Interdisciplinary cross-cultural and cross-organizational research offers great opportunities for innovative breakthroughs in the field of smart cities, yet it also presents organizational and knowledge development hurdles. Smart cities must be large towns able to sustain the needs of their citizens while promoting environmental sustainability. Smart cities foment the widespread use of novel information and communication technologies (ICTs); however, experimenting with these technologies in such a large geographical area is unfeasible. Consequently, smart campuses (SCs), which are universities where technological devices and applications create new experiences or services and facilitate operational efficiency, allow experimentation on a smaller scale, the concept of SCs as a testbed for a smart city is gaining momentum in the research community. Nevertheless, while universities acknowledge the academic role of a smart and sustainable approach to higher education, campus life and other student activities remain a mystery, which have never been universally solved. This paper proposes a SC concept to investigate the integration of building information modeling tools with Internet of Things- (IoT)-based wireless sensor networks in the fields of environmental monitoring and emotion detection to provide insights into the level of comfort. Additionally, it explores the ability of universities to contribute to local sustainability projects by sharing knowledge and experience across a multi-disciplinary team. Preliminary results highlight the significance of monitoring workspaces because productivity has been proven to be directly influenced by environment parameters. The comfort-monitoring infrastructure could also be reused to monitor physical parameters from educational premises to increase energy efficiency.

Internet of Things (IoT) has become a fundamental content of any engineering program due to the emerging need of experts in this field. However, the complexity of technologies that interact in IoT environments and the amount of different professional profiles required to design, implement and manage IoT environments, considering cybersecurity as a must, has led to a huge challenge in the educational world. This paper proposes an integral pedagogical strategy for learning IoT cybersecurity structured in three different stages, in a higher education institution. These stages focus not only on the content about IoT and cybersecurity but also on the competencies to acquire, the most suitable learning methodologies and the expected learning outcomes. The association of these concepts in each stage is detailed. Examples of courses are explained, the related competencies and learning outcomes are specified, and the contents and methodologies to achieve the expected results are described. An analysis of student results and stakeholder evaluations is provided to verify if the pedagogical strategy proposed is suitable. Furthermore, students’ feedback is included to corroborate the innovation, the suitability of the acquired skills, and the overall student satisfaction with the related courses and consequently with the proposed IoT cybersecurity pedagogical strategy.

A heterogeneous sensor network offers an extremely effective means of communicating with the international community, first responders, and humanitarian assistance agencies as long as affected populations have access to the Internet during disasters. When communication networks fail in an emergency situation, a challenge emerges when emergency services try to communicate with each other. In such situations, field data can be collected from nearby sensors deploying a wireless sensor network and a delay-tolerant network over the region to monitor. When data has to be sent to the operations center without any telecommunication infrastructure available, HF, satellite, and high-altitude platforms are the unique options, being HF with Near Vertical Incidence Skywave the most cost-effective and easy-to-install solution. Sensed data in disaster situations could serve a wide range of interests and needs (scientific, technical, and operational information for decision-makers). The proposed monitorization architecture addresses the communication with the public during emergencies using movable and deployable resource unit technologies for sensing, exchanging, and distributing information for humanitarian organizations. The challenge is to show how sensed data and information management contribute to a more effective and timely response to improve the quality of life of the affected populations. Our proposal was tested under real emergency conditions in Europe and Antarctica.

The COVID-19 pandemic has generated new needs due to the associated health risks and, more specifically, its rapid infection rate. Prevention measures to avoid contagions in indoor spaces, especially in office and public buildings (e.g., hospitals, public administration, educational centres, etc.), have led to the need for adequate ventilation to dilute the possible concentration of the virus. This article presents our contribution to this new challenge, namely the Ventilation Early Warning System (VEWS) which has aims to adapt the operation of the current Heating, Ventilating and Air Conditioning (HVAC) systems to the ventilation needs of diaphanous workspaces, based on a Smart Campus Digital Twin (SCDT) framework approach, while maintaining sustainability. Different technologies such as the Internet of Things (IoT), Building Information Modelling (BIM) and Artificial Intelligence (AI) algorithms are combined to collect and integrate monitoring data (historical records, real-time information, and location-related patterns) to carry out forecasting simulations in this digital twin. The generated outputs serve to assist facility managers in their building governance, considering the appropriate application of health measures to reduce the risk of coronavirus contagion in combination with sustainability criteria. The article also provides the results of the implementation of the VEWS in a university workspace as a case study. Its application has made it possible to detect and warn of inadequate ventilation situations for the daily flow of people in the different controlled zones.