Special Issue on "Multimedia Analysis for Internet-of-Things"

in "IEEE Access" journal, IF: 3.22, Q1 (SCI/SCIE).

Submission Deadline: 30 January 2018

IEEE Access invites manuscript submissions in the area of Multimedia Analysis for Internet-of-Things.

Statistics reveal that the Internet traffic is shifting from non-multimedia data to multimedia data. This prevalent dominance signifies the importance and increase of multimedia usage in our day-to-day activities. Seamless integration, cooperative sensing, connectivity, and autonomy in the Internet-of-Multimedia-Things (IoMT) infrastructure opens doors to numerous opportunities to improve services and applications through efficient utilization of big multimedia data. However, the heterogeneous nature of big multimedia data demands scalable and customized recommendation frameworks for efficient analysis of big data collected in scenarios like surveillance, retail, telemedicine, traffic monitoring, and disaster management. Recommender systems are the technical response to the fact that we frequently rely on peoples’ experience, cultural norms and regional traditions when confronted with a new field of expertise, where we do not have a broad knowledge of all facts, or where such knowledge would exceed the amount of information humans can cognitively deal with. This observation in the real world suggests that recommender systems are an intuitive and valuable extension, allowing both end-users and multimedia service providers to take a much more active role in selecting semantically relevant content and providing valuable suggestions. For instance, in smart cities, multimedia sensors allow administrators to actively monitor assets and activities. Improvements in automatic interpretation of multimedia big data can enhance capacity of smart city administrators by autonomously reacting to emergency situations, and recommending effective actions, thereby reducing response times significantly. Furthermore, novel solutions for multimedia data processing and management in the IoMT ecosystem can enhance quality of life, urban environment, and smart city administration.

Big data processing includes both data management and data analytics. Data management step requires efficient cleaning, knowledge extraction, and integration and aggregation methods. Whereas, IoMT’s analysis is based on knowledge modelling, and interpretation which is more and more often performed by exploiting deep learning architectures. In couple of years, merging conventional and deep learning methodologies, have exhibited great promise in ingesting multimedia big data, exploring the paradigm of transfer learning, association rule mining, predictive analytics etc. Starting from the above considerations, this Special Section in IEEE Access aims to bring together researchers coming from both academia and industry, asking them to contribute in refining technologies and services aimed at personalization, monitoring, and recommendation in multimedia applications in the IoMT ecosystem based on deep architectures and conventional analysis methodologies, exploring their pros and cons in collaborative decision makings.

The topics of interest include, but are not limited to:

Management and interpretation of multimedia big data
Content and structure-based analytics
Feature learning from IoMT big data to facilitate monitoring, personalization, and recommendation
Methods for insurance data analysis and suggestions for warranty data analysis
Extraction of association rules using big data technologies
Multimedia technology for smart surveillance system with IoT environment
Scalable and semantics-driven indexing of ever growing multimedia data
Context-based summarization and abstraction of IoMT data
Combination of cloud computing and internet of things (IoT) in medical monitoring systems
Data sharing and interoperability of IoT systems
Multimedia processing for virtual reality applications
Data analysis for e-health applications
Role of transfer learning assisted strategies in multimedia analysis for IoMT
Online stream processing of multimedia data for smart cities applications
Efficient and scalable inference of IoMT-oriented deep models
Real-time vision through efficient deep convolutional neural networks (CNN)
Personalized and intelligent services based on multimedia analysis in IoMT environment
Optimizing deep CNNs for embedded vision applications
Embedded and cloud computing for ingesting big multimedia data in IoMT sensor networks
Cyber physical systems based solutions for big data security and privacy
Smarter surveillance applications for monitoring, and recommendation
Soft computing technologies for security assessment and privacy management of multimedia data in IoMT ecosystem
Real-time emergency detection through visual analytics and response recommendation
Scalable and efficient algorithms for big data analytics and data mining in IoMT systems
Information hiding solutions (steganography, watermarking) in smart cities
Evolutionary algorithms for multimedia analysis and recommendations in IoMT ecosystem
Multimodal features extraction techniques for multimedia data analysis in IoMT environment
Novel data collection, deep learning, reality mining, and prediction methods based on physical world observations
We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.



Associate Editor: Zhihan Lv, University College London, UK

Guest Editors:

Irfan Mehmood, Sejong University, South Korea
Mario Vento, University of Salerno, Italy
Minh-Son Dao, Universiti Teknologi Brunei, Brunei
Kaoru Ota, Muroran Institute of Technology, Japan
Alessia Saggese, University of Salerno, Italy


Relevant IEEE Access Special Sections:

Big Data Analytics in Internet-of-Things And Cyber-Physical System
Convergence of Sensor Networks, Cloud Computing, and Big Data in Industrial Internet of Things
Security and Privacy in Applications and Services for Future Internet of Things


IEEE Access Editor-in-Chief: Michael Pecht, Professor and Director, CALCE, University of Maryland

Paper submission: Contact Associate Editor and submit manuscript to:
http://mc.manuscriptcentral.com/ieee-access

For inquiries regarding this Special Section, please contact: Zhihan Lv (lvzhihan@gmail.com) or Irfan Mehmood (irfan@sejong.ac.kr)