Developing a Novel Adaptive Double Deep Q-Learning-Based Routing Strategy for IoT-Based Wireless Sensor Network with Federated Learning DOI Creative Commons

Nalini Manogaran,

Mercy Theresa Michael Raphael,

Rajalakshmi Raja

и другие.

Sensors, Год журнала: 2025, Номер 25(10), С. 3084 - 3084

Опубликована: Май 13, 2025

The working of the Internet Things (IoT) ecosystem indeed depends extensively on mechanisms 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 transmit environmental wirelessly. Because WSNs possess advantages remote they severely hampered by constraints imposed limited energy capacity SNs; hence, energy-efficient routing is pertinent challenge. Therefore, in case clustering mechanisms, two play roles where performed to reduce consumption prolong lifetime network, while refers actual paths transmission data. Addressing limitations witnessed conventional IoT-based data, this proposal presents an FL-oriented framework that new scheme. Such facilitated ADDQL model, creates smart high-speed across changing scenarios WSNs. proposed ADDQL-IRHO model has been compared other existing state-of-the-art algorithms according multiple performance metrics such as consumption, communication delay, temporal complexity, sum rate, message overhead, scalability, extensive experimental evaluation reporting superior performance. This also substantiates applicability competitiveness variable-serviced IoT-oriented next-gen intelligent solutions.

Язык: Английский

Developing a Novel Adaptive Double Deep Q-Learning-Based Routing Strategy for IoT-Based Wireless Sensor Network with Federated Learning DOI Creative Commons

Nalini Manogaran,

Mercy Theresa Michael Raphael,

Rajalakshmi Raja

и другие.

Sensors, Год журнала: 2025, Номер 25(10), С. 3084 - 3084

Опубликована: Май 13, 2025

The working of the Internet Things (IoT) ecosystem indeed depends extensively on mechanisms 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 transmit environmental wirelessly. Because WSNs possess advantages remote they severely hampered by constraints imposed limited energy capacity SNs; hence, energy-efficient routing is pertinent challenge. Therefore, in case clustering mechanisms, two play roles where performed to reduce consumption prolong lifetime network, while refers actual paths transmission data. Addressing limitations witnessed conventional IoT-based data, this proposal presents an FL-oriented framework that new scheme. Such facilitated ADDQL model, creates smart high-speed across changing scenarios WSNs. proposed ADDQL-IRHO model has been compared other existing state-of-the-art algorithms according multiple performance metrics such as consumption, communication delay, temporal complexity, sum rate, message overhead, scalability, extensive experimental evaluation reporting superior performance. This also substantiates applicability competitiveness variable-serviced IoT-oriented next-gen intelligent solutions.

Язык: Английский

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