ATWin: An Improved and Detailed Startup Model of TTP/C
Source abstract
Abstract
TTP/C (Time-Triggered Protocol Class C) is a mainstream communication protocol commonly utilized in cyber–physical systems within the aerospace and automotive industry. Unfortunately, when it comes to the startup model, there are three issues in the standard of TTP/C (namely AS6003). Firstly, AS6003 only mentions a high-level specification, which leads to a gap between the standard and its implementation. Secondly, the standard startup model in AS6003 aggressively handles the multi-clique problem by dropping the first valid frame unconditionally without a contention-detecting mechanism, resulting in additional time consumption in some types of contention scenarios. At last, there is lack of the formal verification for the validity of the standard startup model with an arbitrary number of nodes and the formal derivation of its upper bound of startup time. To address these limitations, we propose a detailed and improved startup model named ATWin based on AS6003. It not only bridges the gap between the top-level standard and its implementation by supplementing the undefined details, but it also enhances the efficiency of the startup time by adding a contention-detecting strategy to the standard startup model. The ATWin model is developed as an open-source implementation for TTP/C’s startup. We also formally demonstrate the validity of ATWin and deduce its upper bound of startup time with an arbitrary number of nodes in this paper.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Formal Methods in Verification · Real-Time Systems Scheduling · Distributed systems and fault tolerance
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Computer Science and Engineering, Beihang University, Beijing 100191, China
State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing 100191, China
Funding
National Natural Science Foundation of China · National Key R&D Program of China · Guizhou Province Science and Technology Project: Research and Demonstration of Sci. & Tech Big Data Mining Technology Based on Knowledge Graph · Teaching Reform Project of Beihang University
References (27)
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