Journal Information
IEEE Transactions on Network Science and Engineering (TNSE)
https://www.comsoc.org/publications/journals/ieee-tnse
Impact Factor:
5.213
Publisher:
IEEE
ISSN:
2334-329X
Viewed:
16611
Tracked:
10
Call For Papers
The IEEE Transactions on Network Science and Engineering is committed to timely publishing of peer-reviewed technical articles that deal with the theory and applications of network science and the interconnections among the elements in a system that form a network. In particular, the IEEE Transactions on Network Science and Engineering publishes articles on understanding, prediction, and control of structures and behaviors of networks at the fundamental level. The types of networks covered include physical or engineered networks, information networks, biological networks, semantic networks, economic networks, social networks, and ecological networks. Aimed at discovering common principles that govern network structures, network functionalities and behaviors of networks, the journal seeks articles on understanding, prediction, and control of structures and behaviors of networks. Another trans-disciplinary focus of the IEEE Transactions on Network Science and Engineering is the interactions between and co-evolution of different genres of networks.

The core topics covered include: Network Sampling and Measurement; Learning of Network Topology; Modeling and Estimation of Network Dynamics; Network Inference; Models of Complex Networks; Modeling of Network Evolution; Network Design; Consensus, Synchronization and Control of Complex Networks; Interactions between and Co-evolution of Different Genres of Networks; Community Formation and Detection; Complex Network Robustness and Vulnerability; Network Interdependency and Cascading Failures; Searching in Complex Networks; Information Diffusion and Propagation; Percolation and Diffusion on Networks; Epidemiology in Complex Systems.
Last updated by Dou Sun in 2021-04-08
Special Issues
Special Issue on Next-generation Traffic Measurement with Network-wide Perspective and Artificial Intelligence
Submission Date: 2022-12-15

Traffic measurement is deemed as the bedrock of the next-generation network systems. Its function is to monitor network traffic at all protocol layers, from the physical layer to the applications, and to capture traffic patterns, relationships, and anomalies in the time dimension and volume dimension to support fundamental network functions and upper-layer services, such as load balancing, routing, intrusion detection, traffic engineering, and performance diagnosis. Recently, the explosive Internet traffic growth, the emerging networking paradigms, and the surging network service demands have opened new challenges for traffic measurement, which have gained significant attention from both academia and industry. However, the state-of-the-art solutions, which mainly focus on single-point measurement scenarios and derive probabilistic formulas to measure elementary metrics like frequency, cardinality, and persistence, cannot meet the arising heterogeneous and fine-grained measurement requirements on performance, throughput, scalability, response time, and diversity. For example, modern switches can forward packets at extremely high throughput (up to several Gpps), practically two orders of magnitude higher than the throughput of existing sketch solutions. For another example, application-oriented sketches that provide timely and accurate features beyond elementary metrics to applications like traffic engineering and intrusion detection systems can undoubtedly benefit such systems, while the design of such sketches and the derivation of measurement formulas are non-trivial problems. Thus, there is an urgent need for systematic and in-depth research on network-wide and AI-powered traffic measurement methods to meet new network traffic characteristics and support emerging applications. It is expected that the next-generation network management systems will feature network-wide measurement algorithms. The big network data is distributed in nature as the sources and destinations of connections may span the entire network. It is thus essential to aggregate the views of multiple measurement points to build a network-wide perception and capture comprehensive and accurate traffic information. Besides, with a proper task breakdown schema, multiple measurement points can federatively run measurements in a completely parallel and distributed manner, reducing the computation overhead and hardware requirement. Another latest trend involves artificial intelligence technologies that allow seamless aggregation of multi-resolution and heterogeneous network traffic data while advancing traffic measurement systems' design, deployment, and application. Additionally, the interplay between traffic measurement and artificial intelligence is bidirectional. Besides using AI intelligence to power traffic measurement, traffic measurement methods can also aid the AI systems since AI systems are often deployed in a decentralized environment where communication plays an important role. For instance, sketches, a family of traditional measurement methods, can naturally compress the input data and approximate its distribution. It can be used as the medium to transfer gradients among distributed AI systems, striking a tradeoff among information compression, convergence time, and system accuracy. This special issue is intended to encourage scholars and experts to systematically discuss the latest research progress and development trends for next-generation traffic measurement, promote in-depth research, and share academic and technical achievements. The topics of this SI include, but are not limited to: Network-wide traffic measurement algorithms and systems Use of artificial intelligence, machine learning, and data analytics in network traffic measurement and its applications Network-wide and/or AI-powered traffic measurement for the Internet, edge networks, data center networks, cloud-based systems, software-defined networks, online social networks, online services, and next-generation networks Traffic measurement with programmable hardware and software platforms Traffic measurement with privacy preservation and anonymization AI-powered design, simulation, modeling, analysis, and visualization for next-generation traffic measurement Validation and repeatability of network-wide and/or AI-powered traffic measurements, shared datasets, or collaborative platforms Novel applications of network-wide and AI-powered traffic measurement for load balancing, flow scheduling, network management, and network evolution Novel applications of network-wide and AI-powered traffic measurement for security, anomaly/vulnerability/attack detection, and user profiling/privacy Novel applications of traffic measurement for artificial intelligence and machine learning
Last updated by Dou Sun in 2022-06-26
Best Papers
YearBest Papers
2019Network Maximal Correlation
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