Grant: Discovery Grants Program - Individual

We are witnessing major growths towards the emerging areas of Society 5.0 and Sixth Generation (6G) mobile communication, which collectively have the potential of incorporating human-centric applications in Cyber-Physical Systems (CPSs). From a humanistic perspective, the multi-faceted communication capabilities of 6G enables the creation of Cyber-Physical-Human (CPH) worlds with ubiquitous connectivity between humans and their borderless environments. The CPH worlds would be enabled by next generation Internet of Everything (IoE) applications such as autonomous systems, Brain-Computer Interfacing (BCI), and Human-Machine Interfacing (HMI). Facilitating such IoE applications, however, requires research, development, and implementation of a novel unified framework to satisfy their diverse requirements in terms of scalability, trustworthiness, elasticity, and geo-distribution. Such a unified framework can be achieved by advancing the Digital Twins (DT) concept. The overall objective of this research program is to research promising ideas in the design, development, and application of Signal Processing (SP) and Machine Learning (ML) models within the context of DTs. The DT can be defined as a digital replica of a living (non-living) entity reflecting all relevant properties of its Physical Twin (PT) throughout its life cycle. DTs are poised to revolutionize CPSs to their next phase (the CPH worlds) and flourish future 6G communication networks. To pave the way towards the development a human-centric DT system and to bridge existing gap between physical and digital worlds, the SP/ML systems in place should be: (i) Scalable to meet latency constraints for inclusion of massive number of connected devices; (ii) Elastic to perform real-time resource allocation in response to highly dynamic requirements, and; (iii) Cooperative to benefit from geographically distributed DT. Realization of such a human-centric and distributed SP/ML system requires fundamental research and robust mathematical modeling to address the following key research challenges: (i) Capturing the complex/dynamic relationships between the physical world and its evolving DT; (ii) Inclusion of human beings into the objective field of DTs; (iii) Proactive real-time learning to meet highly dynamic requirements of IoE applications, and; (iv) Provide robustness guarantees to accurately reproduce the spatial, temporal, and geographical features of the underlying devices. In conclusion, the intellectual merit of this research program is to develop distributed, human-centric, and trustworthy DT-enabled SP/ML frameworks for information fusion and learning within IoE applications. Should the new knowledge and underlying DT technologies prove as valuable as we hope, it is expected that they will have a significant positive impact on the way we live in Canada creating a true human-centric world that balances economic advancement with resolution strategies addressing major societal challenges.