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  3. Volume 7, Number 2, 2022
  4. Coscheduling Spatial Self-organization and Distributed Data Collection in Multi-agent System

Coscheduling Spatial Self-organization and Distributed Data Collection in Multi-agent System

ACPS.
2022;
Volume 7, Number 2
: pp. 76 - 82
https://doi.org/10.23939/acps2022.02.076
Authors:
  1. Alexey Botchkaryov
1
Lviv Polytechnic National University, Computer Engineering Department

The problem of coscheduling spatial self-organization control processes and distributed data collection processes in a multi-agent system has been considered. The goal of coscheduling is to find and use the possibilities of functional coordination of these processes and increase the efficiency of the multi-agent system due to their parallel execution. An analysis of the main features of spatial self-organization tasks that affect the solution of the problem of coscheduling has been carried out. Variants of the mobile agent robotic platform configuration and the problem of the dependence of spatial self-organization algorithms on the type of robotic platform have been considered. A method of coscheduling of spatial self-organization and distributed data collection by coordinated parallel execution of the corresponding data collection process and the process of controlling mobile agent motion has been proposed. The method of coscheduling is implemented using the interaction protocol of these processes and the algorithm for planning their parallel execution using functional decomposition. The simulation results of the proposed method of coscheduling are given. It is proved that the proposed method of coscheduling provides acceleration of computations in the decision-making module of the mobile agent due to more efficient parallelization. On average, for typical values of parameters of control processes, the proposed method of coscheduling provides acceleration of computations in the decision-making module of the mobile agent by 40.6 %.

coscheduling
distributed data collection
spatial self-organization
mobile agent
multi-agent system
  1. Dorri, A., Kanhere, S., Jurdak, R. (2018). Multi-Agent Systems: A Survey, in IEEE Access, vol. 6. – pp. 28573‑28593, DOI: 10.1109/ACCESS.2018.2831228.
  2. Rizk, Y., Awad, M., Tunstel, E. (2018). Decision Making in Multi-Agent Systems: A Survey, in IEEE Transactions on Cognitive and Developmental Systems, vol. 10, no. 3. – pp. 514‑529, DOI: 10.1109/TCDS.2018.2840971.
  3. Arup Kumar Sadhu, Amit Konar (2020). Multi-Agent Coordination: A Reinforcement Learning Approach, Wiley. – 320 p.
  4. Botchkaryov, A., Golembo, V., Paramud, Y., Yatsyuk, V. (2019). Cyber-physical systems: data collection technologies, A. Melnyk (ed.), Lviv, «Magnolia 2006». – 176 p. (in Ukrainian).
  5. Shermin, A., Dhongdi, S. (2022). Review of Underwater Mobile Sensor Network for ocean phenomena monitoring, Journal of Network and Computer Applications, Volume 205, Issue C. – pp. 1‑24. DOI: 10.1016/j.jnca.2022.103418.
  6. Wang, Z., Li, H.X., Chen, C. (2020). Reinforcement Learning-Based Optimal Sensor Placement for Spatiotemporal Modeling. IEEE Transactions on Cybernetics; 50(6). – pp. 2861‑2871. DOI: 10.1109/TCYB.2019.2901897.
  7. Song, M., Hu, C., Gong, W., Yan, X. (2022). Domain Knowledge-Based Evolutionary Reinforcement Learning for Sensor Placement. Sensors (Basel); 22(10):3799. – pp. 1‑17. DOI: 10.3390/s22103799.
  8. Meng, X., Inaltekin, H., Krongold, B. (2019). Deep Reinforcement Learning-Based Topology Optimization for Self-Organized Wireless Sensor Networks, 2019 IEEE Global Communications Conference (GLOBECOM). – pp. 1‑6, DOI: 10.1109/GLOBECOM38437.2019.9014179.
  9. Krishnan, V., Martínez, S. (2018). Distributed Control for Spatial Self-Organization of Multi-agent Swarms, SIAM Journal on Control and Optimization, vol. 56, Iss. 5. – pp. 3642‑3667. DOI: 10.1137/16M1080926.
  10. Liu, Y., Bucknall, R. (2018). A survey of formation control and motion planning of multiple unmanned vehicles. Robotica. 36. – pp. 1‑29. DOI: 10.1017/S0263574718000218.
  11. Sun, Z. (2018). Cooperative Coordination and Formation Control for Multi-agent Systems. Springer Cham. – 179 p. DOI: 10.1007/978-3-319-74265-6.
  12. Krishnan, V., Martínez, S. (2022). A multiscale analysis of multi-agent coverage control algorithms, Automatica, Volume 145,110516.- pp. 1‑26. DOI: 10.1016/j.automatica.2022.110516.
  13. Hu, J., Bhowmick, P., Jang, I., Arvin F., Lanzon, A. (2021). A Decentralized Cluster Formation Containment Framework for Multirobot Systems, in IEEE Transactions on Robotics, vol. 37, no. 6. – pp. 1936‑1955, DOI: 10.1109/TRO.2021.3071615.
  14. Hu, J., Bhowmick, P., Lanzon, A. (2021). Group Coordinated Control of Networked Mobile Robots with Applications to Object Transportation, in IEEE Transactions on Vehicular Technology, vol. 70, no. 8. – pp. 8269‑8274, DOI: 10.1109/TVT.2021.3093157.
  15. Hu, J., Turgut, A., Lennox B., Arvin, F. (2022). Robust Formation Coordination of Robot Swarms with Nonlinear Dynamics and Unknown Disturbances: Design and Experiments, in IEEE Transactions on Circuits and Systems II: Express Briefs, vol.69, no.1. – pp. 114‑118, DOI: 10.1109/TCSII.2021.3074705.
  16. Amrani, N. E. A., Snineh, S. M., Youssfi, M., Abra, O. E. K., Bouattane, O. (2021). Interoperability model between heterogeneous MAS platforms based on mobile agent and reinforcement learning, 2021 Fifth International Conference on Intelligent Computing in Data Sciences (ICDS). – pp. 1‑8, DOI: 10.1109/ICDS53782.2021.9626723.