Multimedia Analysis for Internet-of-Things

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:

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


Relevant IEEE Access Special Sections:

  1. Big Data Analytics in Internet-of-Things And Cyber-Physical System
  2. Convergence of Sensor Networks, Cloud Computing, and Big Data in Industrial Internet of Things
  3. 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:

For inquiries regarding this Special Section, please contact: Zhihan Lv ( or Irfan Mehmood (