Srivastava, Brandon University, Canada. Submission Guidelines Please visit https://www.evise.com/profile/#/COMCOM/login to submit your manuscript. To ensure that all manuscripts are correctly identified for inclusion into the special issue, please select "SI: SecAI-MEC" when you reach the Article Type step in the submission process. For further information, please contact the guest editors.
Last updated by Dou Sun in 2020-04-15
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