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At present, university campuses integrate technologies such as the internet of things, cloud computing, and big data, among others, which provide support to the campus to improve their resource management processes and learning models. Integrating these technologies into a centralized environment allows for the creation of a controlled environment and, subsequently, an intelligent environment. These environments are ideal for generating new management methods that can solve problems of global interest, such as resource consumption. The integration of new technologies also allows for the focusing of its efforts on improving the quality of life of its inhabitants. However, the comfort and benefits of technology must be developed in a sustainable environment where there is harmony between people and nature. For this, it is necessary to improve the energy consumption of the smart campus, which is possible by constantly monitoring and analyzing the data to detect any anomaly in the system. This work integrates a big data framework capable of analyzing the data, regardless of its format, providing effective and efficient responses to each process. The method developed is generic, which allows for its application to be adequate in addressing the needs of any smart campus.

The inclusion of Internet of Things (IoT) for building smart cities, smart health, smart grids, and other smart concepts has driven data-driven decision making by managers and automation in each domain. However, the hyper-connectivity generated by IoT networks coupled with limited default security in IoT devices increases security risks that can jeopardize the operations of cities, hospitals, and organizations. Strengthening the security aspects of IoT devices prior to their use in different systems can contribute to minimize the attack surface. This study aimed to model a sequence of seven steps to minimize the attack surface by executing hardening processes. Conducted a systematic literature review using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) techniques. In this way, we were able to define a proposed methodology to evaluate the security level of an IoT solution by means of a checklist that considers the security aspects in the three layers of the IoT architecture. A risk matrix adapted to IoT is established to evaluate the attack surface. Finally, a process of hardening and vulnerability analysis is proposed to reduce the attack surface and improve the security level of the IoT solution.

Currently, the internet of things (IoT) is a technology entering various areas of society, such as transportation, agriculture, homes, smart buildings, power grids, etc. The internet of things has a wide variety of devices connected to the network, which can saturate the central links to cloud computing servers. IoT applications that are sensitive to response time are affected by the distance that data is sent to be processed for actions and results. This work aims to create a prototype application focused on emergency vehicles through a fog computing infrastructure. This technology makes it possible to reduce response times and send only the necessary data to cloud computing. The emergency vehicle contains a wireless device that sends periodic alert messages, known as an in-vehicle beacon. Beacon messages can be used to enable green traffic lights toward the destination. The prototype contains fog computing nodes interconnected as close to the vehicle as using the low-power whole area network protocol called a long-range wide area network. In the same way, fog computing nodes run a graphical user interface (GUI) application to manage the nodes. In addition, a comparison is made between fog computing and cloud computing, considering the response time of these technologies.