Events
IEEE Access invites manuscript submissions in the area of Communication and Fog/Edge Computing Towards Intelligent Connected Vehicles (ICVs).
Submission Deadline: 30 November 2019
With rapid economic development, the number of vehicles on the road has grown dramatically, which introduces an array of traffic-related issues, such as traffic congestion and driving safety. Intelligent connected vehicles (ICVs) can provide a safer and greener transportation system, which has been envisioned as an effective measure to resolve traffic problems. ICVs are expected to run many emerging smart applications (e.g., autonomous driving, safety early warning, natural language processing, etc.) to assist both the drivers and passengers in vehicular environments. These kinds of applications typically require significant computing power to perform computation-intensive and latency-sensitive tasks generated by the vehicle sensors for low-latency response. However, the limited computation capacity of the on-board computer makes it difficult to satisfy the computation requirements of quality-of-experience (QoE)-demanding applications. To tackle this challenge, fog/edge computing are proposed as innovative computing paradigms to extend computing capacity to the network edge in order to meet the requirements. Fog/edge computing is expected to not only maximize the computation capability and alleviate the greenhouse effect, but also achieve sustainable operation by pushing rich computing and storage resources to the edge of the network.
The limited computation capacity of the on-board computer brings about an unprecedented challenge for the future development of ICVs. Fog/edge computing provides cloud computing capacity in close proximity to vehicles. Vehicles can migrate the computing to the edge of the network via vehicle to everything (V2X) communication. Processing can be completed at road-side unit (RSU) at the side of the network. The advancement of communication technologies and edge computing, such as Fifth-generation (5G), Software Defined Networking (SDN), Network Function Virtualization (NFV), mobile edge/fog computing and so on, makes it possible to enhance computational capabilities, ensure near-real-time responses and realize communication requirements with ultra-low latency and ultra-high reliability. The Special Section in IEEE Access aims to provide the latest research findings and solutions, in terms of communication and edge computing for ICVs.
The topics of interest include, but are not limited to:
* New architecture and framework establishment based on fog/edge computing for ICVs
* Advanced vehicular networks technologies, such as 5G vehicular networks, LTE-V and so on
* Ultra-reliable and low-latency communications for ICVs
* Resource allocation and management based on fog/edge computing for ICVs
* Machine learning, deep learning for intelligent management and control
* Joint analysis of communication and computing to improve performance in vehicular networks
* Cross-layer optimization for fog/edge computing
* Mobility modeling and management for ICVs
* SDN and NFV technologies for vehicular networks
* Security and privacy challenges
We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.
Associate Editor: Lei Shu, Nanjing Agricultural University, China / University of Lincoln, UK
Guest Editors:
1. Junhui Zhao, East China Jiaotong University, China / Beijing Jiaotong University, China
2. Yi Gong, Southern University of Science and Technology, China
3. Changqing Luo, Virginia Commonwealth University, USA
4. Tim Gordon, University of Lincoln, UK
Nowadays we are witnessing the formation of a massive Internet-of-Things (IoT) ecosystem that integrates a variety of wireless-enabled devices ranging from smartphones, wearables, and virtual reality facilities to sensors, drones and connected vehicles. As IoT is penetrating every aspect of people’s life, work and entertainment, an increasing number of IoT devices and the emerging IoT applications are driving an exponential growth in wireless traffic in the foreseeable future. As a result, current IoT system architectures are facing significant challenges to handle millions of devices; thousands of servers; and the transmission and processing of large volume of data, etc. The growing diversity of IoT serveries and complexity of mobile network architectures has made monitoring and managing a multitude of IoT elements extremely difficult. Meanwhile, Deep Reinforcement Learning (DRL) techniques have been seen as a promising approach for building such complex IoT systems and to innovate at a rapid pace. Over the last few years, DRL has achieved remarkable success in different areas including games, robotics, Natural Language Processing (NLP), healthcare, etc. Researchers in IoT areas also began to recognize the power and importance of DRL and have been exploring different DRL techniques to solve problems specific to the emerging IoT systems. Researchers have explored the inherent power of fusion between DRL technologies and IoT systems in both industrial and academic field. DRL algorithms can provide effective and smart solutions for sequential decision-making, optimization and control problems, dealing with incomplete or inconsistent information related to IoT. This special issue aims to foster the dissemination of high-quality research with emerging ideas, approaches, theories and practice to resolve the challenging issues related to DRL in IoT domain. Specially, the special issue is focused on emphasizing the significance of DRL in modelling, identification, optimization, and control of future IoT systems.
