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IoMT Big Data 2017 : Towards Smarter Cities: Learning from Internet of Multimedia Things-Generated Big Data


When N/A
Where N/A
Submission Deadline Sep 1, 2017
Notification Due Nov 15, 2017
Final Version Due Mar 30, 2018
Categories    iomt   big data   smarter cities   deep learning

Call For Papers

Call for Papers in Elsevier Journal "Future Generation Computer Systems" SCI/SCIE, IF: 3.997.

Towards Smarter Cities: Learning from Internet of Multimedia Things-Generated Big Data

Scope and Objective

Smart city’s IoT-based infrastructures envision improvement in quality of life through optimal utilization of resources. Integrating diverse sensors through communication technologies generate big data which is collected, processed, and analyzed, revealing knowledge and information to realize the goals of smart cities. Multimedia sensors serve as the eyes and ears of smart city administrators, enabling them to monitor activities and assets. The big multimedia data generated by these sensors contain a wealth of information, needed to be processed and analyzed for knowledge extraction. However, the huge volume of this data and its inherent complexity hinders ability of traditional computing infrastructures and algorithms to effectively process and extract actionable intelligence from it. There is a growing demand for efficient yet powerful algorithms to consume internet of multimedia things (IoMT)-generated big data and extract needed information from it to run the affairs of smart cities. Deep learning based methods for multimedia data processing and understanding has shown great promise in the recent years. This special issue aims to highlight problems and future challenges in smart cities and invite researchers working towards smart cities and associated technologies like IoMTs, machine learning for big data, and embedded/cloud computing, to develop novel methods for addressing issues related to the transmission, processing, representation, and storage of IoMT-generated big data. It also invites novel deep learning based solutions for real-time data processing, learning from multi-modal big data, distributed learning paradigms with embedded processing, and efficient inference.


This special issue calls for original works revealing the latest research on deep learning from big multimedia data for smart cities. The proposed non-exhaustive list of topics in this special issue is as follows:

Data collection and storage for deep learning in smart cities
Supervised, semi-supervised, and unsupervised feature learning from IoMT big data
Scalable and semantics-driven indexing of big multimedia data in smart cities
Context-based summarization and abstraction of IoMT big data
Online stream processing of IoMT big data for smarter cities applications
Efficient and scalable inference of IoMT-oriented deep models
Real-time vision through efficient deep convolutional neural networks (CNN)
Optimizing deep CNNs for embedded vision in smart cities
Utilizing embedded processing for ingesting big multimedia data in IoMT sensor networks
Physical cyber systems related solutions for big data security and privacy in smart cities
Smarter surveillance
Real-time emergency detection through visual analytics and response invocation
Information hiding solutions (steganography, watermarking) in smart cities
Important Dates

Manuscripts Due: September 01, 2017
First Decision Date: November 15, 2017
Revision Due: December 30, 2017
Final Decision Date: February 30, 2018
Final Paper Due: March 30, 2018

Guest Editors

Dr. Paolo Bellavista (Leading GE)
Associate Professor, University of Bologna, Italy

Dr. Kaoru Ota
Assistant Professor, Muroran Institute of Technology, Japan

Dr. Zhihan Lv
Research Associate, University College London, UK
Email: ,

Dr. Irfan Mehmood
Assistant Professor, Sejong University, Seoul, Republic of Korea

Dr. Seungmin Rho
Assistant Professor, Sungkyul University, Anyang, Republic of Korea

Paper Submission

Authors should prepare their manuscript according to the Guide for Authors available from the online submission page of the Future Generate Computer Systems at Authors must select article type name of "SI:IoMT Big Data"

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