A Crowd-Based Image Learning Framework using Edge Computing for Smart City Applications

• Published in: 2019 IEEE Fifth International Conference on Multimedia Big Data (BigMM) pp. 11 - 20
• Main Authors: Constantinou, Giorgos, Sankar Ramachandran, Gowri, Alfarrarjeh, Abdullah, Kim, Seon Ho, Krishnamachari, Bhaskar, Shahabi, Cyrus
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.09.2019
• Subjects: Computational modeling; Crowd-based Learning; Crowdsourcing; Data models; Edge Devices; Feature extraction; Image edge detection; Machine Learning; Media; Performance evaluation; Servers; Smart Cities
• DOI: 10.1109/BigMM.2019.00-47

Smart city applications covering a wide area such as traffic monitoring and pothole detection are gradually adopting more image machine learning algorithms utilizing ubiquitous camera sensors. To support such applications, an edge computing paradigm focuses on processing large amount of multimedia data at the edge to offload processing cost and reduce long-distance traffic and latency. However, existing edge computing approaches rely on pre-trained static models and are limited in supporting diverse classes of edge devices as well as learning models to support them. This research proposes a novel crowd-based learning framework which allows edge devices with diverse resource capabilities to perform machine learning towards the realization of image-based smart city applications. The intelligent retraining algorithm allows sharing key visual features to achieve a higher accuracy based on the temporal and geospatial uniqueness. Our evaluation shows the trade-off between accuracy and the resource constraints of the edge devices, while the model re-sizing option enables running machine learning models on edge devices with high flexibility.