Next location prediction has aroused great interests in the era of internet of things (IoT). With the ubiquitous deployment of sensor devices, e.g., GPS and Wi-Fi, IoT environment offers new opportunities for proactively analyzing human mobility patterns and predicting user’s future visit in low cost, no matter outdoor and indoor. In this paper, we consider the problem of next location prediction in IoT environment via a session-based manner. We suggest that user’s future intention in each session can be better inferred for more accurate prediction if patterns hidden inside both trajectory and signal strength sequences collected from IoT devices can be jointly modeled, which however existing state-of-the-art methods have rarely addressed. To this end, we propose a trajectory and sIgnal sequence (TSIS) model, where the trajectory transition regularities and signal temporal dynamics are jointly embedded in a neural network based model. Specifically, we employ gated recurrent unit (GRU) for capturing the temporal dynamics in the multivariate signal strength sequence. Moreover, we adapt gated graph neural networks (gated GNNs) on location transition graphs to explicitly model the transition patterns of trajectories. Finally, both the low-dimensional representations learned from trajectory and signal sequence are jointly optimized to construct a session embedding, which is further employed to predict the next location. Extensive experiments on two real-world Wi-Fi based mobility datasets demonstrate that TSIS is effective and robust for next location prediction compared with other competitive baselines.

下一位置预测在物联网（IoT）时代引起了人们极大的兴趣。随着诸如GPS和Wi-Fi之类的传感器设备的无处不在，物联网环境为主动分析人员出行方式并以低成本预测用户的未来访问提供了新的机会，无论室外还是室内。在本文中，我们通过基于会话的方式考虑了物联网环境中的下一个位置预测问题。我们建议，如果可以对从物联网设备收集的轨迹和信号强度序列中隐藏的模式进行联合建模，则可以更好地推断出每个会话中用户的未来意图，以便进行更准确的预测，但是，现有的最新方法很少解决。为此，我们提出了一种轨迹和信号序列（TSIS）模型，其中轨迹过渡规律和信号时态动力学共同嵌入基于神经网络的模型中。具体来说，我们采用门控循环单元（GRU）来捕获多元信号强度序列中的时间动态。此外，我们在位置转换图上采用门控图神经网络（门控GNN）来显式地对轨迹的转换模式进行建模。最后，将从轨迹和信号序列中学习到的低维表示形式进行联合优化，以构建会话嵌入，然后将其进一步用于预测下一个位置。在两个基于真实世界Wi-Fi的移动性数据集上进行的大量实验表明，与其他竞争基准相比，TSIS对于下一位置预测是有效且强大的。