An optimal uplink traffic offloading algorithm via opportunistic communications based on machine learning

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.