Topics include, but are not limited to the following:
- Deep Reinforcement Learning driven energy-efficient networks and services in IoT
- Deep Reinforcement Learning for Quality-of-Experience Management in IoT
- Hybrid Deep Reinforcement Learning Models and Applications for IoT in Industrial applications
- Deep Reinforcement Learning based data analytics and decision automation in IoT
- Deep Reinforcement Learning architecture/algorithms for large-scale IoT systems
- Deep Neural Network modeling, analysis and synthesis techniques in IoT
- Deep Reinforcement Learning for IoT and sensor research: energy, routing, prediction
- Deep Reinforcement Learning for IoT security and privacy
- Deep Reinforcement Learning testbed and experiment experiences in IoT systems
- Deep Reinforcement Learning for IoT enabled healthcare and transportation systems
Submission:
All original manuscripts or revisions to the IEEE IoT Journal must be submitted electronically through IEEEManuscript Central, http://mc.manuscriptcentral.com/iot. Author guidelines and submission information can be found at http://ieee-iotj.org/.
Important Dates:
- Submission Deadline: October 1, 2019
- First Review Due: December 15, 2019
- Revision Due: February 1, 2020
- Acceptance Notification: March 1, 2020
- Final Manuscript Due: March 15, 2020
- Publication Date: 2020
Guest Editors:
- Jia Hu, University of Exeter, UK
- Peng Liu, Hangzhou Dianzi University, China
- Hong Liu, East China Normal University, China
- Obinna Anya, Google, USA
- Yan Zhang, University of Oslo, Norway
Call for Papers – IEEE JSAC Special issue on Advances in Artificial Intelligence and Machine Learning for Networking
—————————————————————————
Artificial Intelligence (AI) and Machine Learning (ML) approaches have
emerged in the networking domain with great promise. They can be clustered
into AI/ML techniques for network engineering and management, network
design for AI/ML applications, and system aspects. AI/ML techniques for
network management, operations and automation improve the way we address
networking today. They support efficient, rapid, and trustworthy management
operations. The current interest in softwarization and network program-
mability fuels the need for improved network automation in agile infra-
structures, including edge and fog environments. Network design and optimi-
zation for AI/ML applications address the complementary topic of supporting
AI/ML-based systems through novel networking techniques, including new
architectures and performance models. A third topic area is system mplemen-
tation and open-source software development.
This special issue will focus on networking aspects (mostly, network layer
and above). Work with primary contribution to physical layer concepts or
wireless access should be submitted to other venues. Prospective authors
are invited to submit high-quality, original manuscripts on the following
topics, but not limited to:
Fundamental Frameworks
* Network theory inspired by machine learning
* Transfer learning and reinforcement learning for networking
* Big data analytic frameworks for networking data
Network analytics
* Machine learning, data mining and big data analytics for networking
* Representation learning on operational data
* Data mining, statistical modeling, and machine learning for network
management
* User experience-driven network planning
* Learning algorithms and tools for network diagnostics and root cause
analysis
Network decision making and optimization
* Protocol design and optimization using machine learning
* Network architecture and optimization for AI/ML applications at scale
* Resource allocation for shared/virtualized networks using machine learning
* Energy-efficient network operations based on AI/ML algorithms
* AI/ML Algorithms for network security
* Network Reliability, robustness and safety based on AI/ML concepts
* Security for networks optimized and operated based on AI/ML concepts
Network automation
* Self-driving networks
* Self-Learning and adaptive networking protocols and algorithms
* Deep learning and reinforcement learning in network control & management
* Predictive or self-aware networking maintenance
* Open-source AI software for networking or networked applications
Submission Guidelines
All submissions must follow the Guide for Authors as published on the
Journal website at http://www.comsoc.org/jsac/paper-submission-guidelines.
