Adaptive Intelligence Techniques Integrated Robust and Efficient Wireless Sensor Networks

Abstract

Wireless Sensor Networks (WSNs) are crucial for environmental monitoring and industrial automation, but face challenges like crowded transmissions, ad hoc deployment, unattended operation, and limited resources. The Dynamic Packet Congestion Control with Reinforcement Learning (DPCC-RL) and Adaptive Topology Management with Swarm Intelligence (ATM-SI) are proposed that can be integrated to improve network performance in Wireless Sensor Networks (WSNs). DPCC-RL uses reinforcement learning to address crowded packet transmissions, while ATM-SI addresses ad hoc sensor node deployment challenges by optimizing network topology. SAEM-RS, on the other hand, focuses on unattended operation, where sensor nodes must operate autonomously for extended periods. It adjusts sleep-wake schedules based on reinforcement learning decisions and swarm intelligence feedback, optimizing energy consumption during data transmission and ensuring nodes are awake when necessary. These techniques collectively improve network reliability, adaptability, and longevity, making them a robust solution for enhancing WSN performance in complex real-world scenarios. The findings demonstrate that, in comparison to previous techniques, the suggested approach achieved high levels of accuracy, precision, recall, and F1-Score.