Since heterogeneous wireless sensor networks consist of sensor nodes of varying capacity and energy-constrained, effective routing techniques are essential to ensure the proper functioning of the systems. Most traditional routing techniques fail to dynamically adjust to varying network conditions, leading to ineffective use of energy and poor performance. Therefore, deep Q-Networks implementation of reinforcement learning provides a beneficial approach to the problem due to adaptive routing decisions depending on the environmental signals and systems’ performance. Therefore, the suggested approach integrates Deep Q-Network into the data routing framework for different Wireless Sensor Networks to improve energy-efficiency and ensure data delivery. The DQN agent is designed to pick routing functions that maximize total rewards which depend on energy consumption, packet delivery, and network stability. Hence, the decentralized learning allows each sensor node to develop its routing policy based on the local environment under the interactions with their neighbors. Therefore, the approach promotes the ability to adapt and learn, crucial for changing network conditions. Thus, extensive simulation was conducted to assess the applicability of the DQN-based routing for different WSNs, proving the significant reducing of energy consumption compared to traditional routing approaches with an average of 25% regardless of the network formation and traffic conditions . This approach also demonstrates lower packet loss of 15%, revealing enhanced data transfer reliability . In particular, the existing on demand routing protocols, only forward the request that arrives first from each route discovery process. The attacker exploits this property of the operation of route discovery. The network lifetime was extended by 30% showing growing energy efficiency for a long-term run. In general, the integration of Deep Q-Networks into data routing provides an excellent opportunity to improve energy-efficiency in different types of wireless sensor networks. Hence, the proposed approach effectively optimizes the routing solutions in real-time, using adaptive lenience, also showing enhancing data delivery, and improving the systems’ lifetime. Hence, the presented results prove the capability of reinforcement learning methods to address the growing challenges of WSNs and leave space for further research in autonomous WSN improvement.
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Bhargavi Gaurav Deshpande
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Zeeshan Ahmad Lone
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link
https://doi.org/10.54216/JCIM.140111