MOR Framework: IoT-Driven Dynamic Decision-Making for Fleet Optimization and Maintenance Prioritization

Introduction: Vehicular Internet of Things–Wireless Sensor Networks (IoT-WSNs) employ interconnected sensors such as GPS, accelerometers, cameras, and temperature monitors to enhance safety, performance, and efficiency. These networks collect data on speed, engine health, tire pressure, and road conditions, enabling real-time analytics for predictive maintenance and routing. However, most existing models are vulnerable to noise and outliers, limiting decision accuracy. Methods: This study introduces the Middle-Order Ranking (MOR) framework, which integrates IoT sensor data to optimize vehicle routing and insurance settlement. Using data from 50 simulated vehicles, the model calculates Feature Ranking Scores (FRS) through the Median of Sensor readings (MOS) and Mean Absolute Deviation (MAD). The Middle-Order Routing Score (MORS) is then derived to allocate vehicles to routes across five complexity levels. Simulations were implemented in Python 3.x with NumPy, Pandas, and Matplotlib libraries. Results: Vehicles with MORS ≥0.70 were consistently assigned to high-complexity routes such as highways or emergency response, while vehicles with MORS ≤0.50 were restricted to low-priority routes or excluded. Brake usage and engine temperature emerged as the most reliable features, achieving the highest FRS values. Discussion: The MOR framework enhances robustness by minimizing the influence of sensor anomalies and noise. Compared to conventional max/min methods, MOS–MAD pairing ensures stable, interpretable, and data-driven vehicle prioritization suitable for insurance and fleet operations. conclusion: Conclusion: Through the optimal prioritization of features for enhanced vehicle selection and risk management efficiency, the MOR model was able to make accurate routing decisions over 10 simulation rounds. MOR exhibits the potential to develop into a productive data-based systems for automated insurance management and future vehicle systems. Conclusion: MOR demonstrates strong potential for intelligent fleet management, predictive maintenance, and automated insurance systems. Future work will extend this model to real-world datasets for greater applicability.