Route Optimization for Logistics and Transportation in IoTNetworks: A Machine Learning Approach with Cybersecurity

The rapid expansion of Internet of Things (IoT) technologies has transformed modern logistics by enabling real-time monitoring of vehicles, traffic conditions, and delivery
operations. However, efficiently optimizing delivery
routes environments in such dynamic remains a challenge,
particularly when traffic variability,operational constraints, and cybersecurity threats are considered. This study proposes a hybrid framework that integrates machine learning–based travel time prediction with a Genetic Algorithm (GA) for route optimization in IoT-enabled
logistics networks. Synthetic IoT data traffic congestion are used to train a prediction model, which then informs the
optimization phase to generate efficient multi-vehicle delivery routes. The results demonstrate significant improvements in total travel time, fleet utilization, and
operational efficiency when compared to conventional
routing methods. Additionally, the study evaluates the impact of cybersecurity vulnerabilities such as GPS spoofing and sensor tampering, highlighting their potential to distort route decisions and proposing mitigation
mechanisms. The findings underscore the importance of combining predictive.