Opportunistic communications as an efficient traffic offloading method can be used to offload uplink traffic of cellular networks to Wi-Fi networks. However, because of its contact pattern (contact frequency and contact duration) the offloading method could not ensure the data to be successfully offloaded to Wi-Fi Access Points (APs) within a time constraint. In this paper, we focus on maximizing the probability of offloading data to Wi-Fi APs by fragmenting the data and assigning the fragments to different direct or indirect paths generated by opportunistic contacts. Firstly, we propose two methods based on mobility prediction, which is realized by machine learning, to separately calculate the probability of offloading data to Wi-Fi APs by the direct offloading path considering multiple opportunistic contacts and contact duration, and the probability of indirectly offloading data to Wi-Fi APs by the indirect offloading path. Then, based on the probability calculation methods the offloading probability maximization is formulated as a non-linear integer programming problem, and we propose a distributed heuristic algorithm to solve it considering complexity of the probability calculation and limited computation capacities of devices. Simulation results prove the data offloading probability of our proposed algorithm outperforms other algorithms under different simulation environment.

机会通信作为一种有效的流量卸载方法，可以用于将蜂窝网络的上行链路流量卸载到Wi-Fi网络。但是，由于其接触方式（接触频率和接触持续时间），这种卸载方法无法确保在时间限制内将数据成功卸载到Wi-Fi接入点（AP）。在本文中，我们专注于通过对数据进行分段并将分段分配给机会性联系所生成的不同直接或间接路径来最大程度地将数据卸载到Wi-Fi AP的可能性。首先，我们提出了两种基于移动性预测的方法，该方法是通过机器学习实现的，分别考虑了多个机会性接触和接触持续时间，通过直接卸载路径分别计算了将数据卸载到Wi-Fi AP的概率，以及通过间接卸载路径将数据间接卸载到Wi-Fi AP的可能性。然后，基于概率计算方法，将卸载概率最大化公式化为非线性整数规划问题，并考虑到概率计算的复杂性和设备的有限计算能力，提出了一种分布式启发式算法来解决该问题。仿真结果表明，在不同的仿真环境下，本文算法的数据卸载概率优于其他算法。考虑到概率计算的复杂性和设备的有限计算能力，提出了一种分布式启发式算法进行求解。仿真结果表明，在不同的仿真环境下，本文算法的数据卸载概率优于其他算法。考虑到概率计算的复杂性和设备的有限计算能力，提出了一种分布式启发式算法进行求解。仿真结果表明，在不同的仿真环境下，本文算法的数据卸载概率优于其他算法。