Energy-Aware Cross-Layer Routing Using Transformer Models in Wireless Sensor Networks

Recently, wireless communication networks have played a vital role in environmental monitoring and other data-driven applications. Even though these networks often struggle with limited energy and redundant data transmissions. Moreover, traditional routing protocols, such as the Cross-layer Opportunistic Routing Protocol (CORP), rely heavily on static routing decisions with fixed-cost functions, leading to a lack of adaptability. To address these issues, this study proposes a Mistral 7B-based Cross-layer Optimization (M7BCO), which integrates adaptive reasoning and prompt-based telemetry compression for energy-aware decisions. The proposed M7BCO model utilizes a Partially Informed Sparse Autoencoder (PISA) to select a minimal subset of informative nodes by learning spatial correlations while preserving data reconstructability. Then, the proposed M7BCO model generates a real-time decision for next-hop selection and transmits power adjustment as it replaces the static optimization with adaptive reasoning. Unlike pure sequential models, the proposed model introduced a lightweight training loop between PISA telemetry selection and Mistral 7B adaptive reasoning. From the results, the proposed M7BCO model achieved better results when compared to the existing CORP model in terms of Energy Efficiency (EE) of 22.5, 65.3, and 100.2 mJ for 150, 300, and 500 nodes respectively.