The 1st International Workshop on Deep Learning for the Web of Things (DLWoT 2021)

 

In conjunction with

The Web Conference 2021 (formerly known as WWW conference)

 

April 19-23, 2021, Ljubljana, Slovenia

 

Scope

In recent years, the techniques of Internet of Things (IoT) and Web of Things (WoT) have been more and more popular to collect sensing data and build intelligent services and applications. Some organizations (e.g., oneM2M, AllSeen Alliance, Open Connectivity Foundation (OCF), IEEE, etc.) were instituted to establish the standards and specifications of IoT for building an IoT ecosystem. These standards and specifications discuss the issues of data models, unique identification of things, service descriptions and dependencies, discovery, trust management, and real-time control and cyber-physical systems. For instance, the AllSeen Alliance and OCF designed discovery and advertisement mechanisms to send multicast packets to find the adapted devices which include the target interface in wireless local area network based on IEEE 802.11 or personal area network based on IEEE 802.15 for building a self-organizing network. The devices can follow the data models and control methods in specifications to control other AllJoyn or OCF devices for IoT applications. However, the communications among the different techniques of IoT standards and specifications are the important challenges. Therefore, the interoperation of services across platforms based on different IoT standards and specifications needs to be investigated. For example, the Interworking Proxy Entity (IPE) was designed to establish the connection of oneM2M, AllJoyn, OCF, and Lightweight M2M in oneM2M's Release 2. The WoT defined by the World Wide Web Consortium (W3C) focuses on the web technologies for the combination and interoperation of the IoT with the web of data. Developers can use the techniques of WoT to collect the sensing data and control the devices via different IoT standards and specifications for the applications of agriculture, energy, enterprise, finance, healthcare, industry, public services, residency, retail, and transportation.

 

Furthermore, Deep learning techniques (e.g. neural network (NN), convolutional neural network (CNN), recurrent neural network (RNN), long short-term memory (LSTM), etc.) have been popularly applied into image recognition and time-series inference for IoT and WoT applications. Advanced driver assistance systems and autonomous cars, for instance, have been developed based on the machine learning and deep learning techniques, which perform the forward collision warning, blind spot monitoring, lane departure warning, traffic sign recognition, traffic safety, infrastructure management and congestion, and so on. Autonomous cars can share their detected information (e.g. traffic signs, collision events, etc.) with other cars via vehicular communication systems (e.g. dedicated short range communications (DSRC), vehicular ad hoc networks (VANETs), long term evolution (LTE), and the 5th generation mobile networks) for cooperation. However, how to enhance the performance and efficiency of these deep learning techniques is one of the big challenges for implementing these real-time applications. Several optimization techniques (e.g. stochastic gradient descent algorithm (SGD), adaptive moment estimation algorithm (Adam), Nesterov-accelerated Adaptive Moment Estimation (Nadam), etc.) have been proposed to support deep learning algorithms for faster solution searching, e.g., the gradient descent method is a popular optimization technique to quickly seek the optimized weight sets and filters of CNN for image recognition. The IoT and WoT applications based on these image recognition techniques (e.g. autonomous cars, augmented reality navigation systems, etc.) have gained considerable attention, and the hybrid approaches typical of mathematics for engineering and computer science (e.g. deep learning and optimization techniques) can be investigated and developed to support a variety of IoT and WoT applications.

 

This workshop will solicit papers on various disciplines, which include but are not limited to:

 

Topics

Ø  Deep learning for massive WoT

Ø  Deep learning for critical WoT

Ø  Deep learning for enhancing WoT security

Ø  Deep learning for enhancing WoT privacy

Ø  Preprocessing of WoT data for AI modeling

Ø  Deep learning for WoT applications (e.g., smart home, smart agriculture, interactive art, etc.)

