| News Details |
Publication of a Scientific Research within Scopus Index Q2 and clarivate Q3
2025-05-18
In a new scientific achievement that adds to the record of international academic cooperation, Assistant Professor Dr. Ahmed Talaat Hamoudi, Director of the Renewable Energy Research Center, published a scientific study in collaboration with a group of researchers from prestigious universities worldwide in the international journal "Physical Communication," which is listed in Scopus Q2. The paper is titled: "Artificial Intelligence Performance Evaluation for URLLC of Industrial IoT Applications: A Review"
I.F.: 2
Researchers from:
Queen's University Belfast - United Kingdom
UTHM - Malaysia
UTAR - Malaysia
University of Surrey - United Kingdom
Cqupt - China participated in the preparation of the study.The integration of fifth-generation/sixth-generation
ultra-reliable low-latency communication (URLLC) with Indus-
trial Internet of Things (IIoT) applications is revolutionizing
Industry 4.0 and enhancing the performance of IIoT applications
through Artificial Intelligence (AI) simulations. Industrial IoT
devices require low latency and high reliability, which can be
effectively addressed by leveraging AI techniques. Moreover, the
absence of AI techniques in industrial operations can result
in efficient decision-making, safety, quality predictions, and
employee adoption. However, integrating AI techniques into IIoT
applications can enhance industrial workflow while presenting
opportunities and challenges for achieving IIoT applications.
Machine learning (ML) and deep learning (DL) algorithms
enable industrial applications to operate efficiently and intel-
ligently. Furthermore, this study provides the requirement for
reliable and low-latency communication links between IIoT
devices. We present the primary research areas in which AI
algorithms can be employed, including faulty diagnosis, lung
cancer detection, intelligent anomaly detection, edge computing,
network performance, and intrusion detection systems in IIoT
applications. Special attention has been paid to the role of AI
techniques in enhancing the performance and efficiency of IIoT
systems. We highlight the advantages, purposes, applications, and
performances of these algorithms. In addition, we discuss the
current state-of-the-art challenges and future directions of AI in
IIoT, providing valuable insights that inspire further research.
Overall, there are many potential areas for further research on
AI and DL for Industrial IoT applications, including the devel-
opment of new techniques, integration with 5G/6G technologies,
autonomous decision-making and self-optimization of models,
addressing mission-critical applications, privacy measures, and
shifting AI processing and decision making to the edge. Finally,
this comprehensive review will benefit academics, researchers,
and professionals in AI and IIoT, as well as industries seeking
to leverage AI technologies to enhance the performance and
efficiency of their IIoT systems
