IoT-enabled Wide Area Monitoring with Privacy-aware Adaptive and Reflective Designs

• Main Authors: Chang-Ru Chen, 陳昶儒
• Other Authors: Ching-Hu Lu
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/99k6up

碩士 === 國立臺灣科技大學 === 電機工程系 === 106 === With the development of Interet of Things (IoT), real-time wide-area monitoring through a large number of cameras will generate a plethora of streaming data, so machine learning incorporating edge computing has attracted increasing attention. However, most prior machine learning for real-time wide-area monitorning ignored setting a learning goal, e.g., protecting user privacy. Next, when any concept drift or uncertainty occurs in the streaming data, the existing model to process the data often cannot effectively selfadapt, thus gradually compromising model performance. In addition, since streaming images do not have ground-truth labels, their online models cannot be reliably corrected once any concept drift occurs. Furthmore, a new wide-area monitorning system cannot be quickly deployed due to failing to reuse previously learned knowledge from the existing systems. To address above issues, this study incorporates human’s self-regulated learning. The study also uses edge devices or cameras to collaborate with cloud servers for balancing computation burdens. There are three elements in self-regulated learning, including Forethought, Performance, and Reflection, and this study proposes their couterparts in marchine learning. For the Forethought stage (a.k.a. mForethought), a goal model can be trained to effectively reduce user privacy concerns in streaming images, which is the preset goal for the demonstration system. Next, a performance model empowered by the proposed generalized meta-cognitive based learning framework for the Performance stage (a.k.a. m Performance) can monitor the occurrence of concept drift for later online model adaption. The Reflection stage (a.k.a. mReflection) uses an expert model to correct mistakes and effectively reduce human interventions given no ground-truth can be obtained. Finally, we propose cross-field knowledge transfer, which uses transfer learning to leverage the exiting data or learned knowledge for rapidly adapting to a new field. With the above enhancements, the experiment results show that the goal model of the mForethought can effiectively blur the detected pedestrians or the whole image yet at the cost of slightly compromsing precision to ensure user privacy. The generalized meta-cognitive based learning framework of the mPerformance enhanced a regular YOLOv2 deep network with the cognitive ability to detect concept drift, the we used knowledge distillation to reduce time of model adaptation by 38% even using blurred perdstrians or images. The expert model of the mReflection reduces about 50% of human intervention for labeling data, meanwhile maintaining as much precsion. The cross-field knowledge transfer stage uses transfer learning to improve model precision of a new field by 37% under the same training iteration for a new field.