Investigating an architectural design/framework for the extension of traditional distributed computing paradigms and how convergence/integration of IoT, DLT, Edge, and AI (IDEAL) technological capabilities can be used to address relevant challenges, considering technical and business ecosystems based on different industry use cases and thereby offering value addition in industry 4.0 and beyond. My research interests include investigating and designing comprehensive architectural frameworks, exploiting convergence and integrating diverse technologies that can address various academic and industry-oriented challenges, specifically the Distributed Ledger Technology (DLT/Blockchain) role in tamper-proof encryption, transparency, and auditable functionality, Dataspace Enablement at the Edge to develop cross-organizational data sharing, reusability of resources, and service/value chain enablement. Primarily using a mix of methodologies which includes Design Science Research (as an overall approach), Software Architectural Design patterns, Prototyping, and Experimentation, Business Process Modelling, Qualitative, Action Research, and Systematic Literature Reviewing Methods. In the future, I look forward to sharpening these skills, expanding and applying this knowledge horizon further with the blend of the techno-business methodological base in my research and industrial project engagements.
Presently, I am working on the "Convergence of Technologies and Distributed Edge Architectures for Industry 4.0 Dataspace Applications". The research will provide novel methods of building trust using Blockchain-enabled distributed security features, digital traceability of events, cross-domain sharing of data, and service chaining of resources, systems, and services via Dataspace enablement at the edge. My research mainly focuses on a) Investigating the theoretical foundations of traditional distributed architectural paradigms. b) System Modelling, Architectural Designing, and Empirical Validation. c) Extending these Cloud-Edge-Device paradigms with a pragmatic approach. d) Exploiting the convergence capabilities of IoT, DLT, Edge, and AI/ML (IDEAL) technologies over distributed architectures. e) Identifying how and where this technological convergence can be applied in real-world industrial use cases. f) Developing cross-domain data-driven value-chains (using dataspace) for the industry to promote sustainable win-win business models.
Digital Backend Course
Thesis Supervision in Digitalization, Dataspace, Cloud Computing, Edge Computing, Blockchain, and Business Modelling.
Software and System Architectural Engineering
Technology Specialization and Project Management
Open for Collaboration in Technological areas where AI, DLT, IoT, Digital Twin, Dataspace, Database, System Architectures, and Cloud-Edge-Devices Computing Continuum are involved.
I am currently collaborating with Eindhoven University of Technology, Netherlands, on the topic of AI and Machine Learning for Data Heterogeneity Harmonization in the Dataspace Ecosystem.
Open For Consultancy on Industry 4.0 oriented Digitalization Aspects.
