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<p>Wireless Sensor Networks (WSNs) require accurate channel modeling to ensure reliability and effectiveness in complex outdoor environments, such as open fields and gardens. This study investigates the performance of WSNs through Received Signal Strength Indicator (RSSI) analysis over varying distances. Utilizing Crystal Formation Optimization (CFO), polynomial and exponential functions were curve-fitted to model wireless channel propagation. The polynomial function with CFO (PCFO) achieved an R<sup>2</sup> value of 0.9956, with a Mean Absolute Error (MAE) of 0.6068 and a Root Mean Square Error (RMSE) of 0.8633. The exponential function with CFO (ECFO) resulted in an R<sup>2</sup> value of