Many-to-Many Data Aggregation Scheduling Based on Multi-Agent Learning for Multi-Channel WSN
Many-to-many data aggregation has become an indispensable technique to realize the simultaneous executions of multiple applications with less data traffic load and less energy consumption in a multi-channel WSN (wireless sensor network). The problem of how to efficiently allocate time slot and chann...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/11/20/3356 |
| _version_ | 1797473629993172992 |
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| author | Yao Lu Keweiqi Wang Erbao He |
| author_facet | Yao Lu Keweiqi Wang Erbao He |
| author_sort | Yao Lu |
| collection | DOAJ |
| description | Many-to-many data aggregation has become an indispensable technique to realize the simultaneous executions of multiple applications with less data traffic load and less energy consumption in a multi-channel WSN (wireless sensor network). The problem of how to efficiently allocate time slot and channel for each node is one of the most critical problems for many-to-many data aggregation in multi-channel WSNs, and this problem can be solved with the new distributed scheduling method without communication conflict outlined in this paper. The many-to-many data aggregation scheduling process is abstracted as a decentralized partially observable Markov decision model in a multi-agent system. In the case of embedding cooperative multi-agent learning technology, sensor nodes with group observability work in a distributed manner. These nodes cooperated and exploit local feedback information to automatically learn the optimal scheduling strategy, then select the best time slot and channel for wireless communication. Simulation results show that the new scheduling method has advantages in performance when comparing with the existing methods. |
| first_indexed | 2024-03-09T20:18:48Z |
| format | Article |
| id | doaj.art-c9cc9c36791144a684ee1e2e43fffdbb |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T20:18:48Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-c9cc9c36791144a684ee1e2e43fffdbb2023-11-23T23:53:49ZengMDPI AGElectronics2079-92922022-10-011120335610.3390/electronics11203356Many-to-Many Data Aggregation Scheduling Based on Multi-Agent Learning for Multi-Channel WSNYao Lu0Keweiqi Wang1Erbao He2School of Mechanical and Electrical Engineering, Guizhou Normal University, Guiyang 550025, ChinaChina Unicom Guiyang Branch, Guiyang 550002, ChinaSchool of Mechanical and Electrical Engineering, Guizhou Normal University, Guiyang 550025, ChinaMany-to-many data aggregation has become an indispensable technique to realize the simultaneous executions of multiple applications with less data traffic load and less energy consumption in a multi-channel WSN (wireless sensor network). The problem of how to efficiently allocate time slot and channel for each node is one of the most critical problems for many-to-many data aggregation in multi-channel WSNs, and this problem can be solved with the new distributed scheduling method without communication conflict outlined in this paper. The many-to-many data aggregation scheduling process is abstracted as a decentralized partially observable Markov decision model in a multi-agent system. In the case of embedding cooperative multi-agent learning technology, sensor nodes with group observability work in a distributed manner. These nodes cooperated and exploit local feedback information to automatically learn the optimal scheduling strategy, then select the best time slot and channel for wireless communication. Simulation results show that the new scheduling method has advantages in performance when comparing with the existing methods.https://www.mdpi.com/2079-9292/11/20/3356many-to-many data aggregation schedulingmulti-channel WSNdecentralized partially observable Markov decisionmulti-agent learning |
| spellingShingle | Yao Lu Keweiqi Wang Erbao He Many-to-Many Data Aggregation Scheduling Based on Multi-Agent Learning for Multi-Channel WSN Electronics many-to-many data aggregation scheduling multi-channel WSN decentralized partially observable Markov decision multi-agent learning |
| title | Many-to-Many Data Aggregation Scheduling Based on Multi-Agent Learning for Multi-Channel WSN |
| title_full | Many-to-Many Data Aggregation Scheduling Based on Multi-Agent Learning for Multi-Channel WSN |
| title_fullStr | Many-to-Many Data Aggregation Scheduling Based on Multi-Agent Learning for Multi-Channel WSN |
| title_full_unstemmed | Many-to-Many Data Aggregation Scheduling Based on Multi-Agent Learning for Multi-Channel WSN |
| title_short | Many-to-Many Data Aggregation Scheduling Based on Multi-Agent Learning for Multi-Channel WSN |
| title_sort | many to many data aggregation scheduling based on multi agent learning for multi channel wsn |
| topic | many-to-many data aggregation scheduling multi-channel WSN decentralized partially observable Markov decision multi-agent learning |
| url | https://www.mdpi.com/2079-9292/11/20/3356 |
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