Mobile Computing, Applications, and Services. 10th EAI International Conference, MobiCASE 2019, Hangzhou, China, June 14–15, 2019, Proceedings

Research Article

Distributed Learning Automata Based Data Dissemination in Networked Robotic Systems

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  • @INPROCEEDINGS{10.1007/978-3-030-28468-8_10,
        author={Gerald Henderson and Qi Han},
        title={Distributed Learning Automata Based Data Dissemination in Networked Robotic Systems},
        proceedings={Mobile Computing, Applications, and Services. 10th EAI International Conference, MobiCASE 2019, Hangzhou, China, June 14--15, 2019, Proceedings},
        proceedings_a={MOBICASE},
        year={2019},
        month={9},
        keywords={Data dissemination Networked robotic systems Learning automata},
        doi={10.1007/978-3-030-28468-8_10}
    }
    
  • Gerald Henderson
    Qi Han
    Year: 2019
    Distributed Learning Automata Based Data Dissemination in Networked Robotic Systems
    MOBICASE
    Springer
    DOI: 10.1007/978-3-030-28468-8_10
Gerald Henderson1,*, Qi Han1,*
  • 1: Colorado School of Mines
*Contact email: gxhenderson@alumni.mines.edu, qhan@mines.edu

Abstract

Networked robotics systems often work in collaboration to accomplish tasks. The random environments the robots work in render any previous contact data between robots useless as the contact patterns are different for each deployment. In the case of military and disaster scenarios, delivering data items quickly is imperative to the success of a mission. However, robots have limited battery and need a lightweight protocol that maximizes data delivery ratio and minimizes data delivery latency while consuming minimal energy. We present two learning automata based data dissemination protocols, LADD and sc-LADD. LADD uses learning automata with direct connections to all neighboring nodes to make efficient and accurate forwarding decisions while sc-LADD uses learning automata and exploits the clustering nature of the robotic systems to abstract clusters/groups and reduce the number of decisions available to the learning automata, which also reduces overhead.