Intelligent Energy-Efficient Routing in Wireless

To promote data transmission and prolong the life of Wireless Sensor Networks (WSN), there is a need for energy-efficient routing protocols. In this paper, Adaptive Gradient Search Optimization (AGSO) is combined with Reinforcement Learning (RL) and Ant Colony Optimization (ACO) to support efficient routing under energy considerations. The Swarm-Based Dynamic Clustering (SBDC) proposal dynamically clusters sensor nodes in an adaptive manner, optimizing cluster establishment. Cluster Head (CH) selection is done based on AGSO, where rather than all CHs sending data to the Base Station (BS), a chosen CH sends data, saving energy and enhancing transmission efficiency For energy-efficient routing, Reinforcement Learning (RL) learns routing decisions based on network conditions and ACO optimizes multi-paths to increase reliability and optimize energy consumption. The proposed framework efficiently reduces energy depletion, minimizes communication overhead, and extends network lifetime. Simulation outcomes reveal that the suggested technique greatly enhances packet delivery ratio, network throughput, energy efficiency, and decreases end-to-end delay with respect to conventional methods like LEACH, HEED, and PSO. In addition, the system guarantees better dependability, adaptability, and global efficiency, which makes it a suitable solution for future WSN applications. For energy-efficient routing, Reinforcement Learning (RL) learns intelligent routing decisions depending on network states, whereas ACO conducts multi-path optimization for improving reliability and energy consumption balancing. The designed framework efficiently reduces energy depletion, minimizes communication overhead, and extends network lifetime. Simulation outcomes prove that the new approach significantly enhances packet delivery ratio, (achieving over 98.4%) network throughput, energy efficiency, and minimizes end-to-end delay in comparison to conventional methods like LEACH, HEED, and PSO. Additionally, the system provides enhanced dependability, flexibility, and overall efficiency, rendering it a good candidate for future WSN applications.