VT-MOOA: A Vehicle Trajectory-Aware Multi-Objective Optimization Algorithm for Task Offloading in SDN-Based Vehicular Edge Networks

This paper proposes a Vehicle Trajectory-aware Offloading Multi-Objective Optimization Algorithm (VT-MOOA), a multi-objective optimization algorithm that employs energy consumption, communication and computation delays, vehicle trajectory prediction, task division into sub-tasks, and SDN-based load balancing to optimize task offloading from vehicles to suitable edge servers in vehicular edge networks. The main aim of this work is to design an offloading framework that is robust to high vehicle mobility while ensuring energy efficiency, reduced delays, and balanced resource utilization. The proposed VT-MOOA enhances the S-Metric Selection Evolutionary Multi-Objective Algorithm (SMS-EMOA) by integrating hypervolume-based selection for faster convergence and improves solution quality by minimizing computation delay, minimizing transmission energy, and minimizing physical distance of the vehicle from the RSU while satisfying load balancing constraints, thereby efficiently managing resources in highly dynamic vehicular environments. Existing approaches are often slow, provide sub-optimal solutions due to single objective, false positive prediction or crowding distance reliance, and ignore critical parameters such as real-time vehicle mobility and trajectory prediction. The proposed <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">VT-MOOA</b> approach addresses these gaps by considering these important parameters along with energy efficiency, task deadlines, and load balancing, enabling more effective offloading decisions. Extensive simulations with real-world vehicular mobility datasets demonstrate that <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">VT-MOOA</b> achieves 14% lower energy consumption, 11% faster task completion time, and 13% reduction in computation delay, while also improving load distribution by about 17% compared to existing solutions, outperforming them.