Mario San Emeterio de la Parte

@upm.es

Group of Next Generation Networks and Services (GRyS), Departamento de Ingeniería Telemática y Electrónica (DTE), Universidad Politécnica de Madrid (UPM)
Universidad Politécnica de Madrid (UPM)

Mario San Emeterio de la Parte


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https://researchid.co/msaneme

RESEARCH, TEACHING, or OTHER INTERESTS

Engineering, Computer Science, Information Systems, Software
9

Scopus Publications

152

Scholar Citations

4

Scholar h-index

3

Scholar i10-index

Scopus Publications

  • Internet of Things: A comprehensive survey on syntactic and semantic interoperability for effective inter-entity communication
    Mario San Emeterio de la Parte, José-Fernán Martínez-Ortega, Yiting Wang, Sara Lana Serrano
    Array, 2026
    In the current landscape, the Internet of Things (IoT) represents a fragmented ecosystem, driven by the development of non-interoperable systems, intelligent solutions, and platforms. This fragmentation generates significant challenges for the integration of solutions and the advancement of IoT itself, due to the diversity of protocols, data models, ontologies, technologies, and heterogeneous devices. Interoperability is crucial to unlock the potential of IoT and data-driven solutions by enabling effective communication between the various entities that make up the ecosystem. This paper analyzes IoT standards to establish a structured, multi-layered framework for interoperability to enable a systematic classification of related research. Based on this framework, it presents a comprehensive analysis of the state-of-the-art in IoT interoperability, with a particular emphasis on the syntactic and semantic levels, which are crucial for effective data exchange and communication. This analysis evaluates the most representative proposals in the current literature and identifies the main approaches to achieve an interoperable IoT ecosystem. After their identification, the most suitable approaches for achieving semantic interoperability in the IoT are discussed, with the aim of solving the current fragmentation of the ecosystem, eliminating data silos, and enhancing the achievement of a next-generation IoT. Finally, a novel approach is proposed, based on the development of a system that takes advantage of fuzzy string matching algorithms, and the spatio-temporal semantic nature of the data generated in IoT. This proposal has the potential to solve the current problem, enable communication between entities and intelligent solutions, and lay the foundation for future research in this field.
  • A Secure and Efficient LLM-Based System for Natural Language Query-to-SQL Translation in IoT Data Management
    Mario San Emeterio De La Parte, Yiting Wang, José-Fernán Martínez-Ortega, Néstor Lucas Martínez
    IEEE Access, 2026
    Structured data embodies vast reservoirs of actionable knowledge, yet non-technical users often lack the tools to effectively access or leverage these resources. This paper presents a novel middleware system architecture that harnesses Large Language Models (LLMs) to translate natural language queries (NLQs) into executable SQL statements, enabling seamless interaction with relational databases without requiring technical expertise. To bridge a critical gap in existing research, the proposed system integrates a robust security framework featuring multi-factor authentication and fine-grained, role-based access control. A comprehensive evaluation was conducted using over 813,000 real spatio-temporal IoT samples from the H2020-AFarCloud international project, comparing both general-purpose LLMs and models fine-tuned for NLQ-to-SQL translation. Two complementary evaluation metrics—Execution Accuracy (EX) and Valid Efficiency Score (VES)—were employed to assess syntactic correctness, semantic fidelity, and runtime efficiency. Experimental results show that GPT-o3 mini-high achieves the highest overall performance in terms of average VES, while DeepSeek R1 exhibits the most consistent behavior across heterogeneous query scenarios, as reflected by lower performance variability. These findings underscore the interplay between model capability and computational efficiency, demonstrating the potential of LLM-driven middleware to democratize structured data access and enhance data-driven decision-making.
  • Edge-enabled IAM for IoTs with edge-based access management and context-driven syncservice
    Yiting Wang, José-Fernán Martínez-Ortega, Pedro Castillejo, Mario San Emeterio de la Parte
    Journal of Systems Architecture, 2025
    The number of edge IoT services is experiencing explosive growth. As an entry point for network services, Identity and Access Management (IAM) effectively prevents unauthorized access and blocks most cyber-attacks. However, most edge systems still rely on remote, cloud-based IAM for permission verification. The few edge-enabled IAM solutions that do exist operate on the assumption that attribute values are always up-to-date and provided by a completely trustworthy source, which make access decisions questionable in highly dynamic and distributed IoT environments. To address these challenges, this work proposes EIAM-IoT, an edge-enabled IAM architecture, and an improved Local Authentication and Authorization (LAA) method. The LAA evaluates multi-factor attributes, incorporating the freshness of attribute values and the trustworthiness of attribute providers, to achieve reliable access control. Additionally, the identity information required for LAA is synchronized and stored in the edge database by