Journal Information
Computer Communications
http://www.journals.elsevier.com/computer-communications/
Impact Factor:
2.613
Publisher:
ELSEVIER
ISSN:
0140-3664
Viewed:
14316
Tracked:
45

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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 2019-02-02
Special Issues
Special Issue on Software-defined disaster area UAV communication networks for extreme surveillance
Submission Date: 2019-11-30

The monitoring ability of emerging technologies and applications to make them very suitable for extreme surveillance systems. A large number of applications related to extreme events, especially natural disasters, agriculture, water, forest, military, buildings, health monitoring, disaster relief & emergency management, area and industrial surveillance have already been studied from the emerging technologies perspective and most of these surveillance applications have attracted much research attention. Emerging technologies is increasingly becoming the most important and valuable source for insights and information in extreme events. It covers from everyone’s experiences to everything happening in the world. There will be lots of emerging technologies in extreme events surveillance video, disaster images, social media, voice and video, to name a few, only if their volumes grow to the extent that the traditional processing and analysis systems may not handle effectively Unmanned Aerial Vehicles (UAVs) empower people to reach endangered areas under emergency situations. By collaborating with each other, multiple UAVs forming a UAV network (UAVNet) could work together to perform specific tasks in a more efficient and intelligent way than having a single UAV. UAV Nets pose special characteristics of high dynamics, unstable aerial wireless links, and UAV collision probabilities. UAV networks may vary from slow dynamic to dynamic; have intermittent links and fluid topology. While it is believed that ad hoc mesh network would be most suitable for UAV networks yet the architecture of multi-UAV networks has been an understudied area. Software Defined Networking (SDN) could facilitate flexible deployment and management of new services and help reduce cost, increase security and availability in networks. Routing demands of UAV networks go beyond the needs of MANETS and VANETS. To address these challenges, we propose a special issue aims to gather latest research and development achievements in the field of software-defined disaster area UAV communication networks for extreme surveillance. Original papers that address the most current issues and challenges are solicited. Topics of interest include, but are not limited to: • Software defined radio test beds and experiments for extreme surveillance • Disaster resilient location detection protocols for extreme events. • Multi-hop and relay based communications for extreme surveillance • Location detection technologies and protocols for extreme surveillance applications • Software defined networks for architecture for under water search and surveillance applications • Software defined networks for efficient Internet of multimedia things smart surveillance applications • Software defined networking for traffic engineering measurement and management • Software defined networking for cybersecurity applications • Software defined extreme scale networks for bigdata applications • Software defined networks with QoS for cloud applications • Interaction, access, visualization of intelligent tools for extreme events • Intelligent -oriented middleware for extreme-critical applications • Quality of service(QoS) and priority aware models for extreme surveillance applications
Last updated by Dou Sun in 2019-06-28
Special Issue on Intelligent Green Communication Networks for 5G and Beyond
Submission Date: 2019-11-30

Green communication networks, with a focus on energy efficiency, is an emerging technological trend of great significance. These networks can significantly enhance sustainability for 5G and beyond networks with regard to power resources and environmental conditions. However, the high-density deployment of base stations and the exponentially increasing use of sensors and actuators in 5G and beyond networks, will lead to significant energy consumption. Thus, reducing carbon footprint in green communication networks is a key challenge facing researchers in academia and industry. Due to the growing use of artificial intelligence (AI) in this area, several green communication approaches are entering a more mature phase, with exciting applications in various networks. Moreover, the information sharing and intelligent decision-making capabilities help recent green communication networks play an important role in improving not only energy efficiency but also network performance. For instance, a simple and effective green communication solution is to place a device in intelligent sleep mode; this is achieved with the help of various MAC protocols with broad applications in wireless networks. However, it is essential to investigate the trade-off between the energy efficiency for green communication networks, and the network requirements. Moreover, it is crucial to evaluate the performance concerning the energy consumption, the throughput, and the response time, regarding 5G and beyond networks. This Special Issue on Artificially Intelligent Green Communication Networks for 5G and Beyond in Computer Communications solicits submissions of high-quality and unpublished articles that aim to address the technical problems and challenges concerning green communications networks. In particular, we seek submissions, which efficiently integrate novel AI approaches, focusing on network performance evaluation across existing green communication solutions. Both theoretical and experimental studies for artificially intelligent green communication networks scenarios are encouraged. The topics of interest include, but are not limited to: Power consumption trends and reduction in intelligent communications. Machine learning approaches for energy-aware green wireless communication networks. AI based modeling and analysis for green communications. Carbon-neutral intelligent communication networks. Architectures and models for smart green communication networks. Quality of service in smart green communication networks. Intelligent green communication network designs and implementations for green infrastructures. Experimental test-beds and results for artificially intelligent green communication networks.
Last updated by Dou Sun in 2019-07-27
Special Issue on Advanced Computing and Communication Technologies for Internet of Drones
Submission Date: 2019-12-01

