Offloading Elastic Transfers to Opportunistic Vehicular Networks Based on Imperfect Trajectory Prediction

Due to the high cost of cellular networks, vehicle users would like to offload elastic traffic through vehicular networks as much as possible. This demand prompts researchers to consider how to make the vehicular network system achieve better performance for requests coming online, such as maximizing throughput. The traffic in vehicular networks is transferred through opportunistic contacts between vehicles and infrastructures. When making scheduling decisions, the scheduler must be aware of vehicles’ future trajectories. Vehicles’ future trajectories are usually predicted by trajectory prediction algorithms when users are not willing to report their future trips. Unfortunately, no trajectory prediction algorithm can be completely accurate, and these inaccurate prediction results will reduce the throughput. In this paper, we focus on overcoming this challenge. Specifically, we first measure the LSTM (Long Short Term Memory) prediction method that has been widely used recently and understand the accuracy of predicted contacts. Then, based on the enlightenment from the measurement, we design a system, i.e., i-Offload, to offload elastic traffic under an imperfect trajectory prediction algorithm. The experimental results show that our method still has good throughput and scheduling efficiency under an imperfect prediction algorithm. Compared with an existing scheduling method, our method outperforms it by 42% and 45% in terms of scheduling throughput and scheduling efficiency, respectively.