Search Results (3)

With rapid developments of wireless communication and Internet of Things (IoT) technologies, an increasing number of devices and sensors are interconnected, generating massive amounts of data in real time. Among the underlying protocols, Message Queuing Telemetry Transport (MQTT) has become a widely adopted lightweight publish–subscribe standard due to its simplicity, minimal overhead, and scalability. Then, understanding such protocols is essential for students and engineers engaging in IoT application system designs. However, teaching and learning MQTT remains challenging for them. Its asynchronous architecture, hierarchical topic structure, and constituting concepts such as retained messages, Quality of Service (QoS) levels, and wildcard subscriptions are often difficult for beginners. Moreover, traditional learning resources emphasize theory and provide limited hands-on guidance, leading to a steep learning curve. To address these challenges, we propose an AI-assisted, exercise-based learning platform for MQTT. This platform provides interactive exercises with intelligent feedback to bridge the gap between theory and practice. To lower the barrier for learners, all code examples for executing MQTT communication are implemented in Python for readability, and Docker is used to ensure portable deployments of the MQTT broker and AI assistant. For evaluations, we conducted a usability study using two groups. The first group, who has no prior experience, focused on fundamental concepts with AI-guided exercises. The second group, who has relevant background, engaged in advanced projects to apply and reinforce their knowledge. The results show that the proposed platform supports learners at different levels, reduces frustrations, and improves both engagement and efficiency. Full article

Automated spectrum analysis serves as a troubleshooting tool that helps to diagnose faults in wireless networks such as difficult signal propagation conditions and coexisting wireless networks. It provides a higher monitoring coverage while requiring less expertise compared with manual spectrum analysis. In this paper, we introduce a data set that can be used to train and evaluate deep learning models, capable of detecting frames from different wireless standards as well as interference between single frames. Since manually labeling a high variety of frames in different environments is too challenging, an artificial data generation pipeline was developed. The data set consists of 20,000 augmented signal segments, each containing a random number of different Wi-Fi and Bluetooth frames, their spectral image representations and labels that describe the position and type of frame within the spectrogram. The data set contains results of intermediate processing steps that enable the research or teaching community to create new data sets for specific requirements or to provide new interesting examination examples. Full article

Telecom operators and companies ask for graduates with a specific education on the standards of the Institute of Electrical and Electronic Engineers (IEEE) or the 3^rd Generation Partnership Project (3GPP), and the University curricula must consider these needs. The standards are written in a technical form, in a language understandable only by experts, and the technical details and algorithms are not often outlined. Therefore, a new educational methodology must be applied because the teachers have to bridge the gap between the basic knowledge (and the poor technical language) of students and the technical specifics of the standards. The paper presents a structured methodology to provide innovative teaching of the wireless standards for the Engineering Master’s degree, according to the Conceive, Design, Implement, and Operate (CDIO) initiative and project based learning. The methodology is organized in three learning phases to understand the standardization process and improve students’ skills to implement standard compliant communications systems. This challenge can be only won with laboratory activities to assist students in understanding wireless standards and with hands-on experiences during the internship period at telecom operators with the vision of a close cooperation between universities and telecom operators. Only in this way can the students achieve a solid background in designing and developing prototypes compliant with wireless communications standards and working skills for their future professional engineering careers. The effectiveness of the adopted educational methodology to provide innovative learning of wireless standards is evaluated by questionnaires filled in online by students and by the achieved skills implemented as confirmed by telecom operators. In this vision, the paper provides decision support to leaders in educational organizations to teach wireless standards effectively. Full article