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

Simple item page
dc.citation.epage82
dc.citation.issue2
dc.citation.spage76
dc.contributor.affiliationLviv Polytechnic National University
dc.contributor.authorBotchkaryov, A.
dc.coverage.placenameЛьвів
dc.coverage.placenameLviv
dc.date.accessioned2024-03-19T10:17:56Z
dc.date.available2024-03-19T10:17:56Z
dc.date.created2022-02-28
dc.date.issued2022-02-28
dc.description.abstractThe problem of coscheduling spatial selforganization 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 selforganization 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%.
dc.format.extent76-82
dc.format.pages7
dc.identifier.citationBotchkaryov A. Coscheduling Spatial Self-organization and Distributed Data Collection in Multi-agent System / A. Botchkaryov // Advances in Cyber-Physical Systems. — Lviv : Lviv Politechnic Publishing House, 2022. — Vol 7. — No 2. — P. 76–82.
dc.identifier.citationenBotchkaryov A. Coscheduling Spatial Self-organization and Distributed Data Collection in Multi-agent System / A. Botchkaryov // Advances in Cyber-Physical Systems. — Lviv : Lviv Politechnic Publishing House, 2022. — Vol 7. — No 2. — P. 76–82.
dc.identifier.doidoi.org/10.23939/acps2022.02.076
dc.identifier.issn2524-0382
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/61492
dc.language.isoen
dc.publisherВидавництво Львівської політехніки
dc.publisherLviv Politechnic Publishing House
dc.relation.ispartofAdvances in Cyber-Physical Systems, 2 (7), 2022
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dc.relation.referencesen[1] Dorri, A., Kanhere, S., Jurdak, R. (2018). Multi-Agent Systems: A Survey, in IEEE Access, vol. 6, 28573–28593. DOI: 10.1109/ACCESS.2018.2831228.
dc.relation.referencesen[2] Rizk, Y., Awad, M., Tunstel, E. (2018). DecisionMaking in Multi-Agent Systems: A Survey, in IEEE Transactions on Cognitive and Developmental Systems, vol. 10, no. 3, 514–529. DOI: 10.1109/TCDS.2018.2840971.
dc.relation.referencesen[3] Arup Kumar Sadhu, Amit Konar (2020). Multi-Agent Coordination: A Reinforcement Learning Approach, Wiley. 320 p.
dc.relation.referencesen[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).
dc.relation.referencesen[5] Shermin, A., Dhongdi, S. (2022). Review of Underwater Mobile Sensor Network for ocean phenomena monitoring, Journal of Network and Computer Applications, Vol. 205, Is. C, 1–24. DOI: 10.1016/j.jnca.2022.103418.
dc.relation.referencesen[6] Wang, Z., Li, H. X., Chen, C. (2020). Reinforcement Learning-Based Optimal Sensor Placement for Spatiotemporal Modeling. IEEE Transactions on Cybernetics; 50(6), 2861–2871. DOI: 10.1109/TCYB.2019.2901897.
dc.relation.referencesen[7] Song, M., Hu, C., Gong, W., Yan, X. (2022). Domain Knowledge-Based Evolutionary Reinforcement Learning for Sensor Placement. Sensors (Basel); 22(10):3799, 1–17. DOI: 10.3390/s22103799.
dc.relation.referencesen[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), 1–6. DOI: 10.1109/GLOBECOM38437.2019.9014179.
dc.relation.referencesen[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, 3642–3667. DOI: 10.1137/16M1080926.
dc.relation.referencesen[10] Liu, Y., Bucknall, R. (2018). A survey of formation control and motion planning of multiple unmanned vehicles. Robotica, 36, 1–29. DOI: 10.1017/S0263574718000218.
dc.relation.referencesen[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.
dc.relation.referencesen[12] Krishnan, V., Martínez, S. (2022). A multiscale analysis of multi-agent coverage control algorithms, Automatica, Volume 145, 110516, 1–26. DOI: 10.1016/j.automatica.2022.110516.
dc.relation.referencesen[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, 1936–1955. DOI: 10.1109/TRO.2021.3071615.
dc.relation.referencesen[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, 8269–8274. DOI: 10.1109/TVT.2021.3093157.
dc.relation.referencesen[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. 114–118. DOI: 10.1109/TCSII.2021.3074705.
dc.relation.referencesen[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), 1–8. DOI: 10.1109/ICDS53782.2021.9626723.
dc.rights.holder© Національний університет “Львівська політехніка”, 2022
dc.rights.holder© Botchkaryov A., 2022
dc.subjectcoscheduling
dc.subjectdistributed data collection
dc.subjectspatial self-organization
dc.subjectmobile agent
dc.subjectmulti-agent system
dc.titleCoscheduling Spatial Self-organization and Distributed Data Collection in Multi-agent System
dc.typeArticle

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Advances In Cyber-Physical Systems. – 2022. – Vol. 7, No. 2