Unmanned Aerial Vehicles (UAVs) which are commonly known as drones can be potential enablers for providing different type of solutions in a futuristic smart city. Initially, drones were restricted to sophistic areas like military operations due to their cost and limited technological advancements. But, nowadays, the advent of more affordable technologies are enabling drones in finding their way to our day to day usage application areas like logistics, remote monitoring, cinematography, agricultural monitoring, search and rescue, and 3D-mapping. The development on Internet of Drones (IoD) is enabling the design of new architectures, standards and technologies to make the drone operations as autonomous as possible. A few of the thrust areas of IoD research are computing, communication, security and privacy, energy and sustainability. The limited computing resources available with the drones so as to keep it lightweight must be utilized effectively in terms of processing, computing, sensing and analyzing. For this reason, in order to enable long operating durations during the flight, efficient mechanisms must be designed to optimize the computing processes and needs of the drones. The limited battery capacity hinders the progression of drones by limiting the flight time. However, the energy-efficient computing procedures and mechanisms can reduce the tendency of energy dissipation thereby elongating the flight times during hover. Moreover, the latest innovations in terms of energy harvesting, wireless energy transfer and solar plates has opened new doors for opportunistic energy replacement during the hover time of the drones. The deployment of drones in sensitive and consumer related applications make it necessary to design sustainable and eco friendly solutions which could utilize the limited computing facilities of drones to provide enhanced quality of services to the users. Although the distributed nature of IoD architecture provides various advantages in terms of scalability and autonomy, but it bring forward various mobility and controlling issues creating the need of special attention to the communication platforms also. The conventional network systems may not be very efficient in dealing with the dynamic requirements of IoD. Latest network paradigms such as software defined networking and network function virtualization provides new potentials of technological advancements in IoD communication architecture. Moreover, the next generation technologies such as-5G and NOMA also have shown a promising trends in the design of advanced communication solutions for IoD. As the drones are generally deployed in tough environments and terrains, therefore it is quite essential to provide a strong network and communication backbone. Otherwise, it may lead to adverse effects on the designed solutions or applications dependent on the drones. Security and privacy are yet another areas of concern for drones which needs focus of the research community as an attack on the IoD can be fatal and may lead to loses of lives and assets. There may be several solutions related to security and privacy but the applicability of these solutions with IoD perspective is questionable. Therefore, the applicability of those solutions in IoD needs to be analyzed keeping in view the nature of applications, limited computing and communication capabilities and the energy and sustainability constraints of drones. Moreover, the deployment of drones in consumer applications dealing with product delivery and logistics make it a compulsion to preserve the identity of users and the sensitive information associated with them. Blockchain can be a viable solution for preserving the data integrity and securing the entire transactional process involved. But, the limited computing capabilities require lightweight solutions which right now are not feasible in the available blockchain variants. Moreover, new cryptographic primitives need to be designed to protect and preserve the IoD from adversaries and attackers. Keeping in view of the need of technological advancements in different enabling domains related to IoD, this special issue provides a platform to the research, academia and industrial technocrats to present their ideas and solutions from various perspectives related to drones. Thus, for this special issue , the deeply investigated works describing both theoretical and practical evaluations related to the design, analysis and implementation of secure and sustainable computing and communication technologies for IoD are invited. Some of the desired topics include, but are not limited to the following: Cloud and big data technologies for drone networks. Edge as a service for drones in smart communities. Implementation and deployment of real-time test beds for 5G-drone setups. Data collection, analytics, processing and delivery using drones in smart city. Distributed caching and security protocols for inter-drone communications. Content dissemination and named data networks for smart communities. Advanced caching and content delivery technologies for IoD Security, integrity and privacy solutions for IoD Energy-aware and sustainable solutions for Inter-drone communications. Energy-efficient computing, processing and analysis in IoD ecosystem. Energy harvesting and wireless energy transfer for drones. Trust management and collaborative solutions for IoD. Lightweight blockchain solutions for IoD applications. Smart contracts and consortium Blockchains for drone enabled consumer applications. Software defined networks and network service chaining for IoD. Artificial Intelligence in IoD operations and autonomy. Artificial intelligence in traffic management in IoD. Machine Learning and deep learning algorithms for IoD. Mobility management and channel modeling in IoD. IoT architectures and protocols of drone communication.
Last updated by Dou Sun in 2019-06-28
Special Issue on Machine Learning approaches in IoT scenarios
Submission Date: 2019-12-01

