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<p>Energy minimization in cognitive radio networks leads to improved network performance by extending the battery life of devices, allowing them to operate efficiently for longer periods. It also reduces the thermal impact on hardware, minimizing the risk of overheating and potential damage. Sensor nodes play a crucial role in cognitive radio networks by monitoring the spectrum environment for available frequencies. They collect and analyze data on spectrum usage patterns, enabling the network to dynamically adjust frequencies to avoid interference. This helps in optimizing the overall network performance and ensuring efficient spectrum utilization. As a result of the random selection of cluster head nodes in a loop in the LEACH routing protocol, the network’s energy consumption is equalized, causing the network to operate unstable. Using the proposed algorithm, cognitive radio networks can minimize energy consumption by integrating the LEACH protocol. In an effort to minimize energy consumption, NEAQTCPAL has been developed based on Energy Aware QTCP and Adaptive Learning (NEAQTCPAL). A minimal amount of energy is used in the network with the present proposed enhanced algorithm. In comparison with Leach C protocol, an approximate improvement of 15–20% was obtained with the proposed protocol.</p>

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Energy aware adaptive learning algorithm with extended LEACH protocol to minimize energy in cognitive radio networks

  • 期刊论文

  • 发表日期：

    2026年3月16日

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  • Mettu. Jhansi Lakshmi,
  • Mahesh Babu Arrama

摘要

Energy minimization in cognitive radio networks leads to improved network performance by extending the battery life of devices, allowing them to operate efficiently for longer periods. It also reduces the thermal impact on hardware, minimizing the risk of overheating and potential damage. Sensor nodes play a crucial role in cognitive radio networks by monitoring the spectrum environment for available frequencies. They collect and analyze data on spectrum usage patterns, enabling the network to dynamically adjust frequencies to avoid interference. This helps in optimizing the overall network performance and ensuring efficient spectrum utilization. As a result of the random selection of cluster head nodes in a loop in the LEACH routing protocol, the network’s energy consumption is equalized, causing the network to operate unstable. Using the proposed algorithm, cognitive radio networks can minimize energy consumption by integrating the LEACH protocol. In an effort to minimize energy consumption, NEAQTCPAL has been developed based on Energy Aware QTCP and Adaptive Learning (NEAQTCPAL). A minimal amount of energy is used in the network with the present proposed enhanced algorithm. In comparison with Leach C protocol, an approximate improvement of 15–20% was obtained with the proposed protocol.