CALL FOR PAPERS
ACM Transactions on Internet Technology – Special Issue on Edge-AI for Connected Living
The aim of this special issue is to bring academic researchers and industry developers together for sharing the recent advances and future trends of AI-driven edge intelligence for connected living. Topics of interest include, but are not limited to, the following:
Explainable AI (XAI) and predictive edge analytics for infectious diseases such as COVID-19
Edge AI-assisted COVID-19 and similar infectious disease detection or diagnosis systems
AI-centric Mobile Edge Computing approach for Connected Living
AI-enabled IoT-edge data analytics for Connected Living
AI-enabled edge data fusion for Connected Living
ML-driven edge approach to Connected Living
AI/Deep Learning/Machine Learning based networked applications, techniques and testbeds for Connected Living
AI-driven multi access edge computing approach for Connected Living
New opportunities, challenges, case studies, and applications of Edge-AI for Connected Living
Security, Privacy, and Trust of Edge-AI for Connected Living
Please refer to dl.acm.org/journal/toit/author-guidelines.
Please select “SI: Edge-AI for Connected Living” in the TOIT Manuscript Central website, mc.manuscriptcentral.com/toit.
Manuscript Submission: October 1, 2020
First Notification: December 15, 2020
Revised Version Due: February 15, 2021
Final Decision: March 31, 2021
Final Submission: April 15, 2021
Publication Date: To be scheduled in 2021
Prof. M. Shamim Hossain, King Saud University, Saudi Arabia
Prof. Changsheng Xu, Chinese Academy of Sciences, China
Dr. Josu Bilbao, IK4-IKERLAN, Spain
Dr. Md. Abdur Rahman, University of Prince Mugrin, KSA
Prof. Abdulmotaleb El Saddik, University of Ottawa, Canada
*** Call for Papers and Participation ***
2nd KuVS Fachgespräch “Machine Learning and Networking”
Oct. 08/09, 2020
After the success of the first KuVS Fachgespräch “Machine Learning and Networking” in Munich in February 2020, the second edition of this workshop will be held in Würzburg on October 8 and 9, 2020.
Machine learning and artificial intelligence (ML/AI), in particular deep learning, has led to breakthroughs in various domains such as image recognition or natural language processing.This workshop focuses on the topic of ML/AI in the context of communication networks. It aims to discuss research visions and results as well as opportunities and challenges in the intersection of these two areas. The workshop looks for contributions and ideas that provide useful combinations of ML/AI approaches to address networking challenges on all layers from MAC to Application.
Topics of interest include but are not limited to:
* Data mining & visualization, statistical modeling, and big data analytics for networking data
* Frameworks or tools for data analytics or visualization for networking data
* Time series predictions for networking data such as traffic demands, failures, etc.
* AI/ML algorithms for anomaly detection and attack step prediction in network security
* Protocol design and optimization using ML/AI
* Deep learning and reinforcement learning in network control & management
* Resource allocation for virtualized networks using machine learning
* Machine learning & transfer learning for prediction of networking data & control decisions
* Practical implementations or experience with ML/AI in networking
* Self-learning and adaptive networking protocols and algorithms
* Self-X networks: Self-learning, self-driving, self-repairing, etc.
* New concepts like empowerment for quantifying and improving ML/AI-based concepts
Two forms of contribution are possible:
* Title, abstract and reference of previously published work to present and discuss it in the KuVS community.
* Title and abstract for a visionary talk, a project or teaching report, or presentation of original work.
* All contributions should be submitted as PDF documents. Submissions may be up to 2 pages long and should be formatted according to the IEEE conference layout.
* Link to submission system: https://easychair.org/conferences/?conf=kuvsfgmln2020
The KuVS Fachgespräch is all about in-depth discussions within the community about the presented works, so we plan to have a physical meeting in Würzburg. Due to the Corona situation, we additionally prepare to enable online participation in case people cannot travel, or might have a fully online event if a physical meeting will not be possible. Thanks to our sponsors, only a small registration fee of € 20 will be charged for the participation in the workshop.
