The next industrial revolution, generally referred to as Industry 4.0, is already here. This envisions a rapid transformation in the design, operation and service of manufacturing systems, where machines, sensors and actuators are interconnected in the factories in order to enable spontaneous collaboration, monitoring and control. Currently, Cyber-Physical-Systems (CPS) use regular Internet protocols (e.g. SOAP or HTTP) to communicate with each other. However, these protocols inherently present certain drawbacks or disadvantages: large footprint, CPU usage, high memory and energy consumption, etc. In recent years, new Industrial Internet of Things (IIoT) protocols have emerged to solve this issue, such as the Constrained Application Protocol (CoAP) and MQ Telemetry Transport (MQTT). These are lightweight protocols designed to face the challenges of industrial environments with a small footprint and overhead. These protocols have different features in terms of communication model and Quality of Service (QoS), that make them more suitable to an specific domain. Thus, it is important to identify the right metrics to adopt the most appropriate. This paper presents a theoretical and empirical comparison of MQTT and CoAP in terms of network overhead and latency, to assist system designers with this decision. Furthermore we describe the quantitative comparison conducted with their implementation in hardware platforms with high constraints. The achieved results provide useful metrics and guidelines to help designers to choose the appropriate schemes for their implementations.

The fast growth in the amount of connected devices with computing capabilities in the past years has enabled the emergence of a new computing layer at the Edge. Despite being resource-constrained if compared with cloud servers, they offer lower latencies than those achievable by Cloud computing. The combination of both Cloud and Edge computing paradigms can provide a suitable infrastructure for complex applications’ quality of service requirements that cannot easily be achieved with either of these paradigms alone. These requirements can be very different for each application, from achieving time sensitivity or assuring data privacy to storing and processing large amounts of data. Therefore, orchestrating these applications in the Cloud–Edge computing raises new challenges that need to be solved in order to fully take advantage of this layered infrastructure. This paper proposes an architecture that enables the dynamic orchestration of applications in the Cloud–Edge continuum. It focuses on the application’s quality of service by providing the scheduler with input that is commonly used by modern scheduling algorithms. The architecture uses a distributed scheduling approach that can be customized in a per-application basis, which ensures that it can scale properly even in setups with high number of nodes and complex scheduling algorithms. This architecture has been implemented on top of Kubernetes and evaluated in order to asses its viability to enable more complex scheduling algorithms that take into account the quality of service of applications.

El paradigma de la Industria 4.0 plantea el uso de herramientas y tecnologías que aumenten la eficiencia y la capacidad de adaptación de los procesos productivos para hacer frente a un mercado globalizado y con una alta competitividad. El concepto de Gemelo Digital es clave en el contexto de la Industria 4.0, y ha sido objeto de interés para la industria y la academia. Sin embargo, no hay consenso en la definición de este concepto, existiendo principalmente tres interpretaciones: la primera define un Gemelo Digital como un modelo de simulación de alta precisión; la segunda lo interpreta como una representación virtual del estado y el comportamiento de un activo de fabricación para su integración en la Industria 4.0; y la tercera, de reciente aparición, plantea la combinación de las anteriores. En este trabajo, se propone una metodología para la implementación de Gemelos Digitales que se ajusten a esta tercera interpretación. De esta forma, se proponen acciones concretas para la implementación de un Gemelo Digital que tenga capacidad de simular una estación y realizar su puesta en marcha virtual, además de facilitar su integración en la Industria 4.0.

The following topics are dealt with: production engineering computing; learning (artificial intelligence); mobile robots; optimisation; manufacturing systems; control engineering computing; position control; scheduling; decision making; robot vision.