It is foreseen that by 2020 the total number of Internet-connected devices being used will be between 25 and 50 billion. As technologies become more mature, the number of connected devices will keep increasing and the consequent amount of data published will keep overwhelming our computing systems. On the other hand, development of innovative hardware, software and communication technologies fostered the emergence of Internet connected sensor devices which observe the physical world and provide data measurements. This Internet of Things (IoT), thus, keeps on enriching and providing interaction between the cyber and the physical world. The increased volume of big data produced within the IoT requires intelligent processing and analysis of this data to support smart and scalable IoT applications. Accordingly, machine learning represents an effective tool to deal with the challenges posed by IoT scenarios. Different machine learning techniques and approaches can be introduced to make the network more intelligent and extract relevant information from the big data. Keeping in mind the need for technological advancements in different enabling domains related to big data processing and filtering in IoT, this special issue provides a platform to the research, academia and industrial technocrats to present their ideas and solutions from various perspectives related to use of machine learning in the challenging IoT scenarios. This special issue will be devoted to both theoretical and practical evaluations related to the design, analysis and implementation of machine learning techniques for IoT. Some of the relevant topics include, but are not limited to the following: Machine learning/deep learning techniques for smart systems (smart buildings, smart cities, smart transportation, smart healthcare) Supervised, Unsupervised and Reinforcement learning for IoT, drones, WSN networks Machine learning/deep learning applied to IoT Applications Reasoning/learning and techniques applied IoT Data Management Machine learning/deep learning for IoT protocol design and optimization Machine learning for energy efficiency in IoT systems Self-Learning and adaptive networking protocols and algorithms Machine learning in sliced network control & management Experimental evaluation of learning systems
Last updated by Dou Sun in 2019-07-27
Special Issue on Enabling Cognitive Smart Cities: Security and Privacy in IoT applications (CSCIoT)
Submission Date: 2019-12-30

A smart city is a label given to a city that incorporates Information and Communication technologies (ICT) to enhance the quality and performance of urban services such as energy, transportation and utilities in order to reduce resource consumption, wastage and overall costs. The main objective of a smart city is to enhance the quality of living for its citizens through smart technologies like the Internet, telecommunications network, broadcast network, wireless broadband network and other sensors networks where high-performance computing, computing intelligence, cloud computing as well as IoT is at its core. With the explosive growth of smart city, IoT and high-performance computing based services receive increasing attention. It creates many scientific and engineering challenges that call for ingenious research efforts for the development of efficient, scalable, and reliable communication and network technologies. In particular, internet of things (IoT) with cloud computing plays an vital role to connect everything and to the Internet through specific protocols for information exchange and communications, achieving intelligent recognition, location, tracking, monitoring and management. IoT with cloud-based smart cities can provide various kinds of services for both the citizens and the administrators. For example, smart homes, smart parking lots, weather systems, vehicular traffic, environmental pollution, surveillance systems, smart energy, and smart grids. One of the biggest concerns, while using any of the IoT based applications is the safety and privacy of individual users. Any breach in the security leads to confidential data loss, which further leads to financial loss. Any such security breach leads to compromise of an individual’s privacy. As many devices are connected, the security risk gets increase a lot in IoT based smart city applications. Future cities are to be not only an intelligent and green living environment but also providing human-centric public services at a lower cost. The IoT, artificial intelligence, cloud computing, computational intelligence, and high-performance computing are four pillars for smart cities enablement and define the transition from smart to cognitive. Cognitive smart city refers to the convergence of emerging IoT and smart city technologies, their generated big data, and artificial intelligence techniques to predictive, proactive real-time adaptations over suggestion management. Smart city implementation may include implementation of - Smart energy grid, Intelligent buildings, smart water management, connected healthcare and patient monitoring, environment/climate monitoring, connected cars, and smart transportation, intruder detection using home Wi-Fi router, vehicular network for traffic control, and video surveillance with intelligent recognition, location tracking, monitoring and management. A consistent citizen enagagement, uniquitous data collection, and sophisticated analytics can lead to the shift to produce the best kind of cognitive city. The motivation of the special issue is to invite research scholars, academic researchers and industry professionals to submit their research ideas and implementations related to artificial intelligence and deep learning for the security and privacy in computer, IoT and communication networks. Topics are as below but are not limited to: Security issues in smart city Deep Learning for IoT IoT architectures, protocols, and algorithms for applications Smart education and health services Event alert and prediction in smart cities Intelligent data processing Smart software application development Machine Learning techniques for smart cities AI based data generation and management High-performance computing for smart cities Privacy in communication network AI-based solutions for smart city Cyber-physical systems and society Reliability, security, privacy and trust
Last updated by Dou Sun in 2019-08-24
Special Issue on Smart Green Computing for Wireless Sensor Networks
Submission Date: 2020-02-28