- Manuscript Due: October 1, 2019
- Acceptance notification: March 1, 2020
- Final manuscript due: March 15, 2020
- Expected Publication of the Special Issue: Second quarter 2020
Guest Editors
- Rolf Stadler, KTH Royal Institute of Technology, Sweden, stadler@kth.se (Lead Guest Editor)
- Prosper Chemouil, Orange Labs (retired), France, prosper.chemouil@ieee.org
- Pan Hui, University of Helsinki, Finland & Hong Kong University of Science and Technology, Hong Kong, panhui@cse.ust.hk
- Noura Limam, University of Waterloo, Canada, noura.limam@uwaterloo.ca
- Wolfgang Kellerer, Technical University of Munich, Germany, wolfgang.kellerer@tum.de
- Yonggang Wen, Nanyang Technological University, Singapore, ygwen@ntu.edu.sg
Smart systems including Internet of Things (IoT) are emerged to address contemporary economic, societal, and environmental challenges, such as business and production automation, urban sustainability, climate change, healthcare, and globalization. They encompass different autonomous or collaborative systems with functions of sensing, actuation, and control for describing and analyzing a situation, and making decisions based on the available data in a predictive or adaptive manner. Intelligent networking enables these functions of smart systems by offering a global infrastructure for networked physical devices and everyday objects, which generate gigantic amount of data, or big data. In addition, big data analytics is also employed in analyzing the big data so as to enable the networking to be intelligent and allow smart systems to perform astute, autonomous or collaborative actions. Nevertheless, the efficient and effective big data management and knowledge discovery of large-scale smart systems, big data analytics for intelligent networking, and networking technologies for big data (e.g., collection, processing, analysis and visualization) need more explorations.
The topics of interest for this special issue include, but are not limited to:
- Algorithms, models, and architecture for big data analytics
- Knowledge acquisition and discovery from big data
- Machine learning and computational intelligence techniques for handling big data
- Resource management of big data in smart systems
- Big data security and privacy in smart systems
- Network architecture evolution with big data
- Adaptive protocol design and control based on big data analytics
- Big data assisted planning and design in smart systems
- Network automation with big data analytics
- Network management, measurement, and diagnostics using big data analytics
- Network service and quality management using big data analytics
- Big data with in-network computation
- Networking big data analysis
- Information-centric networking and software-defined network for big data
- Network function virtualization and network slicing for big data
- Edge, fog, and mobile edge computing for big data
- Blockchain with big data networking
- Distributed artificial intelligence with networking
Submission Guidelines
Prospective authors are invited to submit their manuscripts electronically, adhering to the IEEE Transactions on Network Science and Engineering guidelines. Note that the page limit is the same as that of regular papers. Please submit your papers through the online system (https://mc.manuscriptcentral.com/tnse-cs) and be sure to select the special issue or special section name. Manuscripts should not be published or currently submitted for publication elsewhere. Please submit only full papers intended for review, not abstracts, to the ScholarOne portal. If requested, abstracts should be sent by e-mail to the Guest Editors directly.
Important Dates
- Manuscripts Due: 1 December 2019
- Peer Reviews to Authors: 15 February 2020
- Revised Manuscripts Due: 1 April 2020
- Second-Round Reviews to Authors: 31 May 2020
- Final Accepted Manuscript Due: 30 June 2020
Guest Editors
- Ruidong Li (Lead Guest Editor)
National Institute of Information and Communications Technology, Japan - Ka-Cheong Leung
The University of Hong Kong, China - Michele Nogueira
Federal University of Paraná, Brazil - Tarik Taleb
Aalto University, Finland - Jie Wu
Temple University, USA
Big data and Artificial Intelligence/Machine Learning (AI/ML) are transforming the world, and the data communication networks domain is not an exception. The complexity of communication networks has dramatically increased in the last few years, making it more important to design scalable network measurement and analysis techniques, as well as data driven approaches for networking. Critical applications such as network monitoring, network security, or dynamic network management require fast and intelligent mechanisms for on-line analysis of thousands of events per second, as well as efficient techniques for off-line analysis of massive historical data. Making operational sense out of the ever-growing amount of network measurements is becoming a major challenge. In addition, the explosion in volume and heterogeneity of data measurements generated across the entire network stack is opening the door to innovative solutions and out-of-the-box ideas to improve current networks, and many other networking applications besides monitoring and analysis are becoming more data and measurements driven than ever.
