Ornamental horticulture in Mexico is a growing industry that requires the inclusion of various technologies to automate production and marketing in order to increase its profitability. For this, data analysis is key, allowing obtaining knowledge to support decision-making; However, it involves exhaustive information processing time, affecting the productivity of companies due to the lack of a decision support system that implements dynamic business intelligence tools. This research work proposes a business intelligence web system for the creation of dynamic tools and execution of asynchronous queries to the database; which provides an analysis of the historical information on the commercialization of ornamental plants through tables, graphs and reports. It is developed using the PUA methodology, the Python programming language and the Django framework, employing an innovative approach by applying the DFS algorithm as a search mechanism to determine the relationship between the database tables, reducing extraction time, processing , analysis and presentation of information. As a result, it was possible to improve the use of historical information, streamline the processing and analysis of marketing information and, consequently, improve decision-making processes.

The Industrial Internet of Things (IIoT) is considered a key enabler for Industry 4.0. Modern wireless industrial protocols such as the IEEE 802.15.4e Time-Slotted Channel Hopping (TSCH) deliver high reliability to fulfill the requirements in IIoT by following strict schedules computed in a Scheduling Function (SF) to avoid collisions and to provide determinism. The standard does not define how such schedules are built. The SF plays an essential role in 6TiSCH networks since it dictates when and where the nodes are communicating according to the application requirements, thus directly influencing the reliability of the network. Moreover, typical industrial environments consist of heavy machinery and complementary wireless communication systems that can create interference. Hence, we propose a distributed SF, namely the Channel Ranking Scheduling Function (CRSF), for IIoT networks supporting IPv6 over the IEEE 802.15.4e TSCH mode. CRSF computes the number of cells required for each node using a buffer-based bandwidth allocation mechanism with a Kalman filtering technique to avoid sudden allocation/deallocation of cells. CRSF also ranks channel quality using Exponential Weighted Moving Averages (EWMAs) based on the Received Signal Strength Indicator (RSSI), Background Noise (BN) level measurements, and the Packet Delivery Rate (PDR) metrics to select the best available channel to communicate. We compare the performance of CRSF with Orchestra and the Minimal Scheduling Function (MSF), in scenarios resembling industrial environmental characteristics. Performance is evaluated in terms of PDR, end-to-end latency, Radio Duty Cycle (RDC), and the elapsed time of first packet arrival. Results show that CRSF achieves high PDR and low RDC across all scenarios with periodic and burst traffic patterns at the cost of increased end-to-end latency. Moreover, CRSF delivers the first packet earlier than Orchestra and MSF in all scenarios. We conclude that CRSF is a viable option for IIoT networks with a large number of nodes and interference. The main contributions of our paper are threefold: (i) a bandwidth allocation mechanism that uses Kalman filtering techniques to effectively calculate the number of cells required for a given time, (ii) a channel ranking mechanism that combines metrics such as the PDR, RSSI, and BN to select channels with the best performance, and (iii) a new Key Performance Indicator (KPI) that measures the elapsed time from network formation until the first packet reception at the root.

In this research, the process of reengineering was carried out to correct faults and apply improvements to the Follow-up System of Demands (SiSeDe) implanted in the Administrative Court of the State of Colima, Mexico. It is an information system with web features that facilitates the follow-up of sentencing projects, focusing on a module of productivity indicators to improve the time of attention to administrative trials. To control the software engineering process, the Agile Unified Process was used, due to its characteristics to handle small, changing and risky projects. The results reside in a software that presents the time of attention to the demand with statistical elements, considering metrics of evaluation of processes by time in addition to those established by the Administrative Law of the State of Colima, Mexico. The actors responsible for administering administrative justice, with the support of signals like traffic lights, can visualize in the system the productivity of the actors that intervene in the demand, identifying how many demands they have attended and how many are in prevention or in lag of attendance. These data allow the administrative personal to incorporate strategies to improve the attention of the justice processes.

El uso de las aplicaciones del Internet de las Cosas (IoT) está beneficiando a un número cada vez mayor de productores y organizaciones agrícolas en sus procesos de producción, mejorando su eficacia a largo plazo y contribuyendo al desarrollo sostenible, pues se estima que para 2023 estarán cerca de 12 millones de sensores agrícolas en uso globalmente. Por ello, los agricultores de diferentes sectores agrícolas, incluyendo el hortícola ornamental, están apostando por la pronta adopción del IoT para mejorar la eficiencia en su trabajo diario; aun cuando, en países subdesarrollados, los problemas para adquirir, acceder y utilizar las nuevas Tecnologías de Información y Comunicación (TIC) han dificultado esta tarea. En este artículo se describe el diseño arquitectural de una plataforma IoT, utilizada para la monitorización ambiental en viveros de plantas de ornato, permitiendo el almacenamiento y visualización dinámica de los datos sensados en la nube. La metodología en espiral para el desarrollo de prototipos fundamenta el diseño arquitectural presentado. Diseño que proporciona una solución innovadora al integrar un nodo portable que utiliza tecnología WiFi y una red de sensores estática basada en el estándar 6LoWPAN para el sensado, recolección y visualización de datos. Como resultados, este diseño contribuirá a establecer prácticas de agricultura inteligente para mejorar la calidad, productividad, rentabilidad y sustentabilidad en la producción de plantas ornamentales, así como a ahorrar tiempo y dinero en las empresas y productores de este sector al disminuir el esfuerzo laboral y riesgo de errores humanos en la recopilación de datos.