The vehicular ad-hoc network (VANET) provides a way for drivers or vehicles to communicate with each other in a more effective manner, however, there are still many issues with respect to different aspects of security, trust management, and delivering information reliably between two nodes. This study presents a way to facilitate Security in a VANET through a Trust-Aware, Block chain-Supported Information-Centric Networking (ICN) Framework. This Framework allows for more efficient dissemination of secure information in VANETs, making use of the requested/forwarding of Content In-Networks by Caching, which ideally would provide fast access to content and maintain availability and reduced latency for users within the network. A Trust Evaluation Module is employed to determine Node Behavior via the Packet-Forwarding Ratio of Nodes as well as the Validity of Packet-Content being forwarded. Blockchain Technology is used to hold Trust Records using PBFT Consensus, where records of malicious nodes identified. Lastly, Adaptive Trust Thresholds, Probabilistic Caching, and Malicious Content Filtering are utilized for Trust-Aware Forwarding and Caching mechanisms based upon calculated Trust Values with the intent to ensure the delivery of verified Data and to improve Network Performance. Simulation results show that the proposed method achieves a PDR of 98%, higher throughput, reduced delay, and lower packet loss compared to existing approaches, confirming its effectiveness in providing secure and reliable VANET communication under attack conditions.
Jinsha Lawrence, S. Pradeepa, R. T. Kumar et al.· International Conference on...· 0 citations
To maintain the safety and reliability of industrial applications, it is important to analyse faults in three-phase induction motors. Traditional methods have difficulty in identifying the complex characteristics of errors in three-phase induction motors because the signals associated with three-phase induction motors are often noisy and exhibit non-linear dynamics. In this paper, a comprehensive method to analyse errors in three-phase induction motors are proposed that utilises advanced signal preprocessing, feature extraction, and Deep Learning (DL) methods to address these issues. Initially, an Entropy-Driven Empirical Wavelet Filter (EDEWF) are applied to clean the raw motor signal, and a Min-Max scaling is used to normalise the signal to create useful features. Next, an integrated hybrid model combining a Radial Basis Function Neural Network (RBFNN) with a Multi-Head Adaptive Transformer (MHAT) are used for capturing local non-linear characteristics and global context. The features extracted from both models are combined into one feature vector and passed through a fully connected layer for classification. The Starfish Optimization Algorithm (SFOA) is implemented to adjust the hyperparameter of the proposed integrated hybrid model to improve the overall performance. The results obtained from the proposed method showed that it achieved a greatest accuracy, precision, recall, F1-Score of 0.9960 and robustness in the diagnostic of three-phase induction motors compared to traditional diagnostic methods, with respect to classifying the motor states as normal, healthy, and Broken Rotor Bar (BRB) faulted.
Killamsetti Vijeta, Perumal B Sri Suyambulinga, R. T. Kumar et al.· ITEGAM- Journal of Engineeri...· 0 citations