Energy efficient and robust node localization in WSNs using LSTM optimized DV hop framework to mitigate multihop localization errors.

Wireless Sensor Networks (WSNs) are distributed sensor nodes that sense data from their surroundings and relay it to a central network for processing and analysis. Sensor localization is a crucial technique in WSNs, enabling precise positions of target nodes based on environmental signal perception. However, achieving high accuracy in node localization remains a challenge. This study introduces an improved DV-Hop positioning algorithm that integrates Long Short-Term Memory (OLSTM-DVHop) networks to enhance node position predictions. The algorithm processes original data through filtering, analysis, and feature extraction to improve predicted node positions. The study analyzed errors using a standard DV-Hop algorithm and designed a robust architecture for WSN positioning. Simulation experiments validated the proposed improvements, aligning with the algorithm's accuracy requirements. The proposed error correction mechanism addresses uneven error distribution in the DV-Hop algorithm, adjusting the positions of nodes with significant deviations, reducing errors, and enhancing the positioning process's reliability and accuracy. The effectiveness of the proposed algorithm is evaluated by comparing it with other localization algorithms across different terrain types. The improved DV-Hop algorithm significantly reduces localization errors and offers superior accuracy, outperforming other algorithms in various experimental scenarios.