a context-aware synchronization strategy, which selectively and timely extends relevant identity data based on edge context, optimizing the trade-off between local data management costs and LAA performance. The performance and security analyses show that the LAA does not introduce significant overhead to traditional attribute-based solutions while enabling more fine-grained access control, increasing decision reliability, and offering additional features, such as local verification and federated identity management. While the LAA relies on cloud-extended local data, the system ensures greater availability and resilience to connectivity issues in edge-to-cloud setups. EIAM-IoT is particularly more suitable for dynamic, multi-authority, and edge-native IoT applications to achieve secure, low-latency, offline access to edge IoT services.
  • SISS: Semantic Interoperability Support System for the Internet of Things
    Mario San Emeterio de la Parte, José-Fernán Martínez-Ortega, Néstor Lucas Martínez, Vicente Hernández Díaz
    IEEE Internet of Things Journal, 2025
    The Internet of Things (IoT) landscape is hindered by a critical challenge: the lack of semantic interoperability among diverse data models. Existing IoT solutions often function as isolated data silos, impeding the seamless integration of heterogeneous data sources crucial for informed decision-making and streamlined processes. This research addresses this issue by introducing a pioneering solution: the Semantic Interoperability Support System (SISS). SISS is an innovative tool designed to bridge the semantic divide between disparate data models within a common application domain. To address the lack of interoperability between current IoT platforms, devices, and solutions that use native data models, SISS facilitates integration by enabling the generation of gateways or translator components. These components establish mappings between the semantic properties of source and target data models, leveraging advanced semantic analysis and inference techniques. The core principle underpinning SISS is its ability to discern and map the semantic content of data models. Through a meticulous analysis of the temporal and spatial dimensions inherent in the data, SISS establishes meaningful connections. This innovative approach fosters interoperability and enables a deeper understanding of the underlying information, enhancing the potential for data-driven insights. This paper delves into the pervasive issue of semantic interoperability in the current IoT paradigm and presents SISS as a transformative solution. By emphasizing its ability to transcend the limitations of existing solutions and its methodology to generate mappings between disparate data models, this research contributes to the achievement of global semantic interoperability in IoT.
  • Spatio-temporal semantic data management systems for IoT in agriculture 5.0: Challenges and future directions
    Mario San Emeterio de la Parte, José-Fernán Martínez-Ortega, Pedro Castillejo, Néstor Lucas-Martínez
    Internet of Things Netherlands, 2024
    The Agri-Food sector is in a stressful situation due to the high demand for food from the growing population around the world. The agricultural sector is facing a challenging situation; it must increase production and reduce its impact on the environment by appropriately allocating resources, adapting to climate change, and avoiding food waste. Agriculture 5.0, as the fifth agricultural evolution, aims to offer a perfect symbiosis between agriculture, advanced technologies, and sustainability. The most advanced technologies in automation, monitoring, and decision support are driven by the collection and processing of large volumes of agricultural data, such as weather information, farm machinery, soil and crop conditions, and marketing demand for higher profits. Taking advantage of the technological paradigm of the Internet of Things, agricultural data provides information on spatial, temporal, and semantic dimensions. Spatio-temporal semantic data management systems have become the cornerstone for the achievement of Agriculture 5.0 through advanced Internet of Things technologies. This paper aims to review the current literature on spatio-temporal semantic data management systems for Agriculture 5.0. This paper uses a systematic literature review technique to study eleven representative spatio-temporal semantic data management systems. A comprehensive evaluation of the aspects of interoperability, accessibility, scalability, real-time operation capability, etc. is carried out. Based on the evaluation results, future challenges are detected and development trends and possible improvements are proposed for future research. Finally, a distributed architecture capable of satisfying the above needs and challenges is proposed. The paper aims to inspire further research and development efforts to improve the efficiency, accessibility, and performance of spatio-temporal semantic data management systems.
  • Big Data and precision agriculture: a novel spatio-temporal semantic IoT data management framework for improved interoperability
    Mario San Emeterio de la Parte, José-Fernán Martínez-Ortega, Vicente Hernández Díaz, Néstor Lucas Martínez
    Journal of Big Data, 2023