* Submission deadline: 14.08.2020
* Notification of acceptance: 21.08.2020
* Final submission/Camera-ready version and registration: 28.08.2020
* Workshop date: 08./09.10.2020
* Michael Seufert, University of Würzburg
* David Hock, Infosim
Technical Program Committee (preliminary):
* Robert Bauer, Karlsruhe Institute of Technology
* Andreas Blenk, TU Munich
* Pedro Casas, Austrian Institute of Technology
* Hauke Heseding , Karlsruhe Institute of Technology
* Oliver Hohlfeld, Brandenburg University of Technology
* Tobias Hoßfeld, University of Würzburg
* Frank Kargl, Ulm University
* Holger Karl, Paderborn University
* Wolfgang Kellerer, TU Munich
* Stefan Köhler, Infosim
* Michael Menth, University of Tübingen
* Amr Rizk, Ulm University
* Stefan Schmid, University of Vienna
* Stefan Schneider, Paderborn University
* Oliver Waldhorst, Hochschule Karlsruhe
* Martina Zitterbart, Karlsruhe Institute of Technology
The Second IEEE International Workshop on Harnessing the Data Revolution in Networking
In Conjunction with IEEE ICNP 2020
October 13, Madrid, Spain
Artificial Intelligence (AI) and Machine Learning (ML) technologies have achieved remarkable success nowadays in many application domains, e.g., natural language processing, biometrics, and computer vision. Meanwhile, the ever increasing complexity and scale of today’s networks keep posing new challenges for network measurement and analytics techniques and tools. Advances in the high-performance computing and progress in ML methods—particularly using deep learning—have made ML/AI capable of discovering valuable knowledge from enormous amounts of operational and systems data. Therefore, AI/ML has been effectively used in many critical networking data analytic functions, such as fault isolation, intrusion detection, event correlation, log analysis, capacity planning, and design optimization, just to name a few.
Moreover, networking has recently undergone a huge transformation enabled by new models resulting from softwarization, virtualization, and cloud computing. This has led to a number of novel architectures supported by emerging technologies such as Software-Defined Networking (SDN), Network Function Virtualization (NFV), edge computing, IoT, and 5G. On the other hand, maturing ML techniques, such as reinforcement learning and transfer learning, can potentially serve as a basis for incorporating learning into automated network control. The emergence of enhanced design coupled with the increased complexity in networking systems and protocols has fueled the need for improved network autonomy in agile infrastructures, which can be combined with AI/ML techniques to execute efficient, self-adaptive, rapid, and collaborating network systems.
HDR-Nets 2020 workshop is aligned with the National Science Foundations’ (NSF) Harnessing the Data Revolution (HDR) Big Idea, a national-scale activity to enable new modes of data-driven discovery that will allow new fundamental questions to be addressed at the frontiers of science and engineering, with the focus in computer and communication networks. More specifically, HDR-Nets 2020 workshop is targeting research at the intersection of machine learning and networking by bringing together experts from several research communities spanning communications and networking, machine learning, mobile computing, and big data. The agenda will includes discussions of significant contributions, community interests, new tools and research problems related to the design of intelligent, robust, and adaptive communications and networks with the aid of machine learning, as well as identifying best networking practices and design principles for learning systems.
We encourage interdisciplinary contributions of high-quality original technical and survey papers, which have not been published previously, bridging the gap between machine learning, communications, and networking from either a theoretical perspective or a practical point of view. Topics of interest include, but not limited to, the followings:
* Machine learning, deep learning, data mining and big data analytics algorithms for networking
* Reinforcement learning and machine learning techniques in network design, control, scheduling, and optimization
* Energy-efficient/green network operations using machine learning and data mining algorithms
* Machine learning for network data stream / time series analytics in real-time
* Algorithms for dealing with data-level difficulty in networking data, such as imbalance, noise, or high dimensionality
* Self-learning, machine learning and big data analytics for network intrusion/anomaly/outage/failure detection
* Natural language processing techniques for network log analytics
* Data driven network architectural and protocol design (e.g., for vehicular networks, IoT networks)
* Learning algorithms for provisioning network resources
* Autonomous networks in DCs, WANs, IXPs, wireless networks, cloud networks, CDNs, home networks, etc.
* Reinforcement learning and other learning techniques for virtualization techniques including NFV, SDN, SFC, etc.
