V. Sanchez Padilla

V. Sanchez Padilla, Albert Espinal, Jose Cordova-Garcia et al.

Low-power Internet of Things (IoT) technologies are becoming increasingly important in engineering education as a tool to help students connect theory to real applications. However, many institutions face barriers that slow down their adoption in courses and labs. This paper reviews recent studies to understand these barriers and organizes them into three groups: technical, organizational, and curricular/pedagogical. Technical barriers include energy management, scalability, and integration issues. Organizational barriers are related to cost, planning, and the need for trained staff. Curricular and pedagogical barriers include gaps in student readiness, limited lab time, and platform choices that depend on budget. By detailing these barriers with practical examples, this paper aims to help educators and academic leaders develop more effective strategies to adopt low-power IoT in engineering programs.

Albert Espinal, V. Sanchez Padilla, Yesenia Cevallos

The performance of Dropbox, Google Drive, and OneDrive cloud storage services was evaluated under Wi-Fi and LTE network conditions during multimedia file uploads. Traffic was captured using Wireshark, and key metrics (including delay, jitter, bandwidth, and packet loss) were analyzed. Google Drive maintained the most consistent performance across both types of networks, showing low latency and reduced jitter. Dropbox showed efficient bandwidth utilization, but experienced a longer delay over LTE, attributed to a greater number of intermediate hops. OneDrive presented variable behavior, with elevated packet rates and increased sensitivity to fluctuations in the mobile network. A bimodal distribution of packet sizes was observed and modeled using a dual Poisson function. In general, Wi-Fi connections provided greater stability for multimedia transfers, while LTE performance varied depending on platform-specific implementations. The results contribute to a better understanding of traffic behavior in cloud-based storage applications and suggest further analysis with larger datasets and heterogeneous access networks.