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Multi-agent based DRL with federated learning for data transmission in mobile sensor networks.

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Title: Multi-agent based DRL with federated learning for data transmission in mobile sensor networks.
Authors: Senthamilselvi, M.1 (AUTHOR) senthamilselvi409@gmail.com, Ranjeeth Kumar, C.2 (AUTHOR)
Source: Automatika: Journal for Control, Measurement, Electronics, Computing & Communications. Aug2025, Vol. 66 Issue 3, p475-490. 16p.
Subjects: Artificial neural networks, Deep reinforcement learning, Federated learning, Sensor networks, Energy consumption
Abstract: There has been a flurry of activity in the field of wireless sensor networks, or WSNs, as of late. Because packets need to be transported from source nodes to the destination nodes as quickly and energy effectively as feasible in various application areas, packet routing is one of core difficulties in WSNs. A plethora of routing options have been suggested to tackle this problem. The proposed method distributed and designed to run on a network of interconnected routers. Different from most of its competitors, the proposed results frame the routing problem as a reinforcement learning problem with several agents. To optimize more complicated cost functions, such as the time it takes for bags to be delivered and the amount of energy used in a baggage handling system, it is possible to model every router as a deep neural network. The proposed MA-DRL attains latency of 2.41, energy consumption of 26J has superior efficiency compared to the existing methods. However, the MA-DRL has minimized latency and lower energy consumption. This way, each router may take into consideration different types of data about its surroundings. Based on four metrics latency, and energy consumption the simulation results show that this architecture performs well. [ABSTRACT FROM AUTHOR]
Copyright of Automatika: Journal for Control, Measurement, Electronics, Computing & Communications is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Multi-agent based DRL with federated learning for data transmission in mobile sensor networks.
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  Data: <searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+reinforcement+learning%22">Deep reinforcement learning</searchLink><br /><searchLink fieldCode="DE" term="%22Federated+learning%22">Federated learning</searchLink><br /><searchLink fieldCode="DE" term="%22Sensor+networks%22">Sensor networks</searchLink><br /><searchLink fieldCode="DE" term="%22Energy+consumption%22">Energy consumption</searchLink>
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  Data: There has been a flurry of activity in the field of wireless sensor networks, or WSNs, as of late. Because packets need to be transported from source nodes to the destination nodes as quickly and energy effectively as feasible in various application areas, packet routing is one of core difficulties in WSNs. A plethora of routing options have been suggested to tackle this problem. The proposed method distributed and designed to run on a network of interconnected routers. Different from most of its competitors, the proposed results frame the routing problem as a reinforcement learning problem with several agents. To optimize more complicated cost functions, such as the time it takes for bags to be delivered and the amount of energy used in a baggage handling system, it is possible to model every router as a deep neural network. The proposed MA-DRL attains latency of 2.41, energy consumption of 26J has superior efficiency compared to the existing methods. However, the MA-DRL has minimized latency and lower energy consumption. This way, each router may take into consideration different types of data about its surroundings. Based on four metrics latency, and energy consumption the simulation results show that this architecture performs well. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Automatika: Journal for Control, Measurement, Electronics, Computing & Communications is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1080/00051144.2025.2496539
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 16
        StartPage: 475
    Subjects:
      – SubjectFull: Artificial neural networks
        Type: general
      – SubjectFull: Deep reinforcement learning
        Type: general
      – SubjectFull: Federated learning
        Type: general
      – SubjectFull: Sensor networks
        Type: general
      – SubjectFull: Energy consumption
        Type: general
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      – TitleFull: Multi-agent based DRL with federated learning for data transmission in mobile sensor networks.
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              M: 08
              Text: Aug2025
              Type: published
              Y: 2025
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