Energy-aware intelligent routing framework for 6G-based wireless sensor networks

In the context of 6G technology, wireless sensor networks can leverage the advanced features and capabilities of 6G networks, including ultra-fast data speeds, ultra-low latency, massive device connectivity and enhanced energy efficiency. However, developing and implementing 6G networks presents several challenges, particularly in ensuring energy efficiency, which is essential to minimize environmental impact and operational costs. This work addresses the critical issue of energy efficiency by formulating the energy-efficient routing problem and mapping it to a Q-learning technique. An intelligent Q-learning-based framework is proposed to efficiently determine energy-efficient routing paths between sensor nodes and the sink. The different states and transitions are defined using a Markov model. Three separate algorithms are presented as part of the proposed routing framework in 6G-based WSNs. The performance of the proposed framework is compared against clustering-based and particle swarm optimization-based algorithms in terms of energy consumption. Simulation results demonstrate that the proposed routing framework significantly prolongs the overall network lifetime and substantially improves transmission efficiency compared to existing techniques.