[Om-announce] CFP to Special Issue in ACM TOSN on Distributed and Collaborative Learning Empowered Edge Intelligence in Smart City - Deadline Nov. 30, 2022

[Xiaokang Zhou]

[Apologies for multiple postings]

Dear Professor(Dr.),

It is my pleasure and honor to share you this information of CFP in the ACM
Transactions on Sensor Networks.

Please consider submitting a paper to a Special Issue on "Distributed and
Collaborative Learning Empowered Edge Intelligence in Smart City" for ACM
TOSN. The deadline is Nov. 30, 2022.
https://dl.acm.org/pb-assets/static_journal_pages/tosn/pdf/ACM_TOSN_CFP1210-1640635690003.pdf

Also, please kindly help distribute the CFP (see following) and encourage
your colleagues, friends, and students to make submission. Your strong
supports are highly appreciated.


Best regards,
Xiaokang Zhou, on behalf of guest editors


[ACM TOSN Call for Papers]
===========================================================================================
                               ACM Transactions on Sensor Networks

                                         Special Issue on

     Distributed and Collaborative Learning Empowered Edge Intelligence in
Smart City

---------------------------------------------------------------------------------------------------

Aims and Scope
Smart city serves as an important concept utilizing modern artificial
intelligence (AI) techniques, such as machine learning and deep learning
models, to improve urban behavior and capabilities for the next-generation
civilization constructions. Typical smart city infrastructure systems
include smart transportation systems, smart buildings, smart grid, smart
medical systems, and smart housing systems, etc. The smart operation
systems are among the most popular research topics in the fields of
information technology (IT), AI, Internet of things (IoT), cyber-physical
systems (CPS), and intelligent systems, etc. However, when tremendous
amount of data is collected in the early stage of development of smart
city, there exists an urgent demand of decentralized training and learning
technologies, since a centralized system is almost not feasible for big
data management and analytics in smart cities.

Distributed learning (DL) and collaborative learning (CL) are classic
decentralized learning paradigms managing and processing big data and
heavily-loaded resources for smart cities, where DL focuses on learning
mechanisms on different clients through an IoT network system, and CL
focuses on the integration of the distributed learning on different
clients. In particular, the emergence of edge intelligence provides DL and
CL with the computational power of the heterogeneous devices on the outer
edge of the IoT network, which leverages the robustness optimization of
network topology for IoT, and consequently achieves higher efficiency and
better performance.

Topics may include (but are not limited to):

• Distributed and collaborative learning in intelligent end-edge-cloud
systems
• Distributed and collaborative learning in smart CPS
• Distributed and collaborative learning in mobile and ubiquitous computing
• Distributed and collaborative learning with intelligent IoT for smart
healthcare
• Distributed and collaborative learning based computer vision in smart
cities
• Distributed and collaborative learning based speech assistants in smart
cities
• Distributed and collaborative learning solutions on trust system
development
• Edge intelligence for smart environment design, construction and
maintenance
• Edge intelligence in daily living support
• Edge intelligence in sustainable computing
• Edge intelligence in distributed design
• Edge intelligence in cyber security and privacy concerns
• Intelligent sensing data applications in smart cities
• Big data analytics for smart city management
• Knowledge-based or agent-based models for intelligent systems
• Distributed IoT in smart services
• Intelligent devices and process-aware information systems in smart cities

Submissions
Submissions to the special issue will be screened by the Special Issue
Editors to ensure that they conform to the quality standards of ACM
Transactions on Sensor Networks (TOSN). Papers that do not pass this
initial screening will be immediately returned to the authors. Reviewers
will apply those standards in forming recommendations for acceptance,
revision, or rejection. Papers should be formatted with TOSN style (
https://dl.acm.org/journal/tosn/author-guidelines). Prospective
contributors should submit their papers directly to the online submission
system (https://mc.manuscriptcentral.com/tosn). In addition, Authors please
choose the Special Issue on Distributed and Collaborative Learning
Empowered Edge Intelligence in Smart City in the online submission.

Important Dates
• Open for submissions:                Aug. 31, 2022
• Submissions deadline:                Nov. 30, 2022
• First-round review decisions:        Feb. 28, 2023
• Deadline for revision submissions:   Apr. 30, 2023
• Notification of final decisions:     Jun. 30, 2023
• Tentative publication:               Jul. 30, 2023

Guest editors:
• Prof. Xiaokang Zhou, Shiga University, Japan, Email:
zhou at biwako.shiga-u.ac.jp
• Prof. Vincenzo Piuri, University of Milan, Italy, Email:
vincenzo.piuri at unimi.it
• Prof. Henry Leung, University of Calgary, Canada, Email:
leungh at ucalgary.ca

Contact Information
Corresponding Guest Editor, Dr. Zhou (zhou at biwako.shiga-u.ac.jp)
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