Advanced communication technology of IoT era enables a heterogeneous connectivity where mobile devices broadcast information to everything. Previous short-range on-board sensor perception system attached to moblie applications such as robots and vehicles could be transferred to long-range mobilesensing perception system, which can be used as part of a more extensive intelligent system surveilling real-time state of the environment. However, the mobile sensing perception brings new challenges for how to efficiently analyze and intelligently interpret the deluge of IoT data in mission-critical services. In this article, we model the challenges as latency, packet loss and measurement noise which severely deteriorate the reliability and quality of IoT data. We integrate the artificial intelligence into IoT to tackle these challenges. We propose a novel architecture that leverages recurrent neural networks (RNN) and Kalman filtering to anticipate motions and interactions between objects. The basic idea is to learn environment dynamics by recurrent networks. To improve the robustness of IoT communication, we use the idea of Kalman filtering and deploy a prediction and correction step. In this way, the architecture learns to develop a biased belief between prediction and measurement in the different situation. We demonstrate our approach with synthetic and real-world datasets with noise that mimics the challenges of IoT communications. Our method brings a new level of IoT intelligence. It is also lightweight compared to other state-of-theart convolutional recurrent architecture and is ideally suitable for the resource-limited mobile applications