* Networking for efficient learning systems
* Machine learning at the network edge
* Federated learning and distributed networking
* New knowledge discovery theories and models from network systems data
* Data collection and visualization techniques for networks and applications
* Techniques for anonymization and user privacy protection in networking data
* Performance analysis (e.g., security, optimality, privacy) of ML algorithms as applied to networking problems
* Machine learning algorithms for fingerprinting network device/service
* Adversarial machine learning and networking
* Open-source AI software for networking or networked applications
* Use case applications of harnessing networking data for business intelligence such as process optimization and vendor selection
* Use case applications of harnessing networking data for enhanced service and user experience such as content recommendation, location-based service and advertising
* Human factors in ML and human-ML interactions for network systems
– Steering Co-Chairs
. Zhi-Li Zhang, Minnesota University, USA
– General Co-Chairs
. Dan Pei, Tsinghua University, China
. Anwar Walid, Nokia Bell Labs, USA
– TPC Co-Chairs
. Harshit Chitalia, Juniper Networks, USA
. Bartosz Krawczyk, Virginia Commonwealth University, USA
. Tamer Nadeem, Virginia Commonwealth University, USA
– Publicity Chair
. Hannaneh B. Pasandi, Virginia Commonwealth University, USA
– Web Chair
. Santosh Nukavarapu, Virginia Commonwealth University, USA
– Submission deadline: July 24, 2020 11:59pm EDT (Extended)
– Acceptance notification: August 10, 2020
– Camera-ready due date: August 24, 2020
– Workshop date (in Conjunction with ICNP 2020): October 13, 2020
All submissions must be original research not under review at any other venue. Submissions will be evaluated on the basis of technical quality, novelty, potential impact, and clarity. Solicited submissions include both full technical workshop papers and white position papers. Maximum length of such submissions is 6 pages in two-column 10pt IEEE Computer Society format, and if accepted they will be published by IEEE and appear in the IEEE Xplore. Formatting for all submissions (excluding page length) must adhere to the guidelines here: https://icnp20.cs.ucr.edu/submission.html. In accordance with the ICNP 2020 Conference, this workshop will adapt the double-blind review policy. All accepted papers must be presented by one of the authors.
Papers must be submitted electronically as PDF files via https://hdrnets20.hotcrp.com.
ICNP organizers are closely monitoring the status of the COVID-19 pandemic and its impact on conferences and travel. We also recognize the legitimate concerns of authors and participants regarding their own health and safety. While we prefer to have an in-person conference/workshop to the maximum extent possible, the decision on a particular format for the conference will be decided later based on information available closer to the conference dates. Regardless of the eventual format for the conference, we will allow authors to present their accepted work remotely.
*Journal of Network and Systems Management – Springer*
*Special issue on Cybersecurity Management in the era of AI*
***Submissions due:* October 31st, 2020****
*We encourage early submission, please check the open submission schedule
Fifth Generation (5G) and beyond cellular networks have revolutionized the
communication architecture, providing connectivity for people, things,
data, applications, transport, and cities in smart networked environments,
at faster data rates, reduced latencies, and acceptable costs. The massive
number and volume of heterogonous connected devices in such an open space,
as well as the advancements in human computer interaction (HCI), artificial
intelligence (AI), computing and communication technologies have led to an
increasing number of personal and ubiquitous intelligent systems. Such a
wide deployment of connected smart technologies introduces new challenges
to system security and privacy, mainly for Cyber-physical Systems.
Cyberphysical is a term used for the integration of physical and computing
domains as seen in many different areas such as medical, automotive, energy
and other critical systems. Nowadays, cyberphysical systems are highly
prone to cyber attacks and other forms of security threats at the
communication layer due to system high connectivity characteristics. Some
of today’s emerging security threats are hard to detect using traditional
security and privacy measures and techniques. Therefore, innovative
security methods and privacy protection solutions are needed to provide
more secure and robust privacy-preserving intelligent cyber-physical
systems. To achieve this, *cybersecurity management systems *need to adapt
to the changing cyber security threats autonomously with minimal user
intervention to provide maximum protection against cyber attacks,
intrusions, malware and various types of data breaches. AI has the
potential to be leveraged in different aspects of cybersecurity and
cyberthreat detection. It has received significant interest lately, where a
plethora of AI and other intelligent learning solutions such as deep and
reinforcement learning are now being integrated into cybersecurity systems
to provide more secure and robust privacy-preserving solutions for personal
and ubiquitous systems. Such integration will play a vital role in
providing enhanced security for intelligent autonomous systems and enables
organizations to make crucial changes to their security landscape.
