Designing an iot-based fault detection system for real-time vehicle diagnostics using cloud computing

The fast advancement of technology has revolutionized the automobile industry, enabling real-time diagnostics and predictive maintenance for vehicles through the Internet of Things (IoT). Traditional methods used to diagnose a vehicle in most cases cannot offer real-time diagnostics nor predict potential failures, therefore causing increased downtime and safety issues. With the intention to provide IoT-based real-time diagnostics systems, a novel Dynamic Reptile Search Optimized Spatio-Temporal Convolutional Neural Network (DRS-SCNN) framework is introduced utilizing cloud computing techniques. Initially, data comprising vehicle sensor data is used, and then the raw input data is normalized to obtain consistency by using Min-Max normalization. Fourier Transform is deployed for extracting the features based on frequency-domain representation in a manner that results in removal of periodic patterns from which faults can be effectively detected. Then, the dynamic optimization allows DRS-SCNN to adaptively tune the architecture and hyperparameters during training, which leads to more efficient and effective learning. The experimental outcome of the proposed approach showed a precision of 97.3%, an accuracy of 98.4%, a recall of 97.5%, and an F1-score of 97.7% as compared to traditional approaches. Finally, the system employs cloud computing for real-time data storage and processing. The faults will be immediately detected, and maintenance alerts will be created. This solution has the potential to improve vehicle maintenance, reduce downtime, and enhance safety in vehicles. Moreover, the integration of cloud-based analytics ensures that large-scale vehicle data can be processed efficiently, allowing fleet management companies and individual users to monitor vehicle health remotely. The framework's adaptability further enables it to be deployed across different vehicle models and manufacturers, making it a versatile and scalable solution.