Toward Intelligent Visual Sensing and Lowcost Analysis: A Collaborative Computing Approach In the big data era, there has been an increasing consensus that the label information, computational resources and communication bandwidth are particularly precious. Stateoftheart research is revolutionizing the vision systems of the smart city, which converts the visual signals from sensory input into feature representations and conveys the compact feature for analysis by using the computational resources of both front and back ends. To deploy a robust model, large amounts of labeled data are usually required, and thereby heavy computational and communication resources are incurred in model training as well as inference. However, the computational resources in frontend devices are usually constrained, and heavy transmission burden is imposed when leveraging multiple models amongst different ends. In this work, we propose a novel collaborative computing approach for intelligent sensing and lowcost analysis, which reduces the requirement of labeled data and communication cost, and balances the computational load in model training and inference. By incorporating the adversarial learning mechanism into collaborative model training, knowledge of different domains can be better exploited. Moreover, the learned models are deployed for inference in a collaborative manner, in which part of model is placed in frontends for extracting intermediate feature maps, and part of the model remains in back ends for inference with received feature maps. The effectiveness of the proposed approach has been validated in the context of an emerging digital retina system for smart city intelligent applications.

An Optimal Coverage Model for the Deployment of IoT Devices in Featurebased Video Transmission Systems With the development of visual devices and artificial intelligence technologies, many researchers focus on how to deploy a massive of IoT devices efficiently. The visual devices in smart cities produce large amount of realtime visual data, which challenges the ability of current transmission architecture in Cloud computing. We address how to improve the utilization of both computation and transmission resources for visual data processing through introducing the Fog computing technologies. We propose to use deep learning models and salient zones extraction method to decrease the data amount as well as maintaining important features of visual data. Furthermore, we develop how to arrange Fog nodes reasonably according to the location of the end devices and the data produced from the devices. Simulation results on public video dataset demonstrate that the proposed method achieves 48.85% in term of SSIM higher than that of HEVC. The proposed approach decreases the delay by around 75.93% than the traditional transmission without Fog nodes. The proposed method achieves a high compression ratio and guarantees the quality of the video.