A implantação da tecnologia da Internet das Coisas (IoT) traz benefícios à vida, como controle remoto de pragas na agricultura, monitoramento da cadeia de suprimentos, melhoria na educação e monitoramento de pacientes. No entanto, apesar dos benefícios, existem desafios embutidos na implementação desta tecnologia. Um dos maiores desafios da área é a violação de privacidade e segurança de dados. Portanto, é necessário avaliar a probabilidade de falha dos elementos e, consequentemente, a causa desse problema. Assim, é neste contexto que este trabalho se propõe a identificar, modelar e calcular a probabilidade de falha através de uma análise sistemática, utilizando Redes Bayesianas. Os resultados mostraram que através do uso do modelo proposto foi possível avaliar diferentes cenários para o uso de redes de Internet das Coisas, bem como simular o efeito da probabilidade de falha nos elementos críticos do sistema.

The Internet of Things (IoT) network in the health area offers many facilities or conveniences, as it allows communication between machines, such as monitoring the development of chronic diseases, disseminating disease control, monitoring the fall of the elderly. However, this communication can bring some associated risks, such as breach of privacy and security, loss of data integrity. Thus, this study identified the components of the network that interfere with the occurrence of risk and their respective hierarchy within the IoT system used in the health area. There were 8 (eight) factors identified in the literature and were validated by 2 (two) academic experts with knowledge on the subject. The use of the Analytic Hierarchy Process (AHP) method allowed to identify the most critical componentes related to the study proposed here.

Project risk events are often influenced by each other and rarely act independently. In this context, effective methods to identify, model and analyze these risks are necessary. The objective of this article is to apply the risk analysis in a software development project, based on the model of the Software Engineering Institute (SEI), using the Bayes model to calculate the event probabilities and also the Noisy-OR calculation structure to assign the initial weights of the network of factors that influence the project. In this way, it is expected to increase the chances of success of the risk analysis process. The results obtained by the techniques adopted prove to be promising in assisting the process of decision making by the managers of software development projects.

In recent years, applying quantitative risk analysis in Road cargo transport (RCT) has yielded successful results in assessing risks associated with industrial industries. Recent studies have highlighted the significance of different modes of transportation (such as roads, rails, pipelines, and inland waterways)for hazardous materials, emphasizing their role in determining risk levels. Among these modes, road transport via trucks stands out as a crucial component for economic development and is commonly employed for transporting various types of cargo. To effectively evaluate the risk level of a given activity within RCT, it is essential to determine the severity index for each potential situation that may arise during the transportation process. In this context, this research aims to investigate and calculate the risks associated with RCT by employing Bayesian Networks. Computational models were implemented using Bayesian Network software, and data input was carried out using Microsoft Excel®spreadsheets. The methodology employed for this study entailed field research with the involvement of experts and academic sources, along with the utilization of a systematic literature review. Additionally, the Delphi technique was applied, followed by a survey. The results pointed that the proposed model could effectively aid in identifying the level of risk involved in RCT operations across different scenarios. Furthermore, this model enables managers to evaluate the probability of one or more risk factors occurring during operations, facilitating the implementation of more efficient mitigation measures.