Assistive Technology through Internet of Things and Edge Computing This paper proposes a framework that applies computer vision and machine learning techniques, through an Internet of Things (IoT) network, with the use of cloud computing for capacity increase. Images are captured by an IoT device and sent for an edge element (IoT node), which processes them, identifies objects, computes distances, and, ultimately, converts that information into audible commands, in order to provide guidance for visuallyimpaired people. Unrecognized objects are sent to a cloud service, in order to provide network retraining and, consequently, system evolution. With the goal of validation, the proposed framework was implemented, using known and proven technologies, such as Raspberry Pi, Esp32CAM, OpenCv, Tensorflow, GoogleTTS and Python. Finally, experimental results demonstrated its feasibility and effectiveness.

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.