Network traffic analysis (NTA) is a technique used by network administrators to monitor network activity, ensure availability, and detect unusual patterns to identify potential anomalies. However, traditional traffic monitoring systems often struggle to detect these anomalies accurately because they rely on rigid models and a limited pool of data. Additionally, anomaly detection is particularly challenging, as anomalies exhibit patterns that differ from most network activities, making their identification based on prior knowledge difficult. This underscores the necessity for an automated and unsupervised approach capable of detecting various types of anomalies despite these limitations. In this paper, we propose an unsupervised framework that combines three statistical machine learning (ML) methods for mobile network failure detection, such as the lower control limit, the cumulative sum algorithm (CUSUM), and the robust stat detector model. Compared to previous studies, models often struggle with large datasets and generate high rates of false positives. However, our approach has proven to be better suited to the demands of traffic monitoring in large telecommunications infrastructures. It offers improved data handling and significantly reduces the rate of false positives while achieving an impressive 98% detection rate for anomalies on telecommunications sites.

Industry 4.0 is speeding up the move to connected, data-driven, and automated production, where the Internet of Things (IoT) enables sensing, communication, and real-time support for decisions. At the same time, rapid growth in industrial IoT studies has led to scattered technologies, architectures, and results. This paper fills this gap through a systematic literature review on IoT for Industry 4.0. It also helps readers compare methods and choose suitable building blocks for real deployments today. We focus on key technologies, integration architectures, application areas, challenges, trends, and reported benefits. Using PRISMA 2020, we searched five major databases (Scopus, MDPI, IEEE Xplore, ScienceDirect, and Web of Science) for 2020–2025 and found 584 records. After removing duplicates and screening, we kept 96 peer-reviewed studies for detailed analysis. Results show that most studies use a layered stack that combines sensing/actuation, industrial networking, data collection pipelines, and analytics across edge, fog, and cloud resources. MQTT, OPC UA, CoAP, LPWAN, and 5G connectivity are often used for communication, while RAMI 4.0, IIRA, and similar layered models guide system design. Many architectures follow an edge–cloud pattern, with growing focus on digital twin/CPS links and security-by-design. Applications are mainly smart manufacturing, predictive maintenance, and logistics, with added work in energy management, Construction 4.0, and agri-food monitoring. The key barriers remain interoperability, data quality and evaluation gaps, cybersecurity risks, legacy integration, and deployment limits. The review points to future work on edge AI/TinyML, deterministic connectivity, scalable digital twins, trusted data sharing, and sustainable industrial IoT.

On account of the evolution of technology, more complicated software arrives with the need to be verified to prevent the errors occurrence in a system which could generate fatal accidents and economic loss. These errors must be detected in an early stage during the development process to reduce redesign costs and faults. To ensure the correctness of software systems, formal verification provides an alternative approach to verify that an implementation of the expected system fulfills its specification. This paper focuses on the verification of reactive system behaviors specified by the Multi Decisional Reactive Agent (MDRA) and modeled using MDRA Profile. The objective in this paper is to use the Model Checking technique for MDRA Profile verification through the Model Checker SMV (Symbolic Model Verifier) to automatically verify the system properties expressed in temporal logic. The SMV mainly focusing on reactive systems provides a modular hierarchical descriptions and definition of reusable components. Besides, the expression of system properties is more described through both Computational Tree Logic (CTL) and Linear Temporal Logic (LTL).