Edge-based Content-aware Crowdsourcing Approach for Image Sensing in Disaster Environment
Ziming Zhao, Fang Liu, Zhiping Cai, Nong Xiao · 2017
Photos obtained via crowdsourcing can be used in image sensing for disaster management. Due to the weak communication environment after a disaster, it is difficult to transfer the huge amount of crowdsourced photos. To address this problem, we propose COCO, a content-aware crowdsourcing system that leverages edge computing to support real-time image sensing in disaster environment. COCO filters the crowdsourced images at the data source and only uploads the images that contain relevant objects which the application is interested in. We use a machine-learning based computer vision detector to understand the content of images. Considering the resource constraints of mobile devices, we implement the computer vision detector at the edge server which located in the close proximity to data source. As the unstable network bandwidth is normal in disaster environment, we propose an adaptive mechanism to further improve the sensing performance. We have implemented the COCO prototype which is evaluated via a real-world dataset. The experimental results demonstrate the effectiveness of COCO.