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Título
Message Encryption in Robot Operating System: Collateral Effects of Hardening Mobile Robots
Autor
Facultad/Centro
Área de conocimiento
Título de la revista
Frontiers in ICT
Cita Bibliográfica
Rodríguez-Lera, F. J., Matellán-Olivera, V., Balsa-Comerón, J., Guerrero-Higueras, Á. M., & Fernández-Llamas, C. (2018). Message encryption in robot operating system: Collateral effects of hardening mobile robots. Frontiers in ICT, 5(MAR). https://doi.org/10.3389/FICT.2018.00002
Editorial
Frontiers
Fecha
2018-03-02
Resumen
[EN] In human–robot interaction situations, robot sensors collect huge amounts of data from
the environment in order to characterize the situation. Some of the gathered data ought
to be treated as private, such as medical data (i.e., medication guidelines), personal, and
safety information (i.e., images of children, home habits, alarm codes, etc.). However,
most robotic software development frameworks are not designed for securely managing
this information. This paper analyzes the scenario of hardening one of the most widely
used robotic middlewares, Robot Operating System (ROS). The study investigates a
robot’s performance when ciphering the messages interchanged between ROS nodes
under the publish/subscribe paradigm. In particular, this research focuses on the nodes
that manage cameras and LIDAR sensors, which are two of the most extended sensing
solutions in mobile robotics, and analyzes the collateral effects on the robot’s achievement under different computing capabilities and encryption algorithms (3DES, AES, and
Blowfish) to robot performance. The findings present empirical evidence that simple
encryption algorithms are lightweight enough to provide cyber-security even in lowpowered robots when carefully designed and implemented. Nevertheless, these techniques come with a number of serious drawbacks regarding robot autonomy and performance if they are applied randomly. To avoid these issues, we define a taxonomy that links
the type of ROS message, computational units, and the encryption methods. As a result,
we present a model to select the optimal options for hardening a mobile robot using ROS.
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