We modified the object detection demo in Tensorflow Lite to enable an Android smartphone to offload its real-time camera captured video frames to an edge server. We designed and implemented an edge-based mobile AR system to analyze the interactions between AR configurations and the mobile device's energy consumption. The mobile AR client transfers the converted RGB frames to the edge server through a TCP socket connection. To avoid the processing of stale frames, the mobile AR client sends the latest camera captured frame to the server and waits for the detection result before sending the next frame for detection. The edge server is developed to process received image frames and to send the detection results back to the Mobile AR client. Two major modules are implemented on the edge server: (i) the communication handler which establishes a TCP socket connection with the Mobile AR device and (ii) the analytics handler which performs object detection for the Mobile AR client. The analytics handler is designed based on a custom framework called Darknet with GPU acceleration and runs YOLOv3, a large Convolutional Neural Networks (CNN) model.

If you use the code in your work please cite our papers!

• Haoxin Wang and Jiang Xie, "User Preference Based Energy-Aware Mobile AR System with Edge Computing," in Proc. IEEE International Conference on Computer Communications (INFOCOM 2020), Beijing, China, Apr. 2020, pp.1-10. (https://infocom2020.ieee-infocom.org/accepted-paper-list-main-conference)

• Haoxin Wang, BaekGyu Kim, Jiang Xie, and Zhu Han, "How is Energy Consumed in Smartphone Deep Learning Apps? Executing Locally vs. Remotely," in Proc. IEEE Global Communications Conference (GLOBECOM 2019), Waikoloa, HI, Dec. 2019, pp.1-6. (https://doi.org/10.1109/GLOBECOM38437.2019.9013647)