Developing a Novel Adaptive Double Deep Q-Learning-Based Routing Strategy for IoT-Based Wireless Sensor Network with Federated Learning
The working of the Internet of Things (IoT) ecosystem indeed depends extensively on the mechanisms of real-time data collection, sharing, and automatic operation. Among these fundamentals, wireless sensor networks (WSNs) are important for maintaining a countenance with their many distributed Sensor...
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2025-05-01
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| author | Nalini Manogaran Mercy Theresa Michael Raphael Rajalakshmi Raja Aarav Kannan Jayakumar Malarvizhi Nandagopal Balamurugan Balusamy George Ghinea |
| author_facet | Nalini Manogaran Mercy Theresa Michael Raphael Rajalakshmi Raja Aarav Kannan Jayakumar Malarvizhi Nandagopal Balamurugan Balusamy George Ghinea |
| author_sort | Nalini Manogaran |
| collection | DOAJ |
| description | The working of the Internet of Things (IoT) ecosystem indeed depends extensively on the mechanisms of real-time data collection, sharing, and automatic operation. Among these fundamentals, wireless sensor networks (WSNs) are important for maintaining a countenance with their many distributed Sensor Nodes (SNs), which can sense and transmit environmental data wirelessly. Because WSNs possess advantages for remote data collection, they are severely hampered by constraints imposed by the limited energy capacity of SNs; hence, energy-efficient routing is a pertinent challenge. Therefore, in the case of clustering and routing mechanisms, these two play important roles where clustering is performed to reduce energy consumption and prolong the lifetime of the network, while routing refers to the actual paths for transmission of data. Addressing the limitations witnessed in the conventional IoT-based routing of data, this proposal presents an FL-oriented framework that presents a new energy-efficient routing scheme. Such routing is facilitated by the ADDQL model, which creates smart high-speed routing across changing scenarios in WSNs. The proposed ADDQL-IRHO model has been compared to other existing state-of-the-art algorithms according to multiple performance metrics such as energy consumption, communication delay, temporal complexity, data sum rate, message overhead, and scalability, with extensive experimental evaluation reporting superior performance. This also substantiates the applicability and competitiveness of the framework in variable-serviced IoT-oriented WSNs for next-gen intelligent routing solutions. |
| format | Article |
| id | doaj-art-cfbb16c54e4e48daafc06d4b6883a698 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-cfbb16c54e4e48daafc06d4b6883a6982025-08-20T02:33:48ZengMDPI AGSensors1424-82202025-05-012510308410.3390/s25103084Developing a Novel Adaptive Double Deep Q-Learning-Based Routing Strategy for IoT-Based Wireless Sensor Network with Federated LearningNalini Manogaran0Mercy Theresa Michael Raphael1Rajalakshmi Raja2Aarav Kannan Jayakumar3Malarvizhi Nandagopal4Balamurugan Balusamy5George Ghinea6Department of Computer Science and Business Systems, S.A. Engineering College (Autonomous), Chennai 600077, Tamil Nadu, IndiaDepartment of Data Science and Business Systems, School of Computing, College of Engineering and Technology, SRM Institute of Science and Technology, Chennai 603203, Tamil Nadu, IndiaDepartment of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai 600119, Tamil Nadu, IndiaDepartment of BioMedical Engineering, SRM Institute of Science and Technology, Chennai 600089, Tamil Nadu, IndiaDepartment of Computer Science and Engineering, School of Computing, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai 600062, Tamil Nadu, IndiaAssociate Dean Academics, Office of Dean of Academics, Shiv Nadar University, Delhi-National Capital Region (NCR), Dadri 201314, Uttar Pradesh, IndiaDepartment of Computer Science, College of Engineering Design and Physical Sciences, Brunel University London, Uxbridge UB8 3PH, UKThe working of the Internet of Things (IoT) ecosystem indeed depends extensively on the mechanisms of real-time data collection, sharing, and automatic operation. Among these fundamentals, wireless sensor networks (WSNs) are important for maintaining a countenance with their many distributed Sensor Nodes (SNs), which can sense and transmit environmental data wirelessly. Because WSNs possess advantages for remote data collection, they are severely hampered by constraints imposed by the limited energy capacity of SNs; hence, energy-efficient routing is a pertinent challenge. Therefore, in the case of clustering and routing mechanisms, these two play important roles where clustering is performed to reduce energy consumption and prolong the lifetime of the network, while routing refers to the actual paths for transmission of data. Addressing the limitations witnessed in the conventional IoT-based routing of data, this proposal presents an FL-oriented framework that presents a new energy-efficient routing scheme. Such routing is facilitated by the ADDQL model, which creates smart high-speed routing across changing scenarios in WSNs. The proposed ADDQL-IRHO model has been compared to other existing state-of-the-art algorithms according to multiple performance metrics such as energy consumption, communication delay, temporal complexity, data sum rate, message overhead, and scalability, with extensive experimental evaluation reporting superior performance. This also substantiates the applicability and competitiveness of the framework in variable-serviced IoT-oriented WSNs for next-gen intelligent routing solutions.https://www.mdpi.com/1424-8220/25/10/3084Internet of Things (IoT)wireless sensor network (WSN)smart data routingfederated learningdeep earning |
| spellingShingle | Nalini Manogaran Mercy Theresa Michael Raphael Rajalakshmi Raja Aarav Kannan Jayakumar Malarvizhi Nandagopal Balamurugan Balusamy George Ghinea Developing a Novel Adaptive Double Deep Q-Learning-Based Routing Strategy for IoT-Based Wireless Sensor Network with Federated Learning Sensors Internet of Things (IoT) wireless sensor network (WSN) smart data routing federated learning deep earning |
| title | Developing a Novel Adaptive Double Deep Q-Learning-Based Routing Strategy for IoT-Based Wireless Sensor Network with Federated Learning |
| title_full | Developing a Novel Adaptive Double Deep Q-Learning-Based Routing Strategy for IoT-Based Wireless Sensor Network with Federated Learning |
| title_fullStr | Developing a Novel Adaptive Double Deep Q-Learning-Based Routing Strategy for IoT-Based Wireless Sensor Network with Federated Learning |
| title_full_unstemmed | Developing a Novel Adaptive Double Deep Q-Learning-Based Routing Strategy for IoT-Based Wireless Sensor Network with Federated Learning |
| title_short | Developing a Novel Adaptive Double Deep Q-Learning-Based Routing Strategy for IoT-Based Wireless Sensor Network with Federated Learning |
| title_sort | developing a novel adaptive double deep q learning based routing strategy for iot based wireless sensor network with federated learning |
| topic | Internet of Things (IoT) wireless sensor network (WSN) smart data routing federated learning deep earning |
| url | https://www.mdpi.com/1424-8220/25/10/3084 |
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