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.

Artificial Vision on Edge IoT Devices: A Practical Case for 3D Data Classification Nowadays, with the huge advance of sensor technology and the increase of the amount of data generated by them, techniques have to be developed to be able to process all this amount of information in realtime applications on edge devices, close to where data is being generated. If all that information has to be sent to the cloud to be processed, it has certain disadvantages in terms of latency, bandwidth, privacy and reliability, compared to locally processing it on the edge. In this paper, the implementation of deep learning algorithms in low power and limited resources devices in an Internet of Things scenario is studied. In order to work in realtime applications, the influence of different low power consumption deep learning hardware accelerators is studied. Finally, a practical case for smart farming is shown with comparative results in terms of power consumption and performance when running the same artificial vision algorithm on different devices.