This Special Issue invites theoretical and applied cutting edge research on
standards, frameworks, models, and approaches on cybersecurity management
in the era of AI and intelligent learning technologies. More specifically,
we encourage original paper submissions on the most recent advances in
security network and system management solutions using AI. The Special
Issue also welcomes contributions from the industry perspective. Topics of
interest include, but they are not limited to:
– Cybersecurity management in cyber-physical systems using AI;
– Security, privacy, and trust issues in cyber-physical systems;
– Blockchain-enabled cyber-physical systems;
– Utilizing AI technologies for cyber investigation and threat
– The integration of AI and Blockchain for security critical
– Design, optimization and modeling of cybersecurity management
– AI and ML for intrusion detection/prevention in sensitive
– Advanced AI techniques to secure future Internet
– Trust management in cyber-physical networks and systems;
– Privacy management at edge of the network using machine learning;
– Trustworthy data collection and processing using intelligent
– Cybersecurity management of big data;
– AI-based cybersecurity techniques for IoT, IoE, IoH, and IoV;
– Cybersecurity of connected and autonomous vehicles;
– Cybersecurity and AI for digital twin;
– Management framework for intelligent secure networking.
– Cybersecurity management to protect organizations’ sensitive
data using intelligent learning techniques;
– AI-enabled digital investigation;
*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 the fixed deadline,
and the review process will begin right after the paper submission, i.e.,
in a first-in first-serve fashion. The submission schedule is:
– Manuscript due: *October 31st, 2020.*
– Revision notification: within *two* months after the submission.
– Revised paper due: within *one* month after the revision
– Final notification: *one* month after the revised paper
– Expected Publication of the Special Issue: *Second Quarter of
2021* (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 neither published nor currently under review by other journals or
conferences. Parallel submissions will not be accepted. *All papers will
undergo a similarity check using iThenticate and the work similarity should
be below 20%.* 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
Management* system available at http://www.editorialmanager.com/jons.
*Guest Editors of the Special Issue: *
● Moayad Aloqaily, xAnalytics Inc., Canada
● Salil Kanhere, UNSW Sydney, Australia
● Paolo Bellavista, DEIS, Università di Bologna, Italy
● Michele Nogueira, Federal University of Parana, Brazil
Special Issue Information
With a massive amount of data being generated by an increasing number of applications, future systems can learn to be more intelligent and to guarantee better services for users. The techniques of artificial intelligence (AI) and machine learning (ML) can help in building such systems, as they deliver an excellent advantage for studying the essential characteristics of the system as well as data collected from it. More recently, intelligent IoT systems have emerged as a topic that considers the use of AI/ML to enhance performance, enable faster customization, and optimize user experience. This is an important step in the context of autonomic and cognitive system management.
This Special Issue targets research results in the above area of intelligent systems or networks. Contributions should focus on topics such as artificial intelligence techniques and models for IoT network and service management. This also includes personalization, smart service orchestration and delivery, dynamic service function chaining, intent- and policy-based management, and centralized vs. distributed control of SDN/NFV-based networks. Moreover, articles on analytics and big data approaches, knowledge creation, and decision-making are particularly welcome. This Special Issue aims to provide a comprehensive overview of the state-of-the-art development in IoT systems; it also aims to explore novel concepts and practices with a long-term goal of fully automated systems via the technological advances of AI/ML in a wide range of applications.
We invite authors from both industry and academia working on applying methods and techniques of AI/ML to computer systems to submit original research or review articles that cover design, implementation, and optimization with a specific focus on models, protocols, and optimization algorithms.
Potential topics include, but are not limited to, the following:
- AI/ML for software-defined IoT;
- AI/ML for network optimization in IoT;
- Deep and reinforcement learning for IoT data;
- Data mining and big data analytics in IoT networks;
- AI/ML for network management and orchestration for IoT;
- Machine learning for user behavior modeling and prediction in IoT;
- Innovative architectures and infrastructures for intelligent IoT systems;
- Self-learning and adaptive protocols and algorithms for intelligent IoT systems.