The past couple of decades have substantiated the aggrandizement of Wireless Sensor Networks (WSNs) in academia and industry. In the WSN, numerous sensor nodes are deployed and networked to perlustrate a specified region, such that the inquisitive data can be sensed, processed, stored and collected. The physical world can be bridged to computing system via WSN, which constitutes the basis for developing advanced smart applications. Various possible applications of WSNs have been exploited in the realms of smart home, green buildings, environmental engineering, healthcare, industry, and military applications etc. To enable the pervasive deployment of WSNs, the major challenges are the incongruity between the diverse functionalities demanded by applications and the inadequate energy supply for sensor nodes. A retrogressive situation can occur for a large-scale network. Therefore, in this special issue (SI), we scrutinize WSNs focusing on green computing. On one hand, we solicit contributions on energy-efficient cross- layer protocols, to proliferate the network lifespan. Explicitly, to practice WSNs in real world applications, it is needed to analyze a tradeoff between system performance and energy efficiency, acclimatizing sensing/networking functionalities to energy budget. On the other hand, we stoop to probing new approaches to rationally supply energy to sensor nodes. For instance, sensor nodes fortified with energy harvesting mechanism from surrounding environment. The aforementioned techniques are skookum to improve the sustainability and performance of WSNs. Therefore, it is pivotal to utterly investigate how we run a WSN in a green manner. Topics of primary interest are including but not limited to the following: Architectures of Intelligent green computing technology for WSNs. Smart energy harvesting/charging and power management techniques. Long-life sensor node deployment and topology control. Energy-efficient smart computing protocol design. AI based scheduling algorithms for sensor networks. Energy-efficient sensing techniques. New applications of self-sustainable sensor networks. Smart data routing, processing and storage strategies. Network modelling and performance analysis. Artificial intelligence approaches for coordinating devices in WSNs. AI based resource orchestration in WSNs. Wireless sensor Network in Internet of Things State-of-the-art reviews on smart green computing technology trends for WSNs. Experimental results and test-beds for smart green computing systems for WSNs.
Last updated by Dou Sun in 2019-10-14
Special Issue on Intelligent Edge: When Machine Learning Meets Edge Computing
Submission Date: 2020-03-15

The explosion of the big data generated by ubiquitous edge devices motivates the emergence of a new computing paradigm: edge computing. It has attracted attention from both academia and industry in recent years. In edge computing, computations are deployed mainly at the local network edge rather than at remote central computing infrastructures, thereby considerably reducing latency and possibly improving computation efficiency. This computing model has been applied in many areas such as mobile access networks, Internet of Things (IoT), and microservices, enabling novel applications that drastically change our daily lives. As a second trend, a new era of Artificial Intelligence (AI) research has delivered novel machine learning techniques that have been utilized in applications such as healthcare, industry, environment engineering, transportation, smart home and building automation, all of which heavily rely on technologies that can be deployed at the network’s edge. Therefore, intuitively, marrying machine learning techniques with edge computing has high potential to further boost the proliferation of truly intelligent edges. In light of the above observations, in this special issue, we look for original work on intelligent edge computing, addressing the particular challenges of this field. On one hand, conventional machine learning techniques usually entail powerful computing infrastructures (e.g., cloud computing platforms), while the entities at the edge may have only limited resources for computations and communications. This suggests that machine learning algorithms or, at least, the implementations of machine learning algorithms, should be revisited for edge computing, which represents a considerable risk and challenge at once. On the other hand, the adapted deployments of machine learning algorithms at the edge empower the “smartification” across different layers, e.g., from network communications to applications. This in turn allows new applications of machine learning and artificial intelligence, opening up new opportunities. The goal of this special issue is to offer a venue for researchers from both academia and industry to present their solutions for re-designing machine learning algorithms compatible to edge computing, and for building intelligent edge by machine learning techniques, possibly revealing new, compelling use cases. Relevant topics include, but are not limited to: l System architectures of intelligent edge computing l Modeling, analysis and measurement of intelligent edge computing l Machine learning algorithms and systems for edge computing l Machine learning-assisted networking and communication protocols for or using edge computing l Intelligent mobile edge computing l Architectures, techniques and applications of intelligent edge cloud l Resource management for intelligent edge computing l Security and privacy of intelligent edge computing l Data management and analytics of intelligent edge computing l Intelligent edge-cloud collaborations l Programming models and toolkits for intelligent edge computing l Distributed machine learning algorithms for edge computing l Smart applications of edge computing
Last updated by Dou Sun in 2019-11-19
Special Issue on Autonomous Learning-Based Algorithm for Heterogeneous Cellular Networks
Submission Date: 2020-03-30

