The rapid advancement of the Internet of Vehicles (IoV) has significantly enhanced vehicular communication, improving transportation safety and efficiency. However, increased connectivity introduces complex cybersecurity challenges due to diverse cyberattacks. This paper proposes a hybrid ensemble learning-based Intrusion Detection System (IDS) tailored for IoV networks, integrating multiple machine learning classifiers with advanced feature selection and data preprocessing methods to improve attack detection accuracy. The model effectively addresses dataset imbalance using the Synthetic Minority Over-sampling Technique (SMOTE) and is evaluated on the CICIDS2017 dataset, which encompasses a range of known attack types. Experimental results demonstrate that the proposed approach achieves superior classification performance with a detection accuracy of 99.74%, validating the effectiveness and robustness of the hybrid ensemble model for reliable intrusion detection in complex IoV environments, offering a scalable and practical solution for enhancing vehicular cybersecurity.
The wireless Internet of Things (WIoT) networks are becoming more distributed and less resourceful, employing lighter protocols, but they are also becoming more susceptible to cyber threats. In this study, we investigate the security assessment methodology of the WIoT networks based on vulnerability taxonomy. In the present study, the diverse sources, network layer effects, and possible outcomes of vulnerabilities are intended to be classified in a unique way. The risk analysis has been covered using vulnerability analysis, threat modeling-based risk assessment, and penetration testing. Practical application scenarios can be enacted by means of systematic security assessments to make the IoT -based infrastructure secure. The importance of the proposed study is that it helps in developing security frameworks in the WIoT environment, as it addresses the specific issues of other literature on the topic.
The digital economy centralizes data control within platform corporations, creating stark asymmetries between technology giants and individuals. Data cooperatives offer a democratic alternative, yet translating cooperative principles into operational governance remains challenging. This paper introduces a three-dimensional framework aligning the ICA's seven principles with Mechanisms, Enforcement, and Tracking dimensions across six governance categories: blockchain governance, AI monitoring, federated infrastructure, transparency, democratic participation, and automated compliance. Validated through healthcare, agricultural, and mobility cooperative cases, the framework identifies reusable governance patterns that bridge cooperative theory and digital practice, advancing practical pathways toward democratic and interoperable data governance.
The quality of olive oil is a key determinant of its value, yet conventional evaluation techniques are not suited for continuous, real-time monitoring. This paper introduces the design and validation of an Internet of Things (IoT)-based electronic nose (e-nose) system for ongoing assessment of olive oil quality. The proposed system features a custom-printed circuit board (PCB) equipped with an array of metal-oxide semiconductor (MOS) sensors, controlled by an ESP32 micro-controller. An end-to-end IoT architecture was implemented to manage data acquisition, storage, and visualization. The e-nose was validated through a multi-phase experiment in which olive oil samples were subjected to accelerated degradation. Results from the most stable experimental phase confirm the system's ability to monitor changes in the oil's volatile profile over time, highlighting its potential as a proactive and non-destructive quality control tool that addresses the limitations of traditional point-in-time analysis.
Background: In the context of Industry 4.0, the integration of IoT-based monitoring systems has become crucial for managing environmental conditions in industrial environments. However, the utility of collected data depends on its interpretation and the system's ability to handle faults, noise, and incomplete readings. Aims: We aim to demonstrate the application of trend analysis techniques to IoT sensor data, focusing on interpretability and robustness in a real-world industrial environment. Method: We developed an IoT architecture and deployed in a real factory setting to monitor variables such as temperature, humidity, noise, and illuminance using sensors integrated via MQTT. We treated each sensor's time series independently, identifying local trends and generating visual forecasts up to seven hours ahead. Results: Upon using trend analysis, it allowed identification of environmental patterns and atypical behaviors even in the presence of missing data or unstable transmission. The projected trends provided interpretable insights that may support decision-making in operational contexts, contributing to safety and environmental control in industry settings. Conclusions: The usage of trend analysis proved to be a viable strategy for enhancing the interpretation of IoT-based environmental monitoring through physical sensors.