    Precision agriculture in the realm of the Internet of Things is characterized by the collection of data from multiple sensors deployed on the farm. These data present a spatial, temporal, and semantic characterization, which further complicates the performance in the management and implementation of models and repositories. In turn, the lack of standards is reflected in insufficient interoperability between management solutions and other non-native services in the framework. In this paper, an innovative system for spatio-temporal semantic data management is proposed. It includes a data query system that allows farmers and users to solve queries daily, as well as feed decision-making, monitoring, and task automation solutions. In the proposal, a solution is provided to ensure service interoperability and is validated against two European smart farming platforms, namely AFarCloud and DEMETER. For the evaluation and validation of the proposed framework, a neural network is implemented, fed through STSDaMaS for training and validation, to provide accurate forecasts for the harvest and baling of forage legume crops for livestock feeding. As a result of the evaluation for the training and execution of neural networks, high performance on complex spatio-temporal semantic queries is exposed. The paper concludes with a distributed framework for managing complex spatio-temporal semantic data by offering service interoperability through data integration to external agricultural data models. Graphical Abstract
  • Spatio-Temporal Semantic Data Model for Precision Agriculture IoT Networks
    Mario San Emeterio de la Parte, Sara Lana Serrano, Marta Muriel Elduayen, José-Fernán Martínez-Ortega
    Agriculture Switzerland, 2023
    In crop and livestock management within the framework of precision agriculture, scenarios full of sensors and devices are deployed, involving the generation of a large volume of data. Some solutions require rapid data exchange for action or anomaly detection. However, the administration of this large amount of data, which in turn evolves over time, is highly complicated. Management systems add long-time delays to the spatio-temporal data injection and gathering. This paper proposes a novel spatio-temporal semantic data model for agriculture. To validate the model, data from real livestock and crop scenarios, retrieved from the AFarCloud smart farming platform, are modeled according to the proposal. Time-series Database (TSDB) engine InfluxDB is used to evaluate the model against data management. In addition, an architecture for the management of spatio-temporal semantic agricultural data in real-time is proposed. This architecture results in the DAM&DQ system responsible for data management as semantic middleware on the AFarCloud platform. The approach of this proposal is in line with the EU data-driven strategy.
  • Breaking Down IoT Silos: Semantic Interoperability Support System for the Internet of Things
    Mario San Emeterio De La Parte, José-Fernán Martínez-Ortega, Néstor Lucas Martínez, Vicente Hernández Díaz
    International Conference on Electrical Computer Communications and Mechatronics Engineering Iceccme 2023, 2023
    The Internet of Things (IoT) is an emerging technology nurtured by the production of large volumes of data generated by various distributed agents in production environments. Solutions developed in specific application domains use multiple native data models, making integration and interoperability between solutions a complex scenario to achieve. The purpose of this paper is to provide a Semantic Interoperability Support System (SIS) that offers a powerful tool for semantic analysis, mapping, and implementation of connectors or gateways to achieve semantic interoperability between IoT solutions. The paper presents a real evaluation scenario in which the proposal to ensure semantic interoperability between the intelligent platform solutions, AFarCloud and DEMETER, is validated.
  • Location-Based Data Auditing for Precision Farming IoT Networks
    Joaquim Bastos, Paul Marcel Shepherd, Pedro Castillejo, Mario San Emeterio, Vicente Hernandez Diaz, Jonathan Rodriguez
    IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks Camad, 2021
    Modern agriculture and farmers have been benefiting from the broad possibilities that wireless sensor networks and Internet of Things, in general, can bring to this primary sector and associated businesses. This new approach, also known as precision farming, exploits multiple high-end technologies, typically centered on environmental data sensing of diverse nature, e.g., temperature, humidity, moisture and COx, which can provide valuable indicators regarding the conditions where crops and livestock are produced. For that purpose, the respective sensors can be deployed in specific places in a farm, remaining static, or they can be placed on board of vehicles, such as tractors and unmanned aerial vehicles. Such sensors can also be integrated into wearable devices that are put on livestock individuals to track their positions, behavior and physiological aspects. Furthermore, this advanced technology-based approach to farming includes the respective data processing and analytics, in many cases employing machine learning and artificial intelligence techniques and algorithms, namely to provide the farmer with appropriate and reliable decision support systems. The work presented here focus specifically on a mobile application, which allows to collect inputs from users at the field and includes such human assessments in the automated loop of the sensing data flow in a precision farming system. The developed solution enables new functionalities on such systems, namely towards data auditing and labelling, that can be initiated directly from the field, which is where the sensors are and the respective sensing conditions can be assessed by the mobile app user.

