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Compressed Cluster Sensing in Multiagent IoT Control
Traditional multiagent system control relies primarily on inter-agent local communications. In large-scale IoT systems it may appear hard to synthesize local control actions in a simple manner. Cluster control possibilities are thus thoroughly discussed, with possible control goals stated. The relat...
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creator | Uzhva, Denis Granichin, Oleg Granichina, Olga |
description | Traditional multiagent system control relies primarily on inter-agent local communications. In large-scale IoT systems it may appear hard to synthesize local control actions in a simple manner. Cluster control possibilities are thus thoroughly discussed, with possible control goals stated. The relation between multiagent state sparsity and cluster patterns is illustrated, for further utilization of sparsity for cluster control. Consequently, the problem of cluster identification is stated, and a possible solution is proposed in the form of the compressed cluster sensing algorithm. The algorithm utilizes compressed sensing methodology for a compact representation of agent states, which is then used to synthesize compact control actions in a low-dimensional space, without requiring to specify cluster locations. |
doi_str_mv | 10.1109/CDC51059.2022.9992703 |
format | conference_proceeding |
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In large-scale IoT systems it may appear hard to synthesize local control actions in a simple manner. Cluster control possibilities are thus thoroughly discussed, with possible control goals stated. The relation between multiagent state sparsity and cluster patterns is illustrated, for further utilization of sparsity for cluster control. Consequently, the problem of cluster identification is stated, and a possible solution is proposed in the form of the compressed cluster sensing algorithm. The algorithm utilizes compressed sensing methodology for a compact representation of agent states, which is then used to synthesize compact control actions in a low-dimensional space, without requiring to specify cluster locations.</description><identifier>EISSN: 2576-2370</identifier><identifier>EISBN: 9781665467612</identifier><identifier>EISBN: 1665467614</identifier><identifier>DOI: 10.1109/CDC51059.2022.9992703</identifier><language>eng</language><publisher>IEEE</publisher><subject>Clustering algorithms ; Heuristic algorithms ; Nonlinear dynamical systems ; Numerical models ; Sensors ; Synchronization ; Wireless sensor networks</subject><ispartof>2022 IEEE 61st Conference on Decision and Control (CDC), 2022, p.3580-3585</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9992703$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,27925,54555,54932</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9992703$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Uzhva, Denis</creatorcontrib><creatorcontrib>Granichin, Oleg</creatorcontrib><creatorcontrib>Granichina, Olga</creatorcontrib><title>Compressed Cluster Sensing in Multiagent IoT Control</title><title>2022 IEEE 61st Conference on Decision and Control (CDC)</title><addtitle>CDC</addtitle><description>Traditional multiagent system control relies primarily on inter-agent local communications. In large-scale IoT systems it may appear hard to synthesize local control actions in a simple manner. Cluster control possibilities are thus thoroughly discussed, with possible control goals stated. The relation between multiagent state sparsity and cluster patterns is illustrated, for further utilization of sparsity for cluster control. Consequently, the problem of cluster identification is stated, and a possible solution is proposed in the form of the compressed cluster sensing algorithm. The algorithm utilizes compressed sensing methodology for a compact representation of agent states, which is then used to synthesize compact control actions in a low-dimensional space, without requiring to specify cluster locations.</description><subject>Clustering algorithms</subject><subject>Heuristic algorithms</subject><subject>Nonlinear dynamical systems</subject><subject>Numerical models</subject><subject>Sensors</subject><subject>Synchronization</subject><subject>Wireless sensor networks</subject><issn>2576-2370</issn><isbn>9781665467612</isbn><isbn>1665467614</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2022</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotz7FOwzAQgGGDhEQpfQKE5BdIsH0-OzciA6VSEQNlrpzkUhmlSRWnA2_PQKd_-6RfiEetSq0VPYWXgFohlUYZUxKR8QquxIp8pZ1D67zT5losDHpXGPDqVtzl_KMUEFlYCBvG42ninLmVoT_nmSf5xUNOw0GmQX6c-znFAw-z3Iw7GcZhnsb-Xtx0sc-8unQpvt9ed-G92H6uN-F5WyQNMBcOWVmqY40N2bohil2FjfMIWnNnbQ0WqVXguqoxpmpqTw5a5SyjJocES_Hw7yZm3p-mdIzT7_4yCX9wWkTw</recordid><startdate>20221206</startdate><enddate>20221206</enddate><creator>Uzhva, Denis</creator><creator>Granichin, Oleg</creator><creator>Granichina, Olga</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>20221206</creationdate><title>Compressed Cluster Sensing in Multiagent IoT Control</title><author>Uzhva, Denis ; Granichin, Oleg ; Granichina, Olga</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i133t-65e049bab5c94bc99af85c675311ef44b3459d036f8c228cb7963d064e5196593</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Clustering algorithms</topic><topic>Heuristic algorithms</topic><topic>Nonlinear dynamical systems</topic><topic>Numerical models</topic><topic>Sensors</topic><topic>Synchronization</topic><topic>Wireless sensor networks</topic><toplevel>online_resources</toplevel><creatorcontrib>Uzhva, Denis</creatorcontrib><creatorcontrib>Granichin, Oleg</creatorcontrib><creatorcontrib>Granichina, Olga</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Uzhva, Denis</au><au>Granichin, Oleg</au><au>Granichina, Olga</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Compressed Cluster Sensing in Multiagent IoT Control</atitle><btitle>2022 IEEE 61st Conference on Decision and Control (CDC)</btitle><stitle>CDC</stitle><date>2022-12-06</date><risdate>2022</risdate><spage>3580</spage><epage>3585</epage><pages>3580-3585</pages><eissn>2576-2370</eissn><eisbn>9781665467612</eisbn><eisbn>1665467614</eisbn><abstract>Traditional multiagent system control relies primarily on inter-agent local communications. In large-scale IoT systems it may appear hard to synthesize local control actions in a simple manner. Cluster control possibilities are thus thoroughly discussed, with possible control goals stated. The relation between multiagent state sparsity and cluster patterns is illustrated, for further utilization of sparsity for cluster control. Consequently, the problem of cluster identification is stated, and a possible solution is proposed in the form of the compressed cluster sensing algorithm. The algorithm utilizes compressed sensing methodology for a compact representation of agent states, which is then used to synthesize compact control actions in a low-dimensional space, without requiring to specify cluster locations.</abstract><pub>IEEE</pub><doi>10.1109/CDC51059.2022.9992703</doi><tpages>6</tpages></addata></record> |
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subjects | Clustering algorithms Heuristic algorithms Nonlinear dynamical systems Numerical models Sensors Synchronization Wireless sensor networks |
title | Compressed Cluster Sensing in Multiagent IoT Control |
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