Trust identification through cognitive correlates with emphasizing attention in cloud robotics

Attention and Trust remain the crux of robotic sensory perception when associated with cloud robotics. It involves tasks that compete for attention from several stimuli taken by the robot itself upon its selective attention mode, focuses its attention on the task with higher precedence, and filters out the rest to be performed later. The robot could unload storage-extensive and computation extensive jobs towards the cloud while keeping trust establishment in control. Factors leading to these robots' availability, confidentiality, data protection, and isolation security trigger attention. Trust involving suppliers and users is intended to attain safety measures that endorse various cloud suppliers' status and accessible services. It takes several input stimuli from the robot, i.e., confidence, experience, and emotion, and gives output as trust level to pay attention during social interactions among robots. Input parameters are mapped into fuzzy sets, taking a range of input and output membership functions. The fuzzifier and defuzzifier are designed according to the proposed scheme. The developed system, named Trust Annotator, is tested and analyzed using MATLAB R2021a. Mamdani model is conferred, which yielded some unusual yet promising outcomes. These outcomes show conformity between the designed and simulated systems. (C) 2022 THE AUTHORS. Published by Elsevier B.V. on behalf of Faculty of Computers and Information, Cairo University.