IEEE Transactions on Network Science and Engineering
IEEE Transactions on Network Science and Engineering (TNSE) publishes 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. Read the full scope of TNSE.
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From the October-December 2017 issue
Stochastic Subgradient Algorithms for Strongly Convex Optimization Over Distributed Networks
By Muhammed O. Sayin, N. Denizcan Vanli, Suleyman S. Kozat, and Tamer Basar
We study diffusion and consensus based optimization of a sum of unknown convex objective functions over distributed networks. The only access to these functions is through stochastic gradient oracles, each of which is only available at a different node; and a limited number of gradient oracle calls is allowed at each node. In this framework, we introduce a convex optimization algorithm based on stochastic subgradient descent (SSD) updates. We use a carefully designed time-dependent weighted averaging of the SSD iterates, which yields a convergence rate of O(N√N/(1-σ)T) after T gradient updates for each node on a network of N nodes, where 0<σ<1 denotes the second largest singular value of the communication matrix. This rate of convergence matches the performance lower bound up to constant terms. Similar to the SSD algorithm, the computational complexity of the proposed algorithm also scales linearly with the dimensionality of the data. Furthermore, the communication load of the proposed method is the same as the communication load of the SSD algorithm. Thus, the proposed algorithm is highly efficient in terms of complexity and communication load. We illustrate the merits of the algorithm with respect to the state-of-art methods over benchmark real life data sets.
Editorials and Announcements
- TNSE is pleased to participate in a free trial offering of the new IEEE DataPort data repository, which supports authors in hosting and referring to their datasets during the article submission process. Learn more about this exciting opportunity.
- TNSE is now indexed in the Clarivate Analytics Emerging Sources Citation Index (ESCI), a new edition of the Web of Science.
- We are pleased to announce that Dapeng Oliver Wu, a professor in the Department of Electrical & Computer Engineering at the University of Florida, has been named the new 2017-2018 EIC for the IEEE Transactions on Network Science and Engineering.
- Message from the Incoming Editor-in-Chief (April-June 2017)
- State of the Journal Editorial (April-June 2017)
- EIC Editorial (Jan-March 2016)
- IEEE Transactions on Network Science and Engineering (Jan-June 2014)
- IEEE TNSE Inaugural Issue Editorial (Jan-June 2014)
Call for Papers
Special Issue on Network Science in Biological and Bio-inspired Systems
Submission deadline: 31 Dec. 2017. View PDF.
Network science offers a novel framework for systematically exploring the structure and functions of biological and bio-inspired systems, which has attracted much attention within the IEEE and beyond. Indeed, networks have been widely used, not only to represent the couplings of various types, e.g., synapses that connect neurons (connectome), physical contacts between proteins (interactome), chemical reactions of metabolism, gene regulation, and swarming interactions, but also to analyze the functions and underlying working mechanisms of these systems. Given the growing activity of both theory and applications across computer science, physics and biology, the aim of this special issue is to provide a venue for researchers with different backgrounds to discuss the fruitful results, recent advances and challenges in the interdisciplinary area of biological and bio-inspired networks.
Special Issue on Big Data and Artificial Intelligence for Network Technologies
Submission deadline: 30 Jan. 2018. View PDF.
Generation of huge amounts of data, called big data, across different sectors such as banking, healthcare, retail and education, among others, is creating the needs for efficient tools to manage those data. Artificial intelligence (AI) has become the powerful tools in dealing with big data with recent breakthroughs at multiple fronts in machine learning, including deep learning. Meanwhile, information networks are becoming larger and more complicated, generating a huge amount of runtime statistics data such as traffic load, resource usages. The emerging big data and AI technologies may include a bunch of new requirements, applications and scenarios such as ehealth, Intelligent Transportation Systems (ITS), Industrial Internet of Things (IIoT), and smart cities in the term of computing networks. The big data and AI driven network technologies also provide an unprecedented patients to discover new features, to characterize user demands and system capabilities in network resource assignment, security and privacy, system architecture, modeling and applications, which needs more explorations. We believe that these explorations will greatly benefit the academia and Information and Communication Technologies (ICT) industries.
Special Issue on Network of Cyber-Social Networks: Modeling, Analyses, and Control
Submission deadline: 1 Feb. 2018. View PDF.
Network of cyber-social networks (NCN) is a promising new area that has recently attracted significant interests. Its core differentiator is the tight conjoining among heterogeneous cybersocial networks or between cyber networks and physical networks. The focus of this proposed special issue is to address the interdependence of NCN components that facilitates its modeling, analysis, and control.
