The Development of Sustainable Engineering with PjBL during the COVID-19 Pandemic.

Sustainable Engineering education must provide cyber-physical and distributed systems competencies, such as the Internet of Things (IoT), to contribute to the Sustainable Development Goals (SDG). The COVID-19 pandemic caused profound impacts arising from a traditional on-site teaching model rupture and demanded distance learning for engineering students. In this context, we considered the following Research Questions (RQ): How can Project Based Learning (PjBL) be applied in hardware and software courses from the Engineering curriculum to foster practical activities during the COVID-19 pandemic? Is the student performance in the fully remote offering comparable to the face-to-face offering? (RQ1); Which Sustainable Development Goals are related to the Engineering students' project themes? (RQ2). Regarding RQ1, we present how PjBL was applied in first-, third- and fifth-year Computer Engineering Courses to support 31 projects of 81 future engineers during the COVID-19 pandemic. Student grades in a Software Engineering course indicate no relevant differences between student performance in remote and face-to-face offerings. Regarding RQ2, most Computer Engineering students from the Polytechnic School of the University of São Paulo in 2020 and 2021 decided to create projects related to SDG 3-Good Health and Well-being, SDG 8-Decent Work and Economic Growth and SDG 11-Sustainable Cities and Communities. Most projects were related to health and well-being, which was an expected behavior according to how health issues were brought into highlight during the pandemic.

A TDD Framework for Automated Monitoring in Internet of Things with Machine Learning.

Robust, fault tolerant, and available systems are fundamental for the adoption of Internet of Things (IoT) in critical domains, such as finance, health, and safety. The IoT infrastructure is often used to collect a large amount of data to meet the business demands of Smart Cities, Industry 4.0, and Smart Home, but there is a opportunity to use these data to intrinsically monitor an IoT system in an autonomous way. A Test Driven Development (TDD) approach for automatic module assessment for ESP32 and ESP8266 IoT development devices based on unsupervised Machine Learning (ML) is proposed to monitor IoT device status. A framework consisting of business drivers, non-functional requirements, engineering view, dynamic system evaluation, and recommendations phases is proposed to be used with the TDD development tool. The proposal is evaluated in academic and smart home study cases with 25 devices, consisting of 15 different firmware versions collected in one week, with a total of over 550,000 IoT status readings. The K-Means algorithm was applied to free memory available, internal temperature, and Wi-Fi level metrics to automatically monitor the IoT devices under development to identify device constraints violation and provide insights for monitoring frequency configuration of different firmware versions. To the best of the authors' knowledge, it is the first TDD approach for IoT module automatic assessment which uses machine learning based on the real testbed data. The IoT status monitoring and the Python scripts for model training and inference with K-Means algorithm are available under a Creative Commons license.

Teaching Digital Electronics during the COVID-19 Pandemic via a Remote Lab.

Practical knowledge is essential for engineering education. With the COVID-19 pandemic, new challenges have arisen for remote practical learning (e.g., collaborations/experimentations with real equipment when face-to-face offerings are not possible). In this context, LabEAD is a remote lab project that aims to provide practical knowledge learning opportunities for Brazilian engineering students. This article describes how engineering project management methods consisting of application domains, requirement identification, technical solution specification, implementation, and delivery phases, were applied to the development of an Internet of Things (IoT) remote lab architecture. The distributed computing environment allows integration between students' smartphones and IoT devices deployed in campus labs and in student residences. The code is open-source for facilitated replication and reuse, and the remote lab was built in six months to enable six experiments for the digital electronics lab during the COVID-19 pandemic, covering all the experiments of the original face-to-face offering. More than 70% of the 32 students preferred remote labs over simulations, and only 2 were not approved in the digital electronics course offered remotely.Student perceptions collected by questionnaires showed that they could successfully specify, develop, and present their projects using the remote lab infrastructure in four weeks.