Internet of Vehicles (IoV) is one of the most active research disciplines in Intelligent Transportation Systems (ITS), intending to improve VANET (Vehicular-Ad-hoc Network) capabilities. The main objective of IoV is to enhance the safety of passengers by incorporating various advanced information and communication technologies thus, ease the driving experience of passengers and enhances traffic efficiency. IoV has numerous key technologies, and one of them is Radio-Frequency Identification Technology (RFID) which has a plethora of applications in IoV like automatic toll collection, intelligent parking, data dissemination, tracking the location of the vehicle, etc. which enhances the overall performance of IoV networks. Along with this, RFID devices are resource-constrained, thus security and privacy are a major concern and also IoV is a real-time sensitive network where security is of utmost importance. Keeping in mind the security perspective, the concept of Elliptic-Curve Cryptography (ECC) is taken into consideration. So, in this paper, we have proposed a Cryptographic solution-based secure ECC-enabled RFID mutual authentication protocol for IoV. The proposed protocol is comprised of three phases: Setup Phase, Tag Authentication Phase, and Server Authentication Phase. Security evaluation of the proposed protocol is performed by taking into consideration the analysis of security requirements as well as security attacks. Also, the simulation of the proposed protocol is done using the AVISPA tool and the results indicate that the proposed protocol is safe against various malevolent attacks. Performance evaluation of the proposed protocol is computed based on parameters i.e. storage requirements, communication cost, and computational cost. Results indicate that the proposed protocol contributes to high performance and security and has low computational cost than other existing authentication protocols. A novel Blockchain-based security framework for RFID-enabled IoV has also been proposed to further enhance the security of the IoV network.

Sleep is a vital physiological process for human health, and accurately detecting various sleep states is crucial for diagnosing sleep disorders. This study presents a novel algorithm for identifying sleep stages using EEG signals, which is more efficient and accurate than the state-of-the-art methods. The key innovation lies in employing a piecewise linear data reduction technique called the Halfwave method in the time domain. This method simplifies EEG signals into a piecewise linear form with reduced complexity while preserving sleep stage characteristics. Then, a features vector with six statistical features is built using parameters obtained from the reduced piecewise linear function. We used the MIT-BIH Polysomnographic Database to test our proposed method, which includes more than 80 h of long data from different biomedical signals with six main sleep classes. We used different classifiers and found that the K-Nearest Neighbor classifier performs better in our proposed method. According to experimental findings, the average sensitivity, specificity, and accuracy of the proposed algorithm on the Polysomnographic Database considering eight records is estimated as 94.82%, 96.65%, and 95.73%, respectively. Furthermore, the algorithm shows promise in its computational efficiency, making it suitable for real-time applications such as sleep monitoring devices. Its robust performance across various sleep classes suggests its potential for widespread clinical adoption, making significant advances in the knowledge, detection, and management of sleep problems.