Rubi Debnath

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About

Rubi Debnath’s research bridges the critical gap between next-generation wireless networks and deterministic industrial communication. Her most impactful work centers on the integration of 5G with Time-Sensitive Networking (TSN), a domain essential for achieving ultra-reliable and low-latency communication (URLLC) in applications like mobile robotics and Industrial IoT. Her landmark paper, “5GTQ: QoS-Aware 5G-TSN Simulation Framework” (2023), has already garnered 28 citations, reflecting its timely importance in enabling collaborative, low-latency automation. This framework provides a vital tool for researchers and engineers designing converged networks for Industry 4.0. Earlier in her career, Debnath also contributed to computer vision with a novel approach to optical character recognition (2014), demonstrating a foundational interest in pattern recognition and automation. Her work is particularly notable for addressing the real-world challenge of connecting wireless 5G systems with the strict timing requirements of TSN, a key enabler for future smart factories. With her focus on QoS-aware simulation and network convergence, Debnath is shaping the infrastructure that will power the next generation of connected, autonomous systems.