计算机网络 | SCI期刊专刊/国际会议信息5条

全文截稿: 2019-10-12

开会时间: 2020-02-23

会议难度: ★★

CCF分类: 无

会议地点: Lisbon, Portugal

网址：https://www.iaria.org/conferences2020/ICDT20.html

The Fifteenth International Conference on Digital Telecommunications (ICDT 2020) continues a series of special events focusing on telecommunications aspects in multimedia environments.  The scope of the conference is to focus on the lower layers of systems interaction and identify the technical challenges and the most recent achievements.
The conference will serve as a forum for researchers from both the academia and the industry, professionals, and practitioners to present and discuss the current state-of-the art in research and best practices as well as future trends and needs (both in research and practices) in the areas of  multimedia telecommunications, signal processing in telecommunications, data processing, audio transmission and reception systems, voice over packet networks, video, conferencing, telephony, as well as image producing, sending, and mining, speech producing and processing, IP/Mobile TV, Multicast/Broadcast Triple-Quadruple-play, content production and distribution, multimedia protocols, H-series towards SIP, and control and management of multimedia telecommunications.

全文截稿: 2019-10-12

开会时间: 2020-02-23

会议难度: ★★

CCF分类: 无

会议地点: Lisbon, Portugal

网址：https://www.iaria.org/conferences2020/ICN20.html

All topics and submission formats are open to both research and industry contributions.
ICN 2020 conference tracks:
Communication
Communication theory; Communications switching and routing; Communications modeling; Communications security; Computer communications; Distributed communications; Signal processing in communications; Multimedia and multicast communications; Wireless communications (satellite, WLL, 4G, Ad Hoc, sensor networks); Vehicular communications
New Internet technologies
SDN, SDX, NFV; 4G/5G and advanced mobile Internet;  Internet of X (everything, things, people, etc.); Internet, Virtualization and Cloud; Internet and Big Data;  Sensing and sensor networks;  Energy-aware networks; Content-oriented networking; Cellular cognitive networks; Future routing mechanisms
Networking  
Next generation networks [NGN] principles; Storage area networks [SAN]; Access and home networks; High-speed networks; Optical networks;  Peer-to-peer and overlay networking; Mobile networking and systems; MPLS-VPN, IPSec-VPN networks; Vehicular Networks; GRID networks, Cloud and Visualization;  Broadband networks; Small cell networks; Sensing and sensor networks
Computation and networking
Cloud computing; Fog computing; Internet computing; Mobile computing; Ubiquitous computing; Real-time computing; Big data-based computing;  Services computing and opportunistic computing; High Performance Computing (HPC); Fast data processing;  Fast switching and routing protocols; Parallelization of algorithms and applications; Energy-efficient High Performance Computing
Networking metrics
Quality of service, service level agreement [QoS/SLA]; Reliability, availability, serviceabiliy [RAS]; Traffic engineering, metering, monitoring; Voice over IP services; Performance evaluation, tools, simulation; Network, control and service architectures; Network signalling, pricing and billing; Network middleware; Telecommunication networks architectures; On-demand networks, utility computing architectures; Applications and case studies
Next generation networks (NGN) and network management  
NGN protocol design and evaluation NGN Standard Activities [ITU, TMF, 3/4/5GPP, IETF, etc.]; NGN Device Instrumentation; Network Management, scheduling and policy; NGN policy-based control; Networks policy-based management; Management of autonomic networks and systems; Delay tolerant networks; Mobility management
Cognitive radio
Fundamentals; Cognitive radio and emerging technologies; Energy-aware, smart sensing and capacity-aware in advanced technologies; Mechanisms and protocols; Processing and devices; Measurement and management; Applications

全文截稿: 2019-11-30

影响因子: 2.613

CCF分类: C类

中科院JCR分区:

  • 大类 : 工程技术 - 3区

  • 小类 : 计算机：信息系统 - 3区

  • 小类 : 工程：电子与电气 - 3区

  • 小类 : 电信学 - 3区

网址: http://www.journals.elsevier.com/computer-communications

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.

全文截稿: 2019-12-01

影响因子: 3.151

CCF分类: C类

中科院JCR分区:

  • 大类 : 工程技术 - 3区

  • 小类 : 计算机：信息系统 - 3区

  • 小类 : 电信学 - 3区

网址: http://www.journals.elsevier.com/ad-hoc-networks

The focus of this Special Issue is on Artificial Intelligence (AI) for Wireless Networks. AI applications are quickly finding their way into everyday life - whether it's eal-time traffic data, sensor data from self-driving cars, or Netflix entertainment recommendations. All of these wireless network applications generate extreme volumes of data that must be collected and processed in real time. The advantages of AI are numerous, most notably are learning from examples, experience, scalability, adaptability, and the ability to extract rules without the need for detailed or precise mathematical modeling. In general, the advantages of pursuing this line of research have been proven worthy.

The goal of the Special Issue is to publish the most recent results in the development of AI for wireless networks. Researchers and practitioners working in this area are expected to take this opportunity to discuss and express their views on the current trends, challenges, and state of the art solutions addressing various issues in AI for wireless networks. Review papers on this topic are also welcome. Topics to be covered in this Special Issue include the following topics, but are not limited to:

Deep Learning and Neural Networks for Wireless Networks

Machine Learning for Wireless Networks

Computer Vision in Wireless Networks

Reinforcement Learning for Wireless Networks

Knowledge Driven Wireless Networks

Decision Making for Wireless Networks

Fuzzy Systems for Wireless Networks

Evolutionary Computing for Wireless Networks

Human-Inspired Learning and Computing for Wireless Networks

Data Mining in Wireless Networks

AI for Wireless Sensor Networks

AI for Internet of Things

全文截稿: 2019-12-01

影响因子: 2.613

CCF分类: C类

中科院JCR分区:

  • 大类 : 工程技术 - 3区

  • 小类 : 计算机：信息系统 - 3区

  • 小类 : 工程：电子与电气 - 3区

  • 小类 : 电信学 - 3区

网址: http://www.journals.elsevier.com/computer-communications

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

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