Dr. Kandaraj Piamrat
Dr. Hyunhee Park
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI’s English editing service prior to publication or during author revisions.
Special Issue Editors
Interests: wireless networks; multimedia streaming; MAC design; QoE; vehicular networks; artificial intelligence; machine learning
Interests: wireless communications; MAC protocol design; big data analysis; self-driving vehicles; security systems
WAIN – Workshop on AI in Networks and Distributed Systems
2nd November to 6th November 2020, Milan, Italy, to be held with IFIP Performance 2020
Thanks to rapid growth in network bandwidth and connectivity, networks and distributed systems have become critical infrastructures that underpin much of today’s Internet services. They provide services through the cloud, monitor reality with sensor networks of IoT devices, and offer huge computational power with data centers or edge and fog computing.
At the same time, AI and Machine Learning is being widely exploited in networking and distributed systems. Examples are algorithms and solutions for fault isolation, intrusion detection, event correlation, log analysis, capacity planning, resource management, scheduling, and design optimization, just to name a few. The scale and complexity of today’s networks and distributed systems make their design, analysis, optimization and management a daunting task. For this, smart and scalable approaches leveraging machine learning solutions must be deployed to take full advantage of these networks.
WAIN workshop aims at showing to the community new contributions in these fields. The workshop looks for smart approaches and use cases for understanding when and how to apply AI. WAIN will allow researchers and practitioners to share their experiences and ideas and discuss the open issues related to the application of machine learning to computer networks.
Topics of Interest:
The following is a non-exhaustive list of topics of interest for WAIN workshop:
Applications of ML in communication networks and distributed systems
Data analytics and mining in networking and distributed systems
Traffic monitoring through AI
AI applied to IoT and 5G
Application of reinforcement-learning
ML-based methodologies for anomaly detection and cybersecurity
Performance optimization through AI/ML and Big Data
Experiences and best-practices using machine learning in operational networks
Reproducibility of AI/ML in networking and distributed systems
Methodologies for performance evaluation of distributed infrastructure
Machine Learning application in cloud, edge, and fog computing
Performance evaluation of Content Delivery Networks
Application of AI/ML in sensor networks
AI/ML for data center management
AI/ML for cyber-physical systems
ML-driven resource management and scheduling
AI-driven fault tolerance in distributed systems
Submission deadline: August 22nd, 2020 (Anywhere on Earth)
Notification of acceptance: September 25th, 2020
Camera ready version deadline: October, 15th, 2020
Workshop presentation: November 2nd-6th,2020
Papers will be published at ACM SIGMETRICS Performance Evaluation Review (PER, https://www.sigmetrics.org/per.shtml, 3 to 4 pages long).
Submissions must be original, unpublished work, and not under consideration at another conference or journal. The format for the submissions is that of PER (two-column 10pt ACM format)), between 3 and 4 pages, including all figures, tables, references, and appendices. Papers must include authors names and affiliations for single-blind peer reviewing by the TPC. Authors of accepted papers are expected to present their papers at the workshop.
The submission page is available at https://easychair.org/conferences/?conf=wain2020.
Luca Vassio, Politecnico di Torino, Italy
Zhi-Li Zhang, University of Minnesota, USA
Danilo Giordano, Politecnico di Torino, Italy
Abhishek Chandra, University of Minnesota, USA
*CALL FOR PAPERS*
*Special Issue on Deep Learning for Future Smart Cities*
WILEY Internet Technology Letters
Call for Papers
On the onset of a new era of global transformation in which residents and
their surrounding environments are increasingly connected through
rapidly-changing intelligent technologies. This transformation offers great
promise for improved wellbeing and prosperity but poses significant
challenges at the complex intersection of technology and society. Future
smart connected cities in turn, aims to synergistically integrate
intelligent technologies with the natural and built environments, including
infrastructure, to improve the social, economic, and environmental
well-being of those who live, work, or travel within it.
