Route Optimization for Logistics and Transportation in IoT Networks: A Machine Learning Approach with Cybersecurity Implications

Abstract

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 in such dynamic environments 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 capturing distance, vehicle speed, and 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 analytics, optimization algorithms, and cybersecurity strategies to support secure, adaptive, and efficient logistics operations. This framework can be generalized to various logistics applications including e-commerce, urban delivery, and smart transportation systems.