Deep Anticipation: Light Weight Intelligent Mobile Sensing in IoT by Recurrent Architecture

TL;DR

This paper introduces a lightweight recurrent neural network architecture combined with Kalman filtering to improve IoT data analysis and prediction in mobile sensing applications, addressing latency, packet loss, and noise challenges.

Contribution

The paper presents a novel, resource-efficient architecture integrating RNNs and Kalman filtering for robust IoT motion anticipation and interaction modeling.

Findings

Effective motion prediction in noisy IoT environments

Robustness against latency and packet loss demonstrated

Lightweight design suitable for mobile applications

Abstract

The rapid growth of IoT era is shaping the future of mobile services. Advanced communication technology enables a heterogeneous connectivity where mobile devices broadcast information to everything. Mobile applications such as robotics and vehicles connecting to cloud and surroundings transfer the short-range on-board sensor perception system to long-range mobile-sensing perception system. 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 interac- tions 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-the-art convolutional recurrent architecture and is ideally suitable for the resource-limited mobile applications.