María del Mar Gallardo

The success of the Internet of Things (IoT) has driven the development, among others, of many different software architectures for producing, processing, and analyzing heterogeneous data. In many cases, IoT applications share common features, such as the use of a platform or middleware, also known as message broker, that collects and manages data traffic between endpoints. However, in general, data processing is very dependent on the case study (sensors that send temperature data, drones that send images, etc.). Thus, the applications responsible for receiving and processing data, which we call consumers, have to be built ad hoc, since some of their elements have to be specially configured to solve specific needs of the case study. This paper presents Flextory, a software factory tool to make it easier for IoT developers to automatically construct configurable consumer applications, which we call FLEX-consumers. Flextory guides developers through the process of generating Java consumers by selecting some desired features such as, for instance, the particular communication protocol to be used. This way, the developer only has to concentrate on designing the algorithm to process the data. In short, the use of Flextory will result in consumer applications with configurable behavior, namely FLEX-consumers, that can connect to a messaging server (for example RabbitMQ) and process the received messages.

The aim of IEEE Time-Sensitive Networking (TSN) standards is to grant deterministic communication in traditional Ethernet networks for Industry 4.0. Insofar as the use cases in the Factory need some mobility, the extension of the TSN capabilities over the fifth-generation (5G) cellular network is the next step. Some challenges in TSN over 5G, such as TSN translators time synchronization functionality, are well defined in the standards, even if they have not yet been addressed in the market. However other challenges, such as the dynamic configuration of the entire network (or part of the it) based on quality requirements of the current TSN traffic pattern, are defined at a very high level and delegated to vendors for implementation. This paper addresses this challenge, using an Automata Learning approach to monitor and reconfigure the end-to-end 5G QoS flow to keep the quality of a TSN session within the required values. Additionally, algorithms are provided to build the automata from network data and predict potential deviations of the requirements to meet the expected quality. Moreover, this work presents a functional TSN over a 5G testbed where the algorithms have been tested, demonstrating that the proposed solution achieves an improvement of around 40% compared to the usual operation of the network.

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 toolSTAn, which performs runtime verification on data traces that combine timestamped discrete events and sampled real-valued magnitudes.STAnuses theSpinmodel 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 inPromela, that monitors the trace. We present two different transformations for online and offline monitoring, respectively. Then,Spinexplores 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.