Qin Liu

Mr. Qin Liu


Research Focus & Metrics

Academic Career Length

1Year (2026-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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17 February 2026 in Journal of Information Security and Applications
Verified
Journal Article
Closed access
FLFeng Luo
Feng Luo
ZLZhihao Li
Zhihao Li
YTYingpeng Tong
Yingpeng Tong
ZLZeQi Liao
ZeQi Liao
ZZZhouping Zhang
Zhouping Zhang
QLQin Liu
Qin Liu

24 June 2026 in Electronics
Verified
Journal Article
Open access
PDF file
QLQin Liu
Qin Liu
HGHaotian Gan
Haotian Gan
FLFeng Luo
Feng Luo
YLYunpeng Li
Yunpeng Li
ZZZhouping Zhang
Zhouping Zhang
Time-Sensitive Networking (TSN) has emerged as a critical communication protocol for automotive Ethernet to support the high-bandwidth, real-time, and deterministic transmission requirements of next-generation in-vehicle networks. However, a clear and effective TSN mechanism combination tailored to the mixed and bursty traffic characteristics of automotive scenarios remains lacking. To address this issue, this paper proposes a combined TSN scheduling mechanism for automotive scenarios. The highest-priority traffic is scheduled by class-based Time-Aware Shaper (TAS), periodic bursty sensor traffic is shaped by Credit-Based Shaper (CBS), and medium-priority traffic adopts Multi-Cyclic Queueing and Forwarding (MCQF). Based on Compositional Performance Analysis (CPA), this paper derives the worst-case latency upper bound expressions for CQF streams and optimizes the schedulability analysis to reduce conservative errors. Simulation verifies that the theoretically calculated bounds cover the maximum simulation latency, and the optimized analysis reduces conservatism, with peak conservative error of 3.07% in the ring scenario and 10.59% in the automotive scenario. Compared with the strict priority and TAMCQF (a combination of TAS and Multi-CQF), the proposed mechanism combination suppresses the latency jitter of mixed traffic, mitigates long-duration blocking of medium-priority traffic caused by high-priority burst data, and provides reliable deterministic transmission guarantees for automotive in-vehicle networks.