 

Important Dates:

Paper Submission Deadline

January 11, 2021

Author Notification

February 08, 2021

Camera-ready Submission

March 01, 2021

Conference Dates

April 19-23, 2021

 

Organizing Committee

Steering Committee

Ø Prof. Wenzhong Guo (Fuzhou University, China)

Ø Prof. Chin-Chen Chang (IEEE Fellow; Feng-Chia University, Taiwan)

Ø Prof. Eyhab Al-Masri (University of Washington Tacoma, United States of America)

 

General Chairs

Ø Prof. Chi-Hua Chen (Fuzhou University, China)

Ø Prof. Haishuai Wang (Fairfield University & Harvard University, USA)

Ø Prof. K. Shankar (Alagappa University, India)

 

Session Chairs

Ø Prof. Feng-Jang Hwang (University of Technology Sydney, Australia)

Ø Prof. Fuquan Zhang (Minjiang University, China)

Ø Prof. Chia-Yu Lin (Yuan Ze University, Taiwan)

Ø Prof. Cheng Shi (Xi'an University of Technology, China)

Ø Prof. Brij B. Gupta (National Institute of Technology Kurukshetra, India)

 

Technical Program Committee

Ø Prof. Xiao-Guang Yue (European University Cyprus, Cyprus)

Ø Prof. Sabu M. Thampi (Indian Institute of Information Technology and Management-Kerala, India)

Ø Dr. Moayad Aloqaily (Gnowit Inc., Ottawa, Canada)

Ø Dr. Ching-Chun Chang (Tsinghua University, China)

Ø Prof. Chunjia Han (University of Greenwich, United Kingdom)

Ø Dr. Doris Xin (Newcastle University, United Kingdom)

Ø Dr. Lingjuan Lyu (National University of Singapore, Singapore)

Ø Prof. Ting Bi (Maynooth University, Ireland)

Ø Prof. Fang-Jing Wu (Technische Universität Dortmund, Germany)

Ø Dr. Paula Fraga-Lamas (Universidade da Coruña, Spain)

Ø Prof. Usman Tariq (Prince Sattam bin Abdulaziz University, Saudi Arabia)

Ø Prof. Hsu-Yang Kung (National Pingtung University of Science and Technology, Taiwan)

Ø Prof. Chin-Ling Chen (Chaoyang University of Technology, Taiwan)

Ø Prof. Hao-Chun Lu (Chang Gung University, Taiwan)

Ø Prof. Yao-Huei Huang (Fu-Jen Catholic University, Taiwan)

Ø Prof. Hao-Hsiang Ku (National Taiwan Ocean University, Taiwan)

Ø Prof. Yu-Chih Wei (National Taipei University of Technology, Taiwan)

Ø Prof. Hsiao-Ting Tseng (National Central University, Taiwan)

Ø Prof. Liang-Hung Wang (Fuzhou University, China)

Ø Prof. Fangying Song (Fuzhou University, China)

Ø Prof. Genggeng Liu (Fuzhou University, China)

Ø Prof. Chan-Liang Chung (Fuzhou University, China)

Ø Prof. Lianrong Pu (Fuzhou University, China)

Ø Dr. Ling Wu (Fuzhou University, China)

Ø Dr. Xiaoyan Li (Fuzhou University, China)

Ø Prof. Chih-Min Yu (Yango University, China)

Ø Prof. Lei Xiong (Guangzhou Academy of Fine Arts, China)

Ø Prof. Bo-Wei Zhu (Macau University of Science and Technology, Macau)

Ø Dr. Insaf Ullah (Hamdard University, Pakistan)

 

Publication

The proceedings of the workshops will be published jointly with the conference proceedings.

 

Submission and Publication Information

All workshop papers should be no more than 10 pages in length. Papers must be submitted in PDF according to the ACM format published in the ACM guidelines, selecting the generic “sigconf” sample. The PDF files must have all non-standard fonts embedded. Workshop papers must be self-contained and in English. All papers need to be submitted electronically through the conference website (https://easychair.org/conferences/?conf=dlwot2021) with PDF format.

 

Contact

Prof. Chi-Hua Chen, Email: chihua0826@gmail.com