Increased attention is being given to Internet of things (IoT) network security due to attempts to exploit vulnerabilities. Security techniques protecting availability, confidentiality, and information integrity have intensified as IoT devices are viewed as gateways to larger networks by malicious actors. As an additional factor, the microservices‐based platforms have overtaken the deployment of applications that support smart cities; however, the distributed nature of these architectures heightens susceptibility to malicious network infrastructure use. These risks can result in disruptions to system functioning or data compromise. Proposed strategies to mitigate these risks include developing intrusion detection systems and utilizing machine learning to differentiate between normal and anomalous network traffic, indicating potential attacks. This article outlines the development and implementation of an intrusion detection system (IDS) using machine learning to detect online anomalies in network traffic. Comprising a traffic extractor and anomaly detector, the system employs supervised learning with various datasets to train models. The results demonstrate the effectiveness of the decision tree model in detecting traditional denial of service (DoS) attacks, achieving high scores across multiple metrics: an F1‐score of 98.08%, precision of 99.25%, recall of 96.96%, and accuracy of 99.62%. The random forest model excels in identifying slow‐rate DoS attacks, attaining an F1‐score of 99.85%, precision of 99.91%, recall of 99.80%, and accuracy of 99.88%.

The transition of cities towards a smarter approach significantly benefits from citizen participation in the development and implementation of innovative information and communication technology (ICT) products and services. Despite the emergence of various initiatives in recent years aimed at guiding the development of smart cities, there is still a lack of effective strategies to actively engage citizens, businesses, and educational institutions during the creation of these products and services. This study describes a set of practices that includes four co-creation techniques to facilitate the effort of software system development in collaboration with citizens and other stakeholders. The SEMAT standard is used to create and represent a method in which these practices are distributed across four stages: focus, definition, development, and validation. In each stage, a practice is proposed that incorporates a co-creation technique and complementary activities from various software engineering disciplines to promote active citizen participation; stimulate idea generation; and facilitate the creation of necessary documents and components for the development of the desired software system, including design systems, code files, conceptual representations, and technical diagrams, among others. Finally, the applicability and completeness of the method are validated through expert consultation in the fields of software engineering and smart cities. Recognized procedures are followed to obtain qualitative and quantitative results, such as improvement actions (addition or removal of elements), levels of consensus or acceptance, and opportunities for future work.

Field-based phenotyping of water-related traits for precision irrigation in tropical agroecosystems poses a persistent methodological challenge, driven by high climatic variability and the complex water-use physiology of Crassulacean Acid Metabolism (CAM) crops such as pineapple (Ananas comosus var. MD2). We developed and validated a Physics-Informed Machine Learning (PIML) framework that integrates high-resolution UAV multispectral imagery, IoT-based microclimatic records, and a mechanistic soil water balance based on the FAO-56 Penman–Monteith standard to predict plot-scale soil moisture depletion as a proxy of plant water status. A six-month field campaign (March–August 2022) across 25 georeferenced commercial pineapple plots in the Colombian Orinoquia piedmont yielded a spatiotemporally balanced dataset of $N=150$ observations. Soil-adjusted vegetation indices (OSAVI, MSAVI) outperformed standard NDVI for capturing water-related canopy traits, effectively decoupling spectral responses from substrate noise. A Gradient Boosting regressor achieved $R^2=0.842$ and $RMSE=0.0705$ on a normalized target scale, corresponding to a $7.05\%$ error over the prediction range, while the traffic-light Decision Support System (DSS) for irrigation scheduling reached $91.1\%$ accuracy (Cohen’s Kappa $=0.91$). Incorporating daily soil moisture depletion as a mechanistic feature improved predictive accuracy over a spectral-only baseline ($\mathrm{\Delta }{R}^2=+0.052$) and anchored predictions within a physically consistent framework based on the FAO-56 water balance, with no false negatives observed for water deficit detection in the hold-out validation set. This framework advances high-throughput, population-scale phenotyping of water-related traits in open-canopy CAM crops, establishing a transferable methodology for operational precision irrigation under tropical savanna conditions.