ERTIS Research group

ERTIS focuses its research activity on improving the management, accessibility and integration of embedded devices in the context of the Internet of Things. ERTIS is part of the ITIS Software institute.

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Research Areas

ERTIS focuses on several research lines that address the challenges of embedded and distributed systems in real-time and critical domains. Our work is organized into the following areas

Digital Twins and Open Platforms

Architectures for modeling, simulation, and monitoring of physical processes, applied to domains such as agriculture, energy, and infrastructures.

Open Twins

Streaming Deep and Distributed Neural Networks over the IoT/Edge/Fog/Cloud

ERTIS investigates how to integrate deep neural networks with messaging systems for deployment across IoT, Edge, Fog, and Cloud environments, exemplified by its Kafka-ML framework, which manages the entire AI model lifecycle through data streams.

Kafka-ML

IoT, Edge, and Fog Computing

Middleware and distributed architectures that enable efficient data processing across heterogeneous devices and networks.

Real-Time and Critical Systems

Methods and tools to ensure predictability, functional safety, and cybersecurity in systems where failures are not acceptable.

Publications and Projects

Explore our latest research projects and publications in embedded systems and IoT.

Projects

European Projects

  • EVOLVE: Electric Vehicles Point Location Optimisation via Vehicular Communications
    2022-2026
    EVOLVE aims to exploit the scientific excellence and expertise of key academic and industrial players into a joint collaborative effort to design, develop and test various technologies that consider the holistic view of Electric Vehicles (EV) charging taking into account the view of technical and business stakeholders. EVOLVE will pursue innovation for advancing the technologies in EV charging ecosystem by orchestrating and managing the underlying networking and computational resources, design innovative algorithms using Artificial Intelligence (AI) and Machine Learning (ML), developing communication protocols, prediction and optimising load in smart grids, developing software systems and user interfaces (UIs). EVOLVE aims to transcend analytical models and simulation-based validation and aims to deliver five proof-of-concept (PoC) demonstrations. EVOLVE project will provide a platform to foster a close collaboration between academia and industry partners providing each with a unique experience to create new knowledge, share know-how and skills development.

    • National Projects

    • DiTaS: A framework for agnostic compositional and cognitive digital twin services
      (Dr. Manuel Díaz and Dr. Cristian Martín) - 2023-2026
      The requirements for continuous analysis and prediction of the real-time behavior of assets and their possible future state have led to the emergence of the well-known digital twins. Digital twins play a key role in the digital transformation for the industry 4.0 and beyond. However, the efforts made so far have pursued vertical and tailored solutions, but not interoperable and plug & play services. The DiTaS project shifts towards interoperable, composable, and cognitive digital twins as services. DiTaS will provide an open digital twin framework for the definition of interoperable, agnostic, and cognitive digital twin services in the Industry 4.0.
    • TRIATHLON: TRIhAlomeTHanes controL thrOugh AI-based techNologies
      (Dr. Manuel Díaz and Dr. Cristian Martín) - 2023-2026
      The main objective of TRIATHLON (TRIhAlomeTHanes controL thrOugh AI-based techNologies) is to facilitate the management of water supply networks. Specifically, it will work on the formation of subproducts of disinfection like trihalomethanes (THMs). For this purpose, the project will develop and validate a decision support system that allows the monitoring, prevention, and mitigation of these compounds with the help of artificial intelligence techniques and real-time monitoring.
    • DALISE: Design/development of a Data Lake and set of services for experimentation and innovation in energy flexibility environments
      (Dr. José Francisco Aldana Montes) - 2024-2027
      The DALISE project aims to propel the development of services tailored to offer effective strategies for enhancing the energy flexibility of buildings, with a particular focus on HVAC (Heat, Ventilating, and Air Conditioning) systems. Central to this endeavor is the implementation of a data lake for streamlined data management. Leveraging this infrastructure, the project seeks to apply a data-driven Artificial Intelligence (AI) methodology to cultivate precise models for predicting the energy consumption of HVAC devices using real data. Furthermore, the initiative will explore the creation of advanced services grounded in the innovative concept of data spaces.
    • OPTIMA-DONES: Cyber-physical system for proactive supervision and maintenance of critical systems of IFMIF-DONES
      (Dr. Manuel Díaz) - 2025-2027
      The OPTIMA-DONES project aims to develop a cyber-physical system for proactive monitoring and maintenance of IFMIF-DONES’s critical systems, following the Maintenance 5.0 paradigm. Its goal is to enhance safety, availability, and lifespan of key components in this major European fusion facility. Maintaining fusion systems like IFMIF-DONES is especially challenging due to the lack of prior experience with many prototype systems. Gaining this expertise is essential to make fusion energy viable and reliable.
    • Development of an intelligent early warning system to control abstractions and groundwater contamination in urban water supply during periods of drought.
      (DR Antonio Barberá) - 2025
      During droughts, both the quantity and quality of available water decline, often requiring reliance on underground aquifers. To prevent overexploitation and contamination, advanced technologies are needed. This project aims to develop an Early Warning System (iSAT) using low-cost sensors and artificial intelligence algorithms to continuously monitor water quality and availability. The system will send alerts to managers in case of health risks, enhancing groundwater management. The goal is to experimentally validate the prototype under real conditions and reach a TRL7 technology readiness level.
    • DIAGRO: Digital twins based on predictive systems for the management of irrigation water in irrigation communities
      (Dr Manuel Díaz) - 2025
      Faced with increasing water scarcity in Spain, particularly in Andalusia, the agricultural sector has shown strong adaptability through efficient water use. The DIGAGRO project proposes developing advanced digital twins powered by artificial intelligence to optimize irrigation management during drought conditions. Led by the University of Málaga and the University of Córdoba, this initiative will enable water consumption forecasting, anomaly detection, long-term demand analysis, and scenario simulation. Its goal is to support decision-making in irrigation communities through innovative and predictive tools.

      • Contracts with Companies

      • SIEITI: Industrial Pipeline Interior Condition Information System – Collaboration with INNTELIA Soluciones Tecnológicas
        2024
    • Cepsa. Research and innovation work related to the Hydrocracker (HCK) unit located at the Cepsa Energy Park in La Rábida.
      2024

      • Publications

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      Visits

      Some of the distinguished researchers and collaborators who have visited us recently. We value these exchanges as opportunities to foster innovation and collaboration in embedded systems and IoT.

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