The spectrum bands of the multiple base stations comprise the sets of orthogonal wireless channels and spectrum usage scenarios the device to device pairs transmit over the dedicated frequency bands and the device to device pairs operate on the shared cellular channels. The goal of each device pair is to jointly select the wireless channel and power level to maximize its reward, defined as the difference between the achieved throughput and the cost of power consumption, constrained by its minimum tolerable signal-to-interference-plus-noise ratio requirements. We formulate this problem as a stochastic non-cooperative game with multiple players where each player becomes a learning agent whose task is to learn its best strategy and develop a fully autonomous multi-agent Q-learning algorithm converging to a mixed-strategy Nash equilibrium. The learning algorithm shows relatively fast convergence and near-optimal performance after a small number of iterations. Potential topics included, but not limited Reinforcement Learning for self organization and power control of two-tier heterogeneous networks · Optimal new site deployment algorithm for heterogeneous cellular networks· Energy cost minimization in heterogeneous cellular networks with hybrid energy supplies· Configuration algorithm for service scalability in heterogeneous cellular networks· Q-learning based heterogeneous network selection algorithm· Bayesian reinforcement learning-based algorithm for heterogeneous cellular networks· Machine learning paradigms for next-generation communication networks · Online distributed user association for heterogeneous radio access network
Last updated by Dou Sun in 2019-09-07
Special Issue on Internet of Things and Augmented Reality in the age of 5G
Submission Date: 2020-03-30

In the past few decades, people have made great efforts on the Internet of Things, which makes it possible or accessible to be applied in various fields, including home robotics, intelligent cities and Augmented Reality (AR). Therefore, these applications have captured the attention and enhanced aspirations of researchers in fields of machine vision, computer graphics and computer vision. The 5G network is a technology which is intrinsically featured with high bandwidth, ultra-low latency and high speed in a wireless communication network. In particular, 5G will facilitate establishing the Internet of Things as an essential part in our life through laying the foundation for releasing its full potential. With huge improvements over the present functions of the 4G, 5G guarantees a more IoT-friendly ecosystem. Though it remains a long way before 5G becomes a mainstream, companies should begin to develop and re-imagine products and services to utilize the better functions of 5G. Through generating digital twins, 5G combined with IoT will bring all items on the shelf to the internet. If there are billions of hardware-connected devices, the potential for regular consumer goods with digital twins to become components of the new Internet of Things will be higher. Augmented Reality refers to a crucial technology which promotes a major paradigm shift in the way that users interact with data. This has just been known as a feasible solution to different critical needs recently. Moreover, augmented reality (AR) technology can be utilized to visualize data from hundreds of sensors concurrently, overlaying related and actionable information over your environment via a headset. With much more data flow, 5G helps AR technology to be much faster. Featured with easier and more reachable use, it is more likely to be widely applied in various different functions (including video gaming). In conclusion, the convergence of IoT and AR in the age of 5G is actually cool forthcoming wave which will be related with IoT, where the great data storage will enable an AR lens into the scenes in the ways that offer almost immediate insight at a level of depth unthinkable before. Hence, this part intends to introduce the latest findings to the Internet of Things and Augmented Reality technologies for different applications in the age of 5G. It can promote technologists to quicken the latest technical progress. Topics include, but are not limited to: = Novel IoT techniques = Human, IoT and AI Communication Protocols = 5G and Its applications = Augmented reality = 5G-based video transfer techniques for IoT = Novel IoT devices = IoT for augmented reality = IoT device search in the era of 5G = Knowledge-based discovery of devices, data and services in the IoT =Real-world Applications of IoT: security; healthcare; advertising; and government
Last updated by Dou Sun in 2019-09-07
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