One of the main DevOps practices is the automation of resource provisioning and deployment of complex software. This automation is enabled by the explicit definition of Infrastructure-as-Code (IaC), i.e., a set of scripts, often written in different modelling languages, which defines the infrastructure and applications to be deployed. We introduce the DevOps Modelling Language (DOML), a new Cloud modelling language for infrastructure deployments. DOML is a modelling approach that can be mapped into multiple IaC languages, addressing infrastructure provisioning, application deployment and configuration at once. The idea behind DOML is to use a single modelling paradigm which can help to reduce the need of deep technical expertise in using different specialised IaC languages. We present the DOML’s principles and discuss the related work on IaC languages. We demonstrate the DOML advantages for the end-user in comparison with state-of-the-art IaC languages such as Ansible, Terraform, and Cloudify, and show its effectiveness through an example.

The infrastructure-as-code (IaC) is an approach for automating the deployment, maintenance, and monitoring of environments for online services and applications that developers usually do manually. The benefit is not only reducing the time and effort but also the operational costs. This paper aims at describing our experience in applying IaC in cloud-native applications, mainly discussing the key challenges towards modeling and generating IaC faced in the ongoing project Programming Trustworthy Infrastructure-As-Code in a Secure Framework (PIACERE). The concluding insights could spur the wider adoption of IaC by software developers.

The goal of the study is to compare the most popular protocols available in IoT (Internet of Things) nodes and how they perform across multiple samples in controlled conditions. The focus is on local telemetry transmission, in which the ESP32-C6 and ESP32-S3 boards communicate with two other devices—a router and a laptop—over the 2.4 GHz band. For the experiment, three standard nominal transmission sizes of 16, 64, and 256 bytes were pre-set. Each size was trialed for 30 samples for each protocol. All transmissions achieved a 100% success rate and were received by the end device. Both latency and success rate were used as the main performance indicators. The end results were that CoAP had the lowest mean latency of 47.0 ms, followed by MQTT with 63.0 ms and HTTP with a mean overall latency of 1419.0 ms. A comparative test with the ESP32-S3 revealed that while the overall protocol ranking remained identical, the gap eventually narrowed. The more powerful ESP32-S3 processed HTTP significantly faster (mean latency of 449.0 ms) but yielded slower latencies for the lightweight CoAP and MQTT protocols compared to the C6. The results indicate an order of magnitude faster performance of CoAP and MQTT than HTTP in the test setup. The study offers a simple and reproducible setup and benchmarking, allowing it to serve as a practical reference point when selecting the right protocol for a given use case.

Smart campus development is increasingly associated with the combined use of IoT technologies, artificial intelligence, cloud infrastructures, and large-scale data analytics in higher education. Despite this progress, many existing data models are not well-suited to the educational domain, particularly when interoperability and real-time analytical capabilities are required. To address this limitation, the study proposes a Smart Campus Education Data Model (SCEDM), which can be integrated into any FIWARE-based platform. The model is organized as a layered architecture that includes data acquisition, processing, and storage; analytics and decision support; application presentation; and security. The proposed model is not presented only at a conceptual level; it is also validated in a containerized FIWARE environment built around the Orion-ld Context Broker and NGSI-ld specifications. The SCEDM model is validated in a system that supports real-time state management across multiple campus domains. The model’s practical operation is validated across several experimental scenarios, including a simulation of a lecture process, classroom occupancy monitoring, and automated notifications to external platforms. In addition, the study compares five international case studies from different contexts. The comparison shows that, despite differences across local settings, similar benefits can be observed in campus operations and learning conditions. The study also recognizes several continuing challenges in the development of smart campuses, including interoperability, long-term scalability, data governance, privacy protection, stakeholder engagement, and financial sustainability. In response to these issues, the authors propose practical design guidelines alongside strategic recommendations for adoption at the institutional level.

While smart campuses continue to evolve alongside technological advancements, existing data models often fail to comprehensively integrate the diverse array of Internet of Things (IoT) devices and platforms. This study presents a unified data model tailored to the operational requirements of campus decision-makers, facilitating seamless interconnection across heterogeneous systems. By integrating IoT, cloud computing, big data analytics, and artificial intelligence, the proposed model seeks to advance campus operations, sustainability, and educational outcomes by fostering cross-system harmonization and interoperability. The analysis demonstrates that a layered architecture—comprising data acquisition, processing and storage, analytics and decision support, application presentation, and security and privacy—constitutes the foundation of robust smart campus data models. The system is structured to collect, refine, process, and archive raw data for future reference. Analytics and decision support mechanisms generate actionable insights; application presentation delivers results to end users, and security and privacy measures safeguard in-formation. The study further contends that artificial intelligence techniques, including predictive analytics (which forecasts outcomes using historical data), personalized learning (which customizes content to individual needs), and edge intelligence (which processes data at its source), are essential for advancing these models. These enhancements yield measurable benefits, including a 15% increase in student retention through personalized learning and a 20% reduction in energy consumption through predictive energy management [1]. Emerging technologies such as 5G networks, edge and fog computing, blockchain, and three-dimensional geographic information systems (3D GIS) are instrumental in enhancing campus intelligence. For example, the adoption of 5G has led to a 30% increase in data transmission speeds, thereby enabling real-time analytics and reliable connectivity (5G and IoT: How 5G is Transforming the Internet of Things, 2024). Building upon these technological advancements, innovative data models are shown to facilitate predictive energy management, resource optimization, and performance analytics within smart campuses. Nevertheless, ongoing challenges persist, including those related to system interoperability, scalability, and data governance. This study provides actionable design guidelines and offers a balanced evaluation of the achievements and challenges of smart campus implementations.