IEEE Workshop on Advances and Trends on Deep Learning for Internet of Things (DL-IoT'19) with CyberSciTech 2019, August 5-9, 2019, Fukuoka, Japan
IEEE DL-IoT 2019
- URL: https://sites.google.com/view/ieee-dl-iot/home
- Event Date: 2019-08-05 ~ 2019-08-09
- Submission Date: 2019-04-20
- Location: Fukuoka, Japan
[Apologies, if you receive multiple copies of this CFP]
CALL FOR PAPERS
IEEE Workshop on Advances and Trends on Deep Learning for Internet of Things (DL-IoT'19)
In Conjunction with IEEE Cyber Science and Technology Congress (CyberSciTech 2019)
August 5-9, 2019, Fukuoka, Japan
URL: https://sites.google.com/view/ieee-dl-iot/home
Internet of Things (IoT) is an interconnection of several devices, networks, technologies and human resources to achieve a common goal. There is a variety of IoT based applications that are being used in different sectors and have succeeded in providing huge benefits to the users. Data Analytics (DA) is defined as a process which is used to examine big and small data sets with varying data properties to extract meaningful conclusions from these data sets. Data Analytics has a significant role to play in the growth and success of IoT applications and investments. The utilization of data analytics shall, therefore, be promoted in the area of IoT to gain improved revenues, competitive gain, and customer engagement. 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, wearable devices, cloud computing and data analytics offer the potential of acquiring and processing tremendous amount of data from the physical world. Promising computing paradigms and advanced technologies (e.g., Smart home or city) relating to context awareness systems, activity recognition, distributed smart sensing, heterogeneous big data analytics, and deep learning, etc., have been increasingly developed and integrated into this IoT systems in order to make IoT a reality.
This workshop on “Advances and Trends on Deep Learning for Internet of Things” aims at promoting and facilitating exchanges of research knowledge and findings across different disciplines on the design and investigation of deep learning based data Analytics of IoT infrastructures. In addition, this issue encourages and invites researches with great significance and impact on:
1) Addressing the emerging trends and issues on IoT systems and services across various application domains.
2) Investigating the challenges posed by the implementation DL on IoT networking models and services.
3) Providing fundamental theory, model, and methodology in interpreting, aggregating, processing/analyzing data for intelligent DL enabled IoT.
4) Exploring new functions and technologies to provide adaptive services and intelligent applications for different end users.
We are also currently finalizing agreements with a journal publisher for a special topics issue on this workshop theme. A selected set of accepted top-quality papers may have their authors invited to submit full papers to that special issue. Details will be finalized before the IEEE CyberSciTech 2019 conference.
Please send your any inquiries to uttam.ghosh@vanderbilt.edu or sayans@slis.tsukuba.ac.jp
Topics
This workshop solicits contributions from the field of IoT Data analytics using deep learning. 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 perspectives. Topics to be discussed in this workshop include but not are limited to:
- Deep learning and IoT
- Recent trends and advances in DL based IoT
- Heterogeneous Big data analytics for edge/fog Computing
- Novel Network Structures and System Design for DL enabled IoT
- Deep Learning architecture for IoT security
- Deep Learning experiments, test-beds and prototyping systems for IoT security•
- Context-Aware Deep Learning based approaches for IoT
- Data confidentiality, Trust and privacy in DL enabled IoT Applications
- Analysis of Network Dynamics in IoT (this belongs to only IoT)
- Authentication and access control for data usage in IoT (this belongs to only IoT)
- Data mining and statistical modelling for the secure IoT
- User Interface design and implementation for DL enabled IoT
- User Interface design and evaluation for DL enabled IoT
- IoT/big data visualization techniques and application of DL
- Activity recognition in a smart home/city using deep learning
Important Dates
Paper Submission Due: April 20, 2019
Acceptance Notification Due: May 25, 2019
Final Manuscript Due: June 20, 2019
Conference: August 5-8, 2019
Submission Guideline
Regular papers (between 4-6 pages) should present novel perspectives within the general scope of the conference. Extended Abstracts for Posters (work in progress and Industry, 2 pages) are intended for ongoing research projects, concrete realizations, or industrial applications/projects presentations. We encourage submissions on innovative solutions and applications related to commercial or industrial-strength solutions. The IEEE templates in Microsoft Word (US Letter) and LaTeX format can be found at:
http://www.ieee.org/conferences_events/conferences/publishing/templates.html
Paper submission is also considered as a declaration that the same paper has not been accepted or published before or has not been submitted elsewhere for review. Original papers with sufficient technical merit are sought.