The Big-DAMA workshop seeks for contributions in the field of AI/ML and big data analytics applied to data communication network analysis, including the analysis of on-line streams and off-line massive datasets, network traffic traces, topological data, performance measurements, and many more. Big-DAMA targets novel and out-of-the-box approaches and use cases related to the application of AI/ML and big data in networking. The workshop will allow researchers and practitioners to discuss the open issues related to the application of AI/ML to networking problems and to share new ideas and techniques for big data analysis in data communication networks.
TOPICS OF INTEREST
We encourage both mature and positioning submissions describing systems, platforms, algorithms and applications addressing all facets of the application of AI/ML and big data to the analysis of data communication networks. We are particularly interesting in disruptive and novel ideas that permit to unleash the power of AI/ML and big data in the networking domain. The following is a non-exhaustive list of topics:
- AI/ML and big data analytics in networking
- Big networking data analysis
- Deep learning for networking
- Application of reinforcement learning in networking
- Transfer learning and explainable AI (XAI) for networking
- AI/ML and big data for network measurements mining
- Stream-based machine learning for networking
- Big data frameworks for network analytics
- AI/ML and big data for security and anomaly detection
- AI/ML and big data analytics for network management
- Big networking data integrity and privacy
- Big data analytics and visualization for traffic analysis
- Research challenges on AI/ML and big data analytics for networking
SUBMISSION INSTRUCTIONS
Submissions must be original, unpublished work, and not under consideration at another conference or journal. Submitted papers must be at most six (6) pages long, including all figures, tables, references, and appendices in two-column 10pt ACM format. Paper formatting should follow the main ACM CoNEXT 2019 conference guidelines, except from anonymity. Papers must include authors names and affiliations for single-blind peer reviewing by the PC. Accepted papers will be published in the ACM Digital Library. Authors of accepted papers are expected to present their papers at the workshop.
Register and submit your paper at https://conext19big-dama.hotcrp.com/
IMPORTANT DATES
- Paper registration deadline: September 9, 2019
- Paper submission deadline: September 16, 2019
- Paper acceptance notifications: October 4, 2019
- Camera ready due: October 13, 2019
ORGANIZING COMMITTEE
- Pedro Casas, AIT Austrian Institute of Technology, Austria
- Marco Fiore, CNR, Italy
- Steve Uhlig, Queen Mary University of London, UK
- Roya Ensafi, University of Michigan, USA
- Pere-Barlet-Ros, UPC Barcelona, Spain
- Luca Vassio, Politecnico di Torino, Italy
*** Submissions due: December 31, 2019 ***
https://www.springer.com/?SGWID=0-102-2-1338221-0
[We encourage early submission, please check the open submission schedule policy below.]
The emerging and the ever-growing functionality of Internet edges, where physical and virtual things are connected to the network, are opening a wide set of new opportunities, both for novel services/applications and increased efficiency/scalability. These opportunities include locally sharing information, collaborating, and generating/consuming a huge amount of data, by involving a variety of entities, such as micro-data centers, end devices, and resource-sufficient networking nodes. While Fog/Edge Computing is usually expected to bring the resources, including storage and computation closer to users (in comparison to Cloud Computing), a significant effort is needed at the Internet edge to provide a trustworthy computation edge networking environment able to support new computation technologies. Indeed, distributed big data analytics, modern Machine Learning (ML) technology, Artificial Intelligence (AI), real-time data collection and processing, scalable and distributed security solutions such as blockchain, and distributed secure data processing, may play a significant role. Moreover, new networking technologies related to Information-Centric Networking (ICN), Software-Defined Networking (SDN), Network Function Virtualization (NFV), and network slicing have emerged as the novel paradigms for fast and efficient delivering and retrieving data. This triggers the convergence between the emerging networking concepts and the new computation technologies to reach the vision of an intelligent and trustworthy Internet edge.
