Call for Papers

HKIE Transactions

9th Theme Issue

Theme Topic on “Deep Learning for IoT Big Data and Streaming Analytics”




Submission Deadline:
28 February 2024

Publication Date:
June 2024



The HKIE Transactions Committee is honoured to announce that Prof Shahid MUMTAZ, Professor of Digital Innovation in Nottingham Trent University, and Dr Zhigao ZHENG, Associate Professor at Wuhan University, have accepted our invitation to be the Guest Editors for the Theme Issue in the HKIE Transactions on the topic "Deep Learning for IoT Big Data and Streaming Analytics".  This Theme Issue will be published in the HKIE Transactions in June 2024.  You are invited to submit manuscripts to this Theme Issue.  Detailed information is as follows:



In the era of the Internet of Things (IoT), an enormous diversity of sensing devices collect and/or generate various sensory data over time for a wide range of fields and applications. Based on the nature of the application, these devices will create big or fast/real-time data streams. Applying analytics to such data streams for discovering new information, predicting future insights, and making control decisions is a crucial process that makes IoT a worthy paradigm for businesses and a quality-of-life improvement technology. In the last few years, big data analytics technologies have made a great contribution to this aspect.


Beyond big data analytics, IoT data calls for another new class of analytics, namely fast and streaming data analytics, to support applications with high-speed data streams that require time-sensitive (i.e., real-time or near real-time) actions. Indeed, applications such as autonomous driving, fire prediction, and driver/elderly posture (and thus consciousness and/or health condition) recognition that demand fast processing of incoming data and quick actions to achieve their target. In recent years, many researchers have proposed approaches and frameworks for fast-streaming data analytics that leverage the capabilities of cloud infrastructures and services. However, for the aforementioned IoT applications among others, we need fast analytics in smaller scale platforms (i.e., at the system edge) or even on the IoT devices themselves. For example, autonomous cars need to make fast decisions on driving actions such as lane or speed changes. Indeed, this kind of decision should be supported by fast analytics of possibly multi-modal data streaming from several sources, including multiple vehicle sensors (e.g., cameras, radars, LIDARs, speedometer, left/right signals, etc.), communications from other vehicles, and traffic entities (e.g., traffic light, traffic signs). In this case, transferring data to a cloud server for analysis and returning back the response is subject to the latency that could cause traffic violations or accidents. A more critical scenario would be detecting pedestrians by the vehicles. Accurate recognition should be performed in strict real-time to prevent fatal accidents. These scenarios imply that fast data analytics for IoT has to be close to or at the source of data to remove unnecessary and prohibitive communication delays.


This theme issue aims to present a collection of high-quality research papers on the state of the art in emerging technologies for the applications of recent trends in Deep Learning (DL) technologies for the IoT domain. We are soliciting original contributions that have not been published and are not currently under consideration by any other journals. Both theoretical studies and state-of-the-art practical applications are welcome for submission. All submitted papers will be peer-reviewed and selected on the basis of both their quality and their relevance to the theme of this theme issue.


Topics of interest include, but are not limited to, the following scopes:

  • Deep Learning (DL) technologies for IoT domain applications
  • Machine learning for IoT data processing
  • IoT big data analytics and IoT streaming data analytics
  • Emerging DL techniques for IoT data analytics
  • DL for smart IoT devices
  • Data Driven Decision Making Systems in IoT applications
  • Deep Learning Models for Time Series Data and IoT
  • Multi-Task IoT System Modelling and analysis
  • Hybrid Intelligent Models for IoT Context-Aware Systems
  • Multimodal data analysis and information fusion in IoT
  • Prediction of situational awareness with IoT data
  • Streaming data learning algorithms for IoT
  • Swarm Intelligence and Big Data for IoT
  • Cloud-Assisted Data Fusion and sensor selection for Internet of Things
  • Secure and privacy preserving steam analytics
  • IoT analytics for improving the dependability of IoT systems
  • Emerging hardware architectures for IoT and Big Data
  • IoT and Big Data Analytics on Energy-Constrained platforms
  • Optimisation, control, and automation
  • Computational and Artificial Intelligence algorithms
  • Fog and Cloud Computing for (near) real-time analytics
  • Smart cities and systems
  • Blockchain for data security and privacy
  • Fault tolerant, redundant systems
  • Visualisation techniques


Submission Guidelines

The deadline for final manuscript submission is Thursday, 29 February 2024. All manuscripts should be submitted through the HKIE Transactions ScholarOne Manuscripts site at New users should first create a login account. Once logged on to the site, submissions should be made via the Author Centre. For more details regarding the author guidelines, please refer to the Instructions for Authors and Referencing Style.


Guest Editors

Prof Shahid MUMTAZ 

Prof MUMTAZ is a professor of Digital Innovation in Nottingham Trent University. He is also an IET Fellow, IEEE ComSoc, VTS, IAS and ACM Distinguished speaker, recipient of IEEE ComSoC Young Researcher Award, founder and EiC of IET "Quantum Communication", EiC of Alexandria Engineering Journal – Elsevier, Vice-Chair: Europe/Africa Region - IEEE ComSoc: Green Communications & Computing society and Vice-chair for IEEE standard on P1932.1: Standard for Licensed/Unlicensed Spectrum Interoperability in Wireless Mobile Networks. His work resulted in technology transfer to companies and patented technology. His expertise lies in 5G/6G wireless technologies using AI/ML and Digital Twin (VR/XR) tools and innovation path towards industrial and academic. Moreover, he worked as a Senior 5G Consultant at Huawei and InterDigital, contributing to RAN1/RAN2. 


Dr Zhigao ZHENG

Dr ZHENG is an associate professor at Wuhan University. He got a Ph.D. degree from Huazhong University of Science and Technology and the master's degree from Peking University. His current research interests focus on cloud computing, big data processing, and AI systems. He published more than 20 peer-reviewed publications (such as the IEEE Transactions on Parallel and Distributed Systems, IEEE Transactions on Computers). He joins the research projects of various governmental and industrial organisations, such as the National Science Foundation of China, Ministry of Science and Technology, and Ministry of Education.


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  1. The HKIE Transactions reserves the right to withhold any or all of the manuscripts at their absolute discretion.
  2. The HKIE Transactions' decision is final and no correspondence will be entered into.
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