Committee
General Chair
Xiangshi Ren, Kochi University of Technology, Japan, ren.xiangshi@kochi-tech.ac.jp
Program Co-Chairs
Uttam Ghosh, Vanderbilt University, Nashville, USA, uttam.ghosh@vanderbilt.edu
Sayan Sarcar, University of Tsukuba, Japan, sayans@slis.tsukuba.ac.jp
Taro Tezuka, University of Tsukuba, Japan, tezuka@slis.tsukuba.ac.jp
Publicity Chair
Al-Sakib Khan Pathan, Southeast University, Bangladesh, spathan@ieee.org
===============================================================
Regards,
Sakib
Publicity Chair
CALL FOR PAPERS
IEEE Workshop on Advances and Trends on Deep Learning for Internet of Things (DL-IoT'19)
In Conjunction with IEEE Cyber Science and Technology Congress (CyberSciTech 2019)
August 5-9, 2019, Fukuoka, Japan
URL: https://sites.google.com/view/ieee-dl-iot/home
Internet of Things (IoT) is an interconnection of several devices, networks, technologies and human resources to achieve a common goal. There is a variety of IoT based applications that are being used in different sectors and have succeeded in providing huge benefits to the users. Data Analytics (DA) is defined as a process which is used to examine big and small data sets with varying data properties to extract meaningful conclusions from these data sets. Data Analytics has a significant role to play in the growth and success of IoT applications and investments. The utilization of data analytics shall, therefore, be promoted in the area of IoT to gain improved revenues, competitive gain, and customer engagement. 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, wearable devices, cloud computing and data analytics offer the potential of acquiring and processing tremendous amount of data from the physical world. Promising computing paradigms and advanced technologies (e.g., Smart home or city) relating to context awareness systems, activity recognition, distributed smart sensing, heterogeneous big data analytics, and deep learning, etc., have been increasingly developed and integrated into this IoT systems in order to make IoT a reality.
This workshop on “Advances and Trends on Deep Learning for Internet of Things” aims at promoting and facilitating exchanges of research knowledge and findings across different disciplines on the design and investigation of deep learning based data Analytics of IoT infrastructures. In addition, this issue encourages and invites researches with great significance and impact on:
1) Addressing the emerging trends and issues on IoT systems and services across various application domains.
2) Investigating the challenges posed by the implementation DL on IoT networking models and services.
3) Providing fundamental theory, model, and methodology in interpreting, aggregating, processing/analyzing data for intelligent DL enabled IoT.
4) Exploring new functions and technologies to provide adaptive services and intelligent applications for different end users.
We are also currently finalizing agreements with a journal publisher for a special topics issue on this workshop theme. A selected set of accepted top-quality papers may have their authors invited to submit full papers to that special issue. Details will be finalized before the IEEE CyberSciTech 2019 conference.
Please send your any inquiries to uttam.ghosh@vanderbilt.edu or sayans@slis.tsukuba.ac.jp
Topics
This workshop solicits contributions from the field of IoT Data analytics using deep learning. 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 perspectives. Topics to be discussed in this workshop include but not are limited to:
- Deep learning and IoT
- Recent trends and advances in DL based IoT
- Heterogeneous Big data analytics for edge/fog Computing
- Novel Network Structures and System Design for DL enabled IoT
- Deep Learning architecture for IoT security
- Deep Learning experiments, test-beds and prototyping systems for IoT security•
- Context-Aware Deep Learning based approaches for IoT
- Data confidentiality, Trust and privacy in DL enabled IoT Applications
- Analysis of Network Dynamics in IoT (this belongs to only IoT)
- Authentication and access control for data usage in IoT (this belongs to only IoT)
- Data mining and statistical modelling for the secure IoT
- User Interface design and implementation for DL enabled IoT
- User Interface design and evaluation for DL enabled IoT
- IoT/big data visualization techniques and application of DL
- Activity recognition in a smart home/city using deep learning
Important Dates
Paper Submission Due: April 20, 2019
Acceptance Notification Due: May 25, 2019
Final Manuscript Due: June 20, 2019
Conference: August 5-8, 2019
Submission Guideline
Regular papers (between 4-6 pages) should present novel perspectives within the general scope of the conference. Extended Abstracts for Posters (work in progress and Industry, 2 pages) are intended for ongoing research projects, concrete realizations, or industrial applications/projects presentations. We encourage submissions on innovative solutions and applications related to commercial or industrial-strength solutions. The IEEE templates in Microsoft Word (US Letter) and LaTeX format can be found at:
http://www.ieee.org/conferences_events/conferences/publishing/templates.html
Paper submission is also considered as a declaration that the same paper has not been accepted or published before or has not been submitted elsewhere for review. Original papers with sufficient technical merit are sought.
Committee
General Chair
Xiangshi Ren, Kochi University of Technology, Japan, ren.xiangshi@kochi-tech.ac.jp
Program Co-Chairs
Uttam Ghosh, Vanderbilt University, Nashville, USA, uttam.ghosh@vanderbilt.edu
Sayan Sarcar, University of Tsukuba, Japan, sayans@slis.tsukuba.ac.jp
Taro Tezuka, University of Tsukuba, Japan, tezuka@slis.tsukuba.ac.jp
Publicity Chair
Al-Sakib Khan Pathan, Southeast University, Bangladesh, spathan@ieee.org
===============================================================
Regards,
Sakib
Publicity Chair
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