Optimizing optical network longevity via Q-learning-based routing protocol for energy efficiency and throughput enhancement

In optical networks, increasing longevity is of critical importance. This article describes a cutting-edge routing protocol based on Q-learning techniques that have been meticulously constructed to extend the lifetime of optical networks by enhancing energy effectiveness and throughput. The protocol dynamically manages energy usage using Q-learning, a reinforcement learning approach. The primary objective is to choose routing algorithms that optimize long-term revenues for individual nodes while increasing energy efficiency. In a detailed study, the protocol's performance is compared to that of well-known rivals such as Low-Energy Adaptive Clustering Hierarchy (LEACH), Multi-Hop Low-Energy Adaptive Clustering Hierarchy (M-LEACH), and Balanced Low-Energy Adaptive Clustering Hierarchy (B-LEACH) (B-LEACH). The evaluation considers several factors, including network durability as measured by active/inactive node ratios, energy efficiency as measured by per-round energy consumption, quality of service as measured by throughput per round, and scalability as measured over networks with 40, 70, and 100 nodes. The complete examination for each network configuration spans over 5,000 cycles. M-LEACH outperforms LEACH and B-LEACH in all performance measures in the simulation results test, establishing a new benchmark. It is fascinating to compare the performance of the unique Q-learning-based protocol to that of LEACH, M-LEACH, and B-LEACH. Regarding network durability, energy efficiency, quality of service, and scalability, the proposed protocol outperforms.