Showing papers by "María del Mar Prados Gallardo published in 2022"
TL;DR: The aim is to learn from the live network to build a smart controller that can dynamically predict and apply a suitable configuration of the 5G NPN to satisfy the requirements of the current TSN traffic.
Abstract: The need to increase mobility and remove cables in industrial environments is pushing 5G as a valuable communication system to connect traditional deterministic Ethernet-based devices. One alternative is the adoption of Time Sensitive Networking (TSN) standards over 5G Non-Public Networks (5G NPN) deployed in the company premises. This scenario presents several challenges, the most relevant being the configuration of the 5G part to provide latency, reliability and throughput balance suitable to ensure that all the TSN traffic can be delivered on time. Our research work addresses this problem from the perspective of automata learning. Our aim is to learn from the live network to build a smart controller that can dynamically predict and apply a suitable configuration of the 5G NPN to satisfy the requirements of the current TSN traffic. The article presents the main ideas of this novel approach.
3 citations
30 Nov 2022
TL;DR: In this article , the authors present the tool STAn , which performs runtime verification on data traces that combine timestamped discrete events and sampled real-valued magnitudes, and analyzes traces against properties described in the so-called event-driven interval temporal logic.
Abstract: Abstract The increasing integration of systems into people’s daily routines, especially smartphones, requires ensuring correctness of their functionality and even some performance requirements. Sometimes, we can only observe the interaction of the system (e.g. the smartphone) with its environment at certain time points; that is, we only have access to the data traces produced due to this interaction. This paper presents the tool STAn , which performs runtime verification on data traces that combine timestamped discrete events and sampled real-valued magnitudes. STAn uses the Spin model checker as the underlying execution engine, and analyzes traces against properties described in the so-called event-driven interval temporal logic () by transforming each formula into a network of concurrent automata, written in Promela , that monitors the trace. We present two different transformations for online and offline monitoring, respectively. Then, Spin explores the state space of the automata network and the trace to return a verdict about the corresponding property. We use the proposal to analyze data traces obtained during mobile application testing in different network scenarios.