On real-time data fusion in edge computing

Recent years have witnessed a drastic increase in the scale of data generated by sensors and smart devices across the city. While the data scale is increasing, it is more and more important to process the data in a real-time manner. Numerous new applications are to be enabled by low latency real-time processing. The inevitable transmission delay in cloud computing brings the new computing paradigm "edge computing'', which aims to locate computing resources near the end users. Meanwhile, the inevitable weak computing power of edge servers brings challenges to information retrieval quality. In this thesis, we explored multimodality/sensor fusion solutions to enable new applications and to optimize the latency-accuracy trade-off in resource-limited edge scenarios. We first presented a synchronous multimodality stream analytics framework with a typical use case: profanity filtering in real-time video conference. We implemented and evaluated our prototype by real-world scenario test cases. Our system achieves good profanity filtering rate (89%) while maintaining the synchronicity of the video stream and not affecting the overall latency (400 ms), which indicates the potential of multimodality stream processing for new applications in resource-limited scenarios. We then presented a high-accuracy low-latency road information collection system based on object-level fusion. By a multi-path resistant design, our prototype system outperforms not only a visual-only solution but also a state-of-the-art camera-radar sensor fusion solution. Extensive real-world evaluation shows that our system can reduce 25% of the localization error and increase 45\% of the recognition rate comparing with a state-of-the-art method, which confirms the great potential of our method in achieving high-accuracy low-latency object information retrieval.