Attention‐Driven Optimizer‐Based Routing Framework for VLSI‐Enabled Wireless Sensor Networks

The wireless sensor networks (WSNs) that exist in large and dynamic systems demand routing systems that are able to make real‐time decisions with stringent hardware limitations. Although several deep learning (DL)–based routing algorithms have been proposed, their high computational complexity and software‐based inference limit their deployment in low‐power FPGA‐based WSNs. To overcome these challenges, a hybrid multistage routing and prediction framework integrating Ad hoc On‐Demand Distance Vector–Multipoint Relay (AODV‐MPR) routing, Improved Chaotic Brownian Kite Optimizer (ICBKO), and AtSCS‐Net based on Chaotic Hybrid Crab‐Starfish Optimization (CHCSO) is proposed. A minimum redundancy maximum relevance (MRMR) feature selection is used to approximate the most informative routing parameters, which results in reducing computational complexity. Experimental results demonstrate the effectiveness of the proposed framework, achieving a 99.76% success rate, 98.95% positive predictive value (PPV), 0.10 mean square error (MSE), and 0.5 mean absolute error (MAE), thereby outperforming existing methods in routing accuracy and error reduction.