The Internet of Robotic Things (IoRT) is a technology that looks for monitoring, operating, and maintaining the tasks of multiple robots through the cloud. However, using these robots in cyberspace has a risk and an inherent problem in cybersecurity. To analyze the implications of this technology, the objective was to design, operate and submit an IoRT system with the default configuration. The proposed methodology consisted of designing an IoRT architecture; implement three robotic platforms linked to the cloud, applying a sniffing and spoofing cyberattacks, assess the impacts, and propose solutions. The experiment used three prototypes: two servo motors, a 6-degree-of-freedom arm, and a workstation with a robot. Additionally, the tools of the experiment were a conventional computer, a Raspberry Pi microcomputer, the Robotic Operative System middleware, the Kali Linux distribution, and the ThingSpeak cloud service. The contributions of the work were three, first it was proven that four types of links are sufficient to homologate, and ensure the integrity, reliability, and availability in the operation of different types of robots. Also, it was possible the connection of these robots even though they are not designed to work on the internet through a slave-robot node link. Finally, a real list of the consequences was obtained, given the vulnerabilities and the attacks tested, as well as some recommendations.Keywords: Cybersecurity, IoRT, Industry 4.0., Common Vulnerabilities and Exposures, Cloud, ROS.
The Mexican Sign Language (Lenguaje de Señas Mexicano, LSM) consists of movements of the human body and hands to communicate, this is used by the deaf-mute to articulate thoughts and emotions. Thus emerges the initiative to device an Automatic Computer System for the Translation of Sign Language to Text, in order to facilitate communication to deaf and non-deaf people. The system is based on pattern recognition. This recognition is applied continuously using a capture device, in this case a Web camera using the Visual Studio IDE specifically with the OpenCV library. The recognition of the signs is made by comparing a matrix, previously obtained by means of several algorithms for the extraction of the object of interest, in this case the hand. In tests for the recognition of numbers, an average reliability percentage of 80%was obtained for the patterns analyzed.
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