Development of Indoor Smart Environments Leveraging the Internet of Things and Artificial Intelligence: A Case Study

The Internet of Things paradigm envisions a world where every physical object is equipped with sensing/actuation capabilities and computing power and acquires its own digital identity. These objects are referred to as "smart” and have the goal of collecting and processing information about the environment surrounding them. One of the fields of interest in IoT applications concerns the intelligent management of activities in indoor environments, even if affected by unusual restrictions due to special conditions, such as those posed by the Covid-19 pandemic. This study focuses on the development of an IoT application based on the COGITO platform for the intelligent management of meeting rooms. By processing data collected from a set of IoT devices, cameras, and cognitive microphones, the developed prototype is able to autonomously monitor and make decisions about aspects that continuously affect environmental comfort, event management, and assessment of compliance with anti-contagious regulations throughout the time the room is occupied. After a brief review of the state of the art, the chapter describes the developed application. Furthermore, it highlights the features that make meeting room environments more comfortable for users and effective in managing events such as meetings and lectures. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Smart Learning Environments: Overview of Effective Tools, Methods, and Models

Digital learning environments have undergone a zigzagging evolution over the contemporary history of intelligent learning environments. In the pre-COVID-19 phase, e-learning struggled to establish itself in traditional training systems, but since the pandemic outbreak of March 2020, distance learning has become the only possible way to use the training actions. Today’s debate following this enormous experimentation has produced tools, methods, and models that need a further rethink for the post-COVID-19 phase. A possible evolution of full online education is a hybrid version of learning environments in which online and in-person, tangible and digital, alternate in time, space/place, media technology, learning design, and content coexist. These five categories guide the structuring of intelligent environments and adapt to the needs of students, teachers, and the social context in which they are inserted. Although the design follows recursive patterns, it is extremely flexible and adaptable. Furthermore, these digital environments make it possible to convey specific self-regulated learning methods and to develop specific motivational methods aimed at self-determination. The models of hybrid learning environments differ in the purposes to be pursued or the type of users to be reached. The surveys and experiences gained in the sector of innovative teaching methodologies find their most important field of application in hybrid environments. The purpose of this chapter is to summarize the future applications of the results that emerged from the experiments conducted. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Edge-Based Transfer Learning for Classroom Occupancy Detection in a Smart Campus Context.

Studies and systems that are aimed at the identification of the presence of people within an indoor environment and the monitoring of their activities and flows have been receiving more attention in recent years, specifically since the beginning of the COVID-19 pandemic. This paper proposes an approach for people counting that is based on the use of cameras and Raspberry Pi platforms, together with an edge-based transfer learning framework that is enriched with specific image processing strategies, with the aim of this approach being adopted in different indoor environments without the need for tailored training phases. The system was deployed on a university campus, which was chosen as the case study. The proposed system was able to work in classrooms with different characteristics. This paper reports a proposed architecture that could make the system scalable and privacy compliant and the evaluation tests that were conducted in different types of classrooms, which demonstrate the feasibility of this approach. Overall, the system was able to count the number of people in classrooms with a maximum mean absolute error of 1.23.