CHEDDAR Researcher Showcases Digital Twin and AI Innovations at International Conferences

Congratulations to CHEDDAR researcher Dr Huijun Tang on a series of recent research achievements, including an international workshop demonstration, a conference presentation and a new paper acceptance.

Interactive Digital Twin Demonstrated at ENSsys 2026

On 11 May 2026, Dr Huijun Tang presented the paper “An Interactive Digital-Twin Dashboard for Energy Harvesting Assisted IIoT” at the 14th International Workshop on Energy Harvesting & Energy-Neutral Sensing Systems (ENSsys 2026) in Saint-Malo, France.

The work, co-authored with Ling Li, Chang Liu and Prof Hongjian Sun, was showcased through a live demonstration of an interactive digital twin dashboard designed to support energy harvesting-assisted Industrial Internet of Things (IIoT) networks. The demonstration highlighted how digital twin technologies can provide real-time visualisation and optimisation of energy-aware IoT systems.

Research Presented at IEEE ICC 2026

Dr Tang also contributed to a paper presented at the IEEE International Conference on Communications (ICC 2026), held in Glasgow, UK, from 23–28 May 2026.

The paper, “Graph Reinforcement Learning Based Resource Allocation Method in RIS-Aided Heterogeneous Internet of Vehicles”, was authored by Wang Zeng, Huijun Tang, Pinlong Zhao, Pengfei Jiao, Huaifeng Shi, Huaming Wu and Hongjian Sun.

The research explores how graph reinforcement learning can be used to optimise resource allocation in reconfigurable intelligent surface (RIS)-assisted heterogeneous Internet of Vehicles (IoV) environments, helping to improve network efficiency and performance in future intelligent transportation systems.

Paper Accepted at CSNDSP 2026

Further recognition came with the acceptance of the paper “Explainable AI Based Feature Selection Method for Energy Harvesting Assisted IoT Network” at the 15th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP 2026).

The paper, authored by Huijun Tang, Wang Zeng, Min Xue, Junhui Du, Zhizhou He, Pengfei Jiao, Huaming Wu and Hongjian Sun, investigates the use of explainable artificial intelligence techniques to improve feature selection within energy harvesting-assisted IoT networks. By increasing transparency in AI-driven decision-making, the research aims to support more efficient and trustworthy intelligent network management.

These achievements highlight CHEDDAR’s continued contributions to advancing intelligent, energy-efficient and AI-enabled communications systems for future networks.

Congratulations to Dr Huijun Tang and collaborators on these accomplishments.