Over the past decade, we have witnessed an unprecedented growth of communication networks and online social networks in cyber world that “teleports” us to interact with remote people and/or objects. With the development of cyber technologies, heterogeneous networks become increasingly interdependent. Examples include mobile social networks that exploit social ties, modern power networks that rely on the control and communication over the Internet, and intelligent transportation networks that utilize communication networks to monitor traffic congestion in real time. A mobile social network is a typical multi-layer NCN where the shortrange device-to-device communication is influenced by the human social behavior (trust and reciprocity). A modern power network demonstrates a fundamental NCN property where the interconnecting links between networks may induce or impede the risk of cascading failures. Until recently, these heterogeneous networks were mostly treated as separate entities, leaving their complex interactions unexplored.
Special Issue on Network Science for High-Confidence Cyber-Physical Systems
Submission deadline: 1 Mar. 2018. View PDF.
Cyber-Physical Systems (CPS) refer to the engineered systems that can seamlessly integrate the physical world with the cyber world via advanced computation and communication capabilities. Today’s CPS involve smart devices that bring into CPS ubiquitous intelligence, leading to the innovation and competition within sectors such as agriculture, healthcare, transportation, energy, manufacturing, environment, building design and automation, etc., which can completely transform the ways people interact with the physical world. To enable high-confidence CPS for better benefits realization as well as supporting emerging applications, network science based theories and methodologies are needed to cope with the ever-growing complexity of smart CPS, to predict the system behaviors, and to model the deep inter-dependencies among CPS and the natural world. Nevertheless, current research on network science approaches to investigate the challenges in high-confidence CPS is quite scattered. The major objective of this special issue is to exploit various network science techniques such as modeling, analysis, mining, visualization, and optimization to advance the science of supporting high-confidence CPS for greater assurances of security, safety, scalability, efficiency, and reliability.
Special Issue on Network Science for Internet of Things (IoT)
Submission deadline: 1 Apr. 2018. View PDF.
Internet of Things (IoT) applications have been growing significantly in recent years and the so-called IoT ecosystem enables seamless connectivity that is enabling many applications such as smart home, smart health, connected vehicle and smart grid and others. The network infrastructure, connectivity and dynamics in the IoT ecosystem are becoming increasingly complex, scalable and heterogeneous, opening up many challenges for network sciences and system engineering including architectural, operational, service and security challenges. In addition, the interconnection and cooperation of things (e.g., connected sensors and devices) rely on the both the network environment, service provided and the physical environments. However, many related topics such as network control, evolutions, efficiency, security, privacy, network properties, network and device heterogeneity have not been well studied in the literature for the IoT ecosystem. There is a strong need to understand the fundamental characteristics such as control structure, functions and behavior of networks both from a theoretical and practical perspective for future IoT. In addition, since IoT applications generate huge amounts of network traffic over networks, network issues such complexity, efficiency, dynamics, interferences and interaction and robustness need to be reviewed on a large scale. We aim to leverage them in order to better understand network performance bound, user demands and experience and capacity of the IoT network infrastructure which will enable the network to seamlessly connect to IoT devices and support the emerging applications of IoT users. In summary, research on network sciences and engineering for inter-disciplinary IoT applications is still in its infancy. For example, more research efforts are needed to examine the service quality of connected health applications from a network science perspective and principle. This special issue will present how recent and future advances in network science and engineering can be leveraged to enable future and emerging IoT applications.
Special Issue on Intelligent Network Management
Submission deadline: 30 May 2018. View PDF.
With the development of IT technology, communication networks have been evolving from a medium of data exchange to a platform providing diverse services. Recently, operators have started to explore how to use Artificial Intelligence (AI) to simplify, optimize and intelligently assist with network management and control to reduce operational costs and to improve performance and user experience. Recent breakthroughs from big data technology have accelerated this interest.
In general, such AI technologies will enable operators to gain a deeper understanding of the network dynamics and enable more accurate forecasts of network trends. While it is important to gain such knowledge, the more challenging question is how to apply such knowledge to improve the network performance, i.e., to improve planning and operating decisions for managing the network. This is not straightforward for many reasons. For example, current decision making models were not developed for using such knowledge, and efforts made to gain and apply such knowledge is at an early stage. Additionally, forecasts based on such knowledge have a shelf life, and may become invalid after network operating policies are changed. For example, users may adjust their demand patterns in response to network changes. Therefore, new methods are needed for implementing intelligent network management. A three stage closed loop model is needed to uncover useful patterns through: 1) closely observing the network, 2) forecasting network trends, 3) applying the knowledge gained to improve the network.
Addressing the above challenge involves knowledge and skills from different disciplines, spanning from AI, networking, optimization, game theory, and so on. This special issue (SI) calls for research on intelligent network management. While the scope of intelligence application can be broadly defined as network performance analysis and forecasting, we are especially interested in network operational functions such as capacity planning, resource provisioning, routing, faulty recovery, etc. We welcome theoretical work for building scientific foundations, as well as cases of specific applications to demonstrate the potential.
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