Energy Aware Archery Northern Goshawk Optimization‐Based Cluster Head Selection and Gt‐LSTM for Weather Forecasting in WSN‐IoT Network

Abstract

ABSTRACT The distributed and resource‐constrained nature of wireless sensor network–internet of things (WSN‐IoT) nodes poses significant challenges for weather forecasting. Existing methods often struggle to balance prediction accuracy, energy efficiency, and network scalability. An efficient approach is presented in this research to resolve these issues through weather forecasting and cluster head (CH) selection. The selection of CHs is carried out using the Energy‐Aware Archery Northern Goshawk Optimization (ANGO), a nature‐inspired technique that replicates the predatory behavior of the Northern Goshawk. ANGO efficiently handles mobile nodes, maintains energy balance, and improves network longevity. A hybrid Gt‐LSTM model, incorporating gated recurrent unit (GRU) and long short‐term memory (LSTM) architectures, is employed at the WSN‐IoT server for weather forecasting, allowing the extraction of both short‐term fluctuations and long‐term weather trends. Using ANGO for CH selection achieves a remaining energy of 58.855 J, maintains 89% of alive nodes, reaches a throughput of 47.869 Mbps, and results in an overall energy consumption of 55.569 J. Additionally, the Gt‐LSTM technique for weather forecasting achieves a mean square error (MSE), root mean square error (RMSE), mean absolute error (MAPE), and accuracy of 0.219, 0.468, 0.328, 92.812%, respectively.