RECENT SCHOLAR PUBLICATIONS

  • Multi-agent task and motion planning trends analysis: a survey
    X Tao, JF Martínez-Ortega, N Lucas-Martínez, M San-Emeterio
    Artificial Intelligence Review , 2026
    2026.0
  • A Secure and Efficient LLM-Based System for Natural Language Query-to-SQL Translation in IoT Data Management
    MSE De La Parte, Y Wang, JF Martínez-Ortega, NL Martínez
    IEEE Access 14, 28435-28456 , 2026
    2026.0
  • Edge-enabled IAM for IoTs with edge-based access management and context-driven syncservice
    Y Wang, JF Martínez-Ortega, P Castillejo, MSE de la Parte
    Journal of Systems Architecture 165, 103430 , 2025
    2025.0
    Citations: 2
  • SISS: Semantic Interoperability Support System for the Internet of Things
    MSE de la Parte, JF Martínez-Ortega, NL Martínez, VH Díaz
    IEEE Internet of Things Journal , 2025
    2025.0
    Citations: 3
  • Spatio-temporal semantic data management systems for IoT in agriculture 5.0: Challenges and future directions
    MSE de la Parte, JF Martínez-Ortega, P Castillejo, N Lucas-Martínez
    Internet of Things 25, 101030 , 2024
    2024.0
    Citations: 51
  • Contributions to data engineering in spatio-temporal semantic data management for iot
    MSE de la Parte
    Ph. D. dissertation, Universidad Politécnica de Madrid , 2024
    2024.0
    Citations: 2
  • Breaking down IoT Silos: Semantic interoperability support system for the Internet of Things
    MSE De La Parte, JF Martínez-Ortega, NL Martínez, VH Díaz
    2023 3rd International Conference on Electrical, Computer, Communications … , 2023
    2023.0
    Citations: 2
  • Big Data and precision agriculture: a novel spatio-temporal semantic IoT data management framework for improved interoperability
    M San Emeterio de la Parte, JF Martínez-Ortega, V Hernández Díaz, ...
    Journal of Big Data 10 (1), 52 , 2023
    2023.0
    Citations: 64
  • Spatio-temporal semantic data model for precision agriculture IoT networks
    M San Emeterio de la Parte, S Lana Serrano, M Muriel Elduayen, ...
    Agriculture 13 (2), 360 , 2023
    2023.0
    Citations: 20
  • Location-based data auditing for precision farming iot networks
    J Bastos, PM Shepherd, P Castillejo, M San Emeterio, VH Díaz, ...
    2021 IEEE 26th International Workshop on Computer Aided Modeling and Design … , 2021
    2021.0
    Citations: 8
  • grys-upm/Data-Access-Manager_Data-Query: Final Version of DAM&DQ Semantic Middleware
    M San Emeterio de la Parte, S Lana Serrano, V Hernández Díaz, ...
    Zenodo , 0
  • grys-upm/Spatio-Temporal-Semantic Data Model for Precision Agriculture
    M San Emeterio De La Parte, S Lana Serrano, V Hernández Díaz, ...
    Zenodo , 0

