The school dropout problem has been recurrent in different educational areas, which has reinforced important challenges when pursuing education objectives. In this scenario, technical schools have also suffered from considerable dropout levels, even when considering a still increasing need for professionals in areas associated to computing and engineering. Actually, the dropout phenomenon may be not uniform and thus it has become urgent the identification of the profile of those students, putting in evidence techniques such as eXplainable Artificial Intelligence (XAI) that can ensure more ethical, transparent, and auditable use of educational data. Therefore, this article applies and evaluates XAI methods to predict students in school dropout situation, considering a database of students from the Federal Institute of Rio Grande do Norte (IFRN), a Brazilian technical school. For that, a checklist was created comprising explanatory evaluation metrics according to a broad literature review, resulting in the proposal of a new explainability index to evaluate XAI frameworks. Doing so, we expect to support the adoption of XAI models to better understand school-related data, supporting important research efforts in this area.

The Industrial Internet of Things (IIoT) is often presented as a concept that is significantly changing industry, yet continuous improvements in the identification and automation of objects are still required. Such improvements are related to communication speed, security, and reliability, critical attributes for industrial environments. In this context, the radio-frequency identification (RFID) systems present some issues related to frame collision when there are several tags transmitting data. The dynamic framed-slotted ALOHA (DFSA) is a widely used algorithm to solve collision problems in RFID systems. DFSA dynamically adjusts the frame length based on estimations of the number of labels that have competed for slots in the previous frame. Thus, the accuracy of the estimator is directly related to the label identification performance. In the literature, there are several estimators proposed to improve labels identification accuracy. However, they are not efficient when considering a large tag population, requiring a considerable amount of computational resources to perform the identification. In this context, this work proposes an estimator, which can efficiently identify a large number of labels without requiring additional computational resources. Through a set of simulations, the results demonstrate that the proposed estimator has a nearly ideal channel usage efficiency of 36.1%, which is the maximum efficiency of the DFSA protocol.

The Internet of Things (IoT) is transforming how devices interact and share data, especially in areas like vehicle monitoring. However, transmitting large volumes of real-time data can result in high latency and substantial energy consumption. In this context, Tiny Machine Learning (TinyML) emerges as a promising solution, enabling the execution of machine-learning models on resource-constrained embedded devices. This paper aims to develop two online multivariate compression approaches specifically designed for TinyML, utilizing the Typicality and Eccentricity Data Analytics (TEDA) framework. The proposed approaches are based on data eccentricity and do not require predefined mathematical models or assumptions about data distribution, thereby optimizing compression performance. The methodology involves applying the approaches to a case study using the OBD-II Freematics ONE+ dataset, which is focused on vehicle monitoring. Results indicate that both proposed approaches, whether parallel or sequential compression, show significant improvements in execution time and compression errors. These findings highlight the approach’s potential to enhance the performance of embedded IoT systems, thereby improving the efficiency and sustainability of vehicular applications.

In this paper we assess the interference caused by external (uncontrolled) traffic sources upon real-time flows in IEEE 802.11-based mesh networks. Through a set of simulations, we assess the impact of external traffic sources by analyzing the end-to-end delay, frame loss ratio and deadline miss ratio as performance metrics for a set of real-time flows. We infer the maximum interfering traffic load that can be supported so that it does not negatively impact upon the behavior of the real-time flows. We highlight that there is a critical threshold load for the network above which the network can no longer reliably support real-time flows.

This paper assesses the interference caused by uncontrolled traffic sources upon real-time traffic in IEEE 802.11s mesh networks. It was considered a mesh network transmitting real-time traffic and an external interference was injected in order to evaluate its impact over the real-time communication. A set of experiments were performed in ns-3 and different scenarios were evaluated, where the end-to-end delay, message loss and deadline miss ratios were chosen as performance metrics. It can be inferred that the real-time traffic in 802.11s is highly affected by external interferences, and therefore that must be taken into account when setting-up such type of networks.