To resolve the contradictions between the increasing demand of vehicular wireless applications and the shortage of spectrum resources, high mobility, short link lifetime, and spectrum efficiency, a novel cognitive radio (CR) and efficient management of spectrum in vehicular communication is required. Therefore, to exhibit the importance of spectral efficiency, a system model is proposed for cooperative centralized and distributed spectrum sensing in vehicular networks. The proposed architecture is used to minimize both the spectral scarcity and high mobility issues. Furthermore, we analyze the decision fusion techniques in cooperative spectrum sensing for vehicular networks. In addition, a system model is designed for decision fusion techniques using renewal theory, and then, we analyze the probability of detection of primary channel and the average waiting time for CR user or secondary user in PU transmitter. Finally, mathematical analysis is performed to check the probability of detection and false alarm. The results show that the cooperative cognitive model is more suitable for vehicular networks that minimize interference and hidden PU problem.

In an autonomous vehicle (AV), in order to efficiently exploit the acquired resources, big data analyses will be a reliable source for extracting valuable information from various sensors and actuators. The data extracted with the combined ability of telematics and real‐time investigation forms the vibrant asset for self‐driving cars. To demonstrate the significances of big data analysis, this study proposes a competent architecture for real‐time big data analysis for an AV, which indeed keeps pace with the latest trends and advancement concerning an emerging paradigm. There are a massive amount of sensors and independent systems needed to be realised for better competence in an AV, and the proposed model focuses on independent sensors that distinguish objects and handles visual information to decide the path. In order to attain the objective as mentioned above, a sensor fusion mechanism is proposed, which combines 3D camera sensor data and Lidar sensor information to provide an optimised solution for path selection. Furthermore, three algorithms, namely overlapping algorithm, sequential adding algorithm, the distance‐focused algorithm is designed for higher efficiency in sensor fusion mechanism. The proposed methodology is for the best exploitation of the enormous dataset, meant for real‐time processing for an AV.

The significance of user participation in sustaining drinking water quality and assessing other factors, such as cleanliness, sanitary conditions, preservation, and waste treatment, is essential for preserving groundwater quality. Inadequate water quality spreads disease, causes mortality, and hinders socioeconomic growth. In addition, disinfectants such as chlorine and fluoride are used to remove pathogens, or disease-causing compounds, from water. After a substantial amount of chlorine has been added to water, its residue causes an issue. Since the proposed methodology is intended to offer a steady supply of drinkable water, its chlorine concentration must be checked in real-time. The suggested model continually updates the sensor hub regarding chlorine concentration measurements. In addition, these data are transmitted over a communication system for data analysis to analyze chlorine levels within the drinking water and residual chlorine percentage over time using a fuzzy set specifically using a decision tree algorithm. Additionally, a performance investigation of the proposed framework is undertaken to determine the efficiency of the existing model for predicting the quantity of chlorine substance employing metrics such as recall, accuracy, F-score, and ROC. Henceforth, the proposed model has substantially better precision than the existing techniques.