In recent years, Deep Learning approaches have emerged as powerful
computational models and have shown significant success to deal with a
massive amount of data in unsupervised settings. Deep learning is
revolutionizing because it offers an effective way of learning
representation and allows the system to learn features automatically from
data without the need of explicitly designing them. With the emerging
technologies on the Internet of Things (IoT), wearable devices, cloud
computing and data analytics offer the potential of acquiring and
processing tremendous amount of data from the physical world. Recently,
deep learning-based algorithms help efficiently leveraging IoT and Big Data
aspects in the development of personalized services in Smart Cities.
This special issue solicits contributions from the field of Smart City Data
analytics using deep learning and Artificial Intelligence. Each submitted
paper should cover the solutions with the state-of-the-art and novel
approaches for the IoT problems and challenges in deep learning and AI
perspectives. Topics to be included in this special issue include but not
are limited to:
• Deep learning /AI for Urban modelling for Smart cities
• Deep learning /AI for Intelligent infrastructure of Smart cities
• Deep learning/AI for Smart mobility and transportation
• Deep learning/AI for Smart urban governance
• Deep learning/AI for to Resilience and Sustainability of Smart cities
• Deep learning/AI for Smart education
• Deep learning/AI for smart health solution
• Deep learning/AI for Smart integrated grids
• Deep learning/AI for Security and Privacy of smart cities
• Blockchain for Deep learning/AI based finance industries of Smart Cities
The length of the articles should not exceed 6 pages in total. The guest
editors maintain the right to reject papers they deem to be out of scope of
this special issue. Only originally unpublished contributions and invited
articles will be considered for this special issue. The papers should be
formatted according to the ITL guidelines(https://onlinelibrary.wiley.com/page/journal/24761508/homepage/forauthors.html).
Authors should submit a PDF version of their complete manuscript via ITL
submission portal at (https://mc.manuscriptcentral.com/itl) according to
the timetable below.
For more information on formatting (Latex and word), please refer to:
Paper Submission Deadline: November 30, 2020
Author Notification: January 15, 2021
Uttam Ghosh, Vanderbilt University, USA, email@example.com
Chinmay Chakraborty, Birla Institute of Technology, Mesra, India, firstname.lastname@example.org
Xhaolong Ning, , Dalian University of Technology, China, email@example.com
Nawab Muhammad Faseeh Qureshi, Sungkyunkwan University, South Korea, firstname.lastname@example.org
Mamoun Alazab, Charles Darwin University, Australia, email@example.com
Al-Sakib Khan Pathan, Independent University, Bangladesh firstname.lastname@example.org
The 2020 IEEE Globecom Workshops (GC Wkshps): IEEE GLOBECOM Workshop on
Intelligent Fog and Edge Infrastructures for Future Wireless Systems will
be held in Taipei, Taiwan, from 7-11 December 2020.
To support future wireless systems, or Beyond 5G networks, fog and edge
have been emerging as key infrastructures within close proximity to the
end-users, where network intelligence and innovative service could be
efficiently implemented. Leveraged with Artificial Intelligence (AI)
techniques, fog and edge computing infrastructures have further been proved
distinctively effective to deliver optimized and customized services that
demand ultra-low latency and ultra-high bandwidth performance. Moving
computing powers to the edge and fog has also helped in rediscovering a
wealth of dormant assets in the local access networks. These dormant assets
are essentially contextual information collected directly from the users,
smart devices, and nodes. Therefore, fog and edge computing infrastructure
is playing an ever-increasingly crucial role of bringing the IoT, big data,
and distributed intelligence together for future wireless systems.
Nevertheless, designing intelligent Fog and Edge infrastructures posts some
real challenges. They include how AI can be applied for network slicing,
how to apply Machine Learning (ML) to streaming data, how to apply
reinforcement learning to resource allocation and load balancing, and even
how to build cohesive infrastructure to develop AI software, to name but a
few. Meanwhile, some practical issues, such as how to run AI training
algorithms in a power-efficient manner, and how to create and handle data
sets relevant enough to acquire knowledge, etc., should also be taken into
consideration. These challenges are currently being addressed in several
research projects (e.g., H2020 5GROWTH, 5G-DIVE) and standardization bodies.