MOST CITED SCHOLAR PUBLICATIONS

  • Big Data and precision agriculture: a novel spatio-temporal semantic IoT data management framework for improved interoperability
    M San Emeterio de la Parte, JF Martínez-Ortega, V Hernández Díaz, ...
    Journal of Big Data 10 (1), 52 , 2023
    2023.0
    Citations: 64
  • Spatio-temporal semantic data management systems for IoT in agriculture 5.0: Challenges and future directions
    MSE de la Parte, JF Martínez-Ortega, P Castillejo, N Lucas-Martínez
    Internet of Things 25, 101030 , 2024
    2024.0
    Citations: 51
  • Spatio-temporal semantic data model for precision agriculture IoT networks
    M San Emeterio de la Parte, S Lana Serrano, M Muriel Elduayen, ...
    Agriculture 13 (2), 360 , 2023
    2023.0
    Citations: 20
  • Location-based data auditing for precision farming iot networks
    J Bastos, PM Shepherd, P Castillejo, M San Emeterio, VH Díaz, ...
    2021 IEEE 26th International Workshop on Computer Aided Modeling and Design … , 2021
    2021.0
    Citations: 8
  • SISS: Semantic Interoperability Support System for the Internet of Things
    MSE de la Parte, JF Martínez-Ortega, NL Martínez, VH Díaz
    IEEE Internet of Things Journal , 2025
    2025.0
    Citations: 3
  • Edge-enabled IAM for IoTs with edge-based access management and context-driven syncservice
    Y Wang, JF Martínez-Ortega, P Castillejo, MSE de la Parte
    Journal of Systems Architecture 165, 103430 , 2025
    2025.0
    Citations: 2
  • Contributions to data engineering in spatio-temporal semantic data management for iot
    MSE de la Parte
    Ph. D. dissertation, Universidad Politécnica de Madrid , 2024
    2024.0
    Citations: 2
  • Breaking down IoT Silos: Semantic interoperability support system for the Internet of Things
    MSE De La Parte, JF Martínez-Ortega, NL Martínez, VH Díaz
    2023 3rd International Conference on Electrical, Computer, Communications … , 2023
    2023.0
    Citations: 2
  • Multi-agent task and motion planning trends analysis: a survey
    X Tao, JF Martínez-Ortega, N Lucas-Martínez, M San-Emeterio
    Artificial Intelligence Review , 2026
    2026.0
  • A Secure and Efficient LLM-Based System for Natural Language Query-to-SQL Translation in IoT Data Management
    MSE De La Parte, Y Wang, JF Martínez-Ortega, NL Martínez
    IEEE Access 14, 28435-28456 , 2026
    2026.0
  • grys-upm/Data-Access-Manager_Data-Query: Final Version of DAM&DQ Semantic Middleware
    M San Emeterio de la Parte, S Lana Serrano, V Hernández Díaz, ...
    Zenodo , 0
  • grys-upm/Spatio-Temporal-Semantic Data Model for Precision Agriculture
    M San Emeterio De La Parte, S Lana Serrano, V Hernández Díaz, ...
    Zenodo , 0