In order to implement such trustworthy edge networks and services, several operation and management challenges associated with intelligent and trustworthy Internet edge need to be addressed. These challenges include: trustworthy connectivity and network resource management for heterogeneous networking (HetNet); security monitoring, measurement, and assessment to ensure networking and management functions to protect data; effective ML and computation models to consider different resources (storage, networking, and computing), security requirements, and real-time constraints; trustworthiness of data sources; in-network data processing and aggregation, intelligent planning and decision models to handle security and application requirements.
This special issue aims to bring together leading research on management infrastructure, applications and the most recent advances in Internet edge-based secure network management solutions. We hence encourage original paper submissions, which have not been published or submitted for publication elsewhere, from both academia and industry presenting novel research addressing the aforementioned challenges.
Topics of interest include, but are not limited to:
– Secure intelligent edge systems and networking architectures/protocols to integrate storage, computation, and networking;
– Secure intelligent coordination and networking between edge, fog, and cloud;
– Software architectures and toolkits for secure intelligent Internet edge;
– Trust management and networking among intelligent edges;
– Intelligent and secure service function/computation chaining;
– Secure NFV, SDN, and network slicing for distributed computations;
– Secure ICN with/for edge-enabled Internet;
– Secure in-network computation for future networks, inter-data center networking and 5G;
– Privacy management for intelligent Internet edge;
– Accountability, reliability, and resiliency for intelligent Internet edge;
– Quality of Service/Experience and energy efficiency for secure intelligent Internet edges;
– Distributed AI with/for secure edge networking;
– Distributed ML with enhanced data privacy, ownership, and obfuscation;
– Integrating Blockchain with distributed edges and Internet finance;
– Data mining and big data analytics for security management in edge networking;
– Trustworthy data collection and processing for big data at the edge;
– Privacy-preserving big data processing at the edge;
– Emerging applications for intelligent secure Internet edge, such as AR/VR, IoT, industrial IoT for Industry 4.0, 5G, cyber-physical systems, smart cities, vehicular systems, healthcare;
– Management framework for intelligent secure Internet edge;
– Security performance monitoring, measurements, modelling, and evaluations for intelligent Internet edge;
– SDN/NFV security architectures and applications for Internet edge;
– Security mechanisms in wireless SDN/NFV.
Planned Schedule
Open-submission schedule: in this special issue, we implement an “open” submission approach, where we do not have a submission time period. Interested authors can submit the paper any time before a fixed deadline, and the review process will be started right after the paper submission, i.e., in a first-in first-serve fashion. The detailed submission schedule is presented as follows:
– Manuscript due: December 31, 2019
– Revision notification: 2-month after the submission
– Revised paper due: 1.5-month after the revision notification
– Final notification: 1-month after the revised paper notification
– Expected Publication of the Special Issue: third-quarter of 2020 (early accepted papers will be accessible online before the deadline)
Submission Format and Review Guidelines
The submitted manuscripts must be written in English and describe original research not published nor currently under review by other journals or conferences. Parallel submissions will not be accepted. All submitted papers, if relevant to the theme and objectives of the special issue, will go through an external peer-review process. Submissions should (i) conform strictly to the Instructions for Authors available on the JNSM website and (ii) be submitted through the Editorial Management system available at http://www.editorialmanager.com/jons.
Guest Editors of the Special Issue:
– Dr. Dijiang Huang, Arizona State University, USA
Email: dijiang.huang@asu.edu
– Dr. Jéferson Campos Nobre, Federal University of Rio Grande do Sul (UFRGS), Brazil
Email: jcnobre@inf.ufrgs.br
– Dr. Ruidong Li, NICT, Japan
Email: lrd@nict.go.jp
– Dr. Paolo Bellavista, DEIS, Università di Bologna, Italy
Email: paolo.bellavista@unibo.it
– Dr. Abdelkader Lahmadi, University of Lorraine, France
Email: abdelkader.lahmadi@loria.fr