K Pavan Kumar supervised by Dr. Deepak Gangadharan received his Master of Science – Dual Degree in Electronics and Communication Engineering (LED) by Research. Here’s a summary of his research work on Towards Temporal Determinism: Performance Analysis of ST flows with non-zero arrival jitter in TSN

Ethernet is widely used networking technology for connecting devices within a local area network (LAN) to enable data communication. It follows a best-effort delivery model, which provides no guarantees on latency or bandwidth. As a result, it is not suited for real-time applications such as audio and video streaming. To address this limitation, the Audio Video Bridging (AVB) protocol was introduced, which reserves a portion of network bandwidth for audio and video traffic by utilising credit-based shaping mechanism. However, AVB does not satisfy the reliability and low-latency requirements of safety-critical applications. Later, the Time-Sensitive Networking (TSN) communication protocol was introduced by providing enhancements over AVB. TSN is crucial for ensuring deterministic communication in real-time applications. In TSN, Network Traversal Time (NTT) is a critical metric that quantifies the timeliness of received data. Maintaining low NTT is essential for ensuring accurate system responses and to prevent outdated information from affecting real-time operations. However, the presence of non-zero arrival jitter negatively impacts NTT and poses challenges in providing predictable performance. When jitter increases, packets may arrive inconsistently, causing some to be delayed while others may arrive in bursts. To address this, this thesis analyzes the NTT bounds for time-critical flows, influenced by arrival jitter. We propose an analytical framework to estimate best- and worst-case ST slots for data transmission, considering interference from higher priority flows and establishing data reachability along the network path across the switches. To validate our analysis, we conducted experiments using both a synthetic task set and an automotive use case. We also analyzed the impact of flow parameters on NTT determination, which, in some scenarios, led to pessimistic bound estimations. 

July 2026