IoRT cloud survivability framework for robotic AALs using HARMS

Mauricio Alejandro Gomez 1, 2 Abdelghani Chibani 1 Yacine Amirat 1 Eric Matson 2
LISSI - Laboratoire Images, Signaux et Systèmes Intelligents
Abstract : The Internet of Robotic Things, which includes ambient assisted living systems has been pushed to be developed by the research community for reasons such as the population gap between elderly people and their caregivers. Due to the critical mission that is assigned to those systems; interruptions, failures, worse still, full malfunction should not be allowed to materialize. Such systems ought to keep running in a proper way notwithstanding problems caused either by internal and external system collapses or bad intentioned actions in their surroundings. Therefore, including survivability features must be insured to Ambient Assisted Living systems (AALs) using Humans, software Agents, Robots, Machines, and Sensors (HARMS). HARMS stands for the model that allows through the indistinguishability feature to any type of actor to communicate and interact. This work proposes a framework which takes advantage of the Cloud to overcome the state explosion problem encountered when using model checking. Model checking techniques are used to find a possible solution when a problem is already faced by the system — instead of its original purpose to detect errors on the systems during the design stage. This paper presents the implementation of the proposed framework and validates the functionality with experiments. The conducted experiments evaluate the advantages of using cloud tools to offload the model checking capability for applications such as multi-agent systems.
Liste complète des métadonnées
Contributor : Lab Lissi <>
Submitted on : Thursday, May 17, 2018 - 11:17:56 AM
Last modification on : Friday, April 12, 2019 - 10:56:06 AM


  • HAL Id : hal-01794059, version 1



Mauricio Alejandro Gomez, Abdelghani Chibani, Yacine Amirat, Eric Matson. IoRT cloud survivability framework for robotic AALs using HARMS. Robotics and Autonomous Systems, Elsevier, 2018, 106, pp.192-206. ⟨hal-01794059⟩



Record views