Topics of Interest
This workshop expects to become a meeting point between industry and
academia to jointly provide valuable insights into the new definition of
intelligent fog and edge for future wireless systems. This workshop seeks
to attract high-quality contributions covering both theory and practice
overall aforementioned and other related aspects of AI-based fog and edge
infrastructure design and development. Some representative topics of
interest include, but are not limited to:
– Infrastructure Design for AI/ML-enabled Fog/Edge
– Edge data center network design for AI/ML-enabled Fog/Edge
– Energy efficient computing for AI/ML-enabled Fog/Edge
– Energy efficient architecture for AI/ML-enabled Fog/Edge
– Collaborative Edge-Fog Algorithms for AI/ML-enabled Fog/Edge
– Software Platform for AI/ML-enabled Fog/Edge
– Applications and Services for AI/ML-enabled Fog/Edge
– Virtualization Technologies for AI/ML-enabled Fog/Edge
– AI/ML for Network Slicing at Fog/Edge
– AI/ML for Resource Management at Fog/Edge
– AI/ML for Load Balancing at Fog/Edge
– AI/ML for Network Automation at Fog/Edge
Deadline for paper submission: August 1st, 2020
Date for notification: September 1st, 2020
Deadline for final paper submission: October 1st, 2020
Thank you! We look forward to seeing all of the great submissions.
GC 2020 Workshop – IntFog
Antonio de la Oliva
*International Workshop on **Artificial Intelligence in the IoT Security Services (AI-IOTS 2020)*
In conjunction with *ICSOC** 2020*
Dubai, UAE, December 14 – 17, 2020
· Workshop Papers Submission: *August 16, 2020*
– Authors Notification: *September 14, 2020*
– Workshop: *December 14, 2020*
– Submission site: <https://easychair.org/conferences/?conf=wesoacs2020>
Artificial Intelligence (AI) is one of the disciplines of computer science
that emerges in securing the Internet of Things (IoT) services. The AI
techniques including machine learning, deep learning, and reinforcement
learning approaches have been applied to emerging IoT applications in
various fields such as smart city, smart home, smart grid, health care,
smart transportation, smart farming, etc.
According to Gartner, the total number of IoT connected devices will reach
75.44 billion units worldwide by 2025. This trend poses several challenges
in building efficient and reliable IoT systems. Due to the advancement in
AI technology, the connected devices are getting smaller and smarter but
the secured services and constraints to be satisfied are increasingly
complex. The AI plays crucial role to manage huge data flows and storage in
the IoT network. As IoT gains its full potential, AI will be at the
forefront to promote the potential of IoT.
AI-IOTS 2020 welcomes papers on topics that include, but are not limited to
· Architecture design for AI in IoT
· Protocols for AI in IoT
· AI based techniques for real time IoT services
· AI based service-oriented protocols for IoT
· AI based techniques for data driven trustworthy IoT
· AI based techniques for security in IoT systems
· AI based techniques for fault diagnosis in IoT services
· AI based techniques for scalability solutions in IoT
· AI based techniques for smart data storage in IoT
· AI based applications in industrial IoT
· AI based techniques for smart city, smart grid, smart farming, smart transportation, and smart healthcare applications
· AI based techniques for IoT security and services
· AI based techniques for future IoT applications
– Authors are invited to submit original, previously unpublished
research papers. Papers should be written in English strictly following
Springer LNCS style for all text, references, appendices, and figures.
– The papers need to be submitted via the conference management tool
EASYCHAIR <https://easychair.org/conferences/?conf=aiiots2020> in PDF
format. For formatting instructions and templates see the Springer Web
page: Springer LNCS author web page <http://www.springer.de/comp/lncs/authors.html>.
– The workshop papers must not exceed 15 pages.
– Submitted papers will be peer-reviewed by at least two members of the
Workshop Program Committee.
– The accepted papers will be included in the conference proceedings
published by Springer Verlag in the Lecture Notes in Computer Science
*ORGANIZERS AND WORKSHOP CHAIRS*
*Dr. S. Selvakumar (*Indian Institute of Information Technology Una, Himachal Pradesh, India)
*Dr. R. Kanchana (*SSN College of Engineering, Chennai, Tamil Nadu, India)
For questions please email to the workshop chairs:
rkanch at ssn dot edu dot in