We present a novel data-driven approach to perform smooth Wi-Fi/cellular handovers on smartphones. Our approach relies on data provided by multiple smartphone sensors (e.g., Wi-Fi RSSI, acceleration, compass, step counter, air pressure) to predict Wi-Fi connection loss and uses Multipath TCP to dynamically switch between different connectivity modes. We train a random forest classifier and an artificial neural network on real-world sensor data collected by five smartphone users over a period of three months. The trained models are executed on smartphones to reliably predict Wi-Fi connection loss 15 seconds ahead of time, with a precision of up to 0.97 and a recall of up to 0.98. Furthermore, we present results for four DASH video streaming experiments that run on a Nexus 5 smartphone using available Wi-Fi/cellular networks. The neural network predictions for Wi-Fi connection loss are used to establish MPTCP subflows on the cellular link. The experiments show that our approach provides seamless wireless connectivity, improves quality of experience of DASH video streaming, and requires less cellular data compared to handover approaches without Wi-Fi connection loss predictions.
翻译:我们对智能手机用户收集的真实世界传感器数据进行了为期三个月的随机森林分类器和人工神经网络培训,对5个智能手机用户收集的真实世界传感器数据进行了为期三个月的随机森林分类器和人工神经网络培训,在智能手机用户中实施了经过培训的模型,以可靠地预测无线连接损失15秒钟,精确度达到0.97,回顾度达到0.98。 此外,我们介绍了4个DASH视频流学实验的结果,这些实验利用现有的Wi-Fi/手机网络在Nexus 5智能手机上进行,利用现有的Wi-Fi/手机网络对Wi-Fi连接损失进行神经网络预测,用于在手机链接上建立MPTCP子流。实验显示,我们的方法提供了无缝无线连接,提高了DASH视频流流的经验质量,与无Wi-Fi连接率交接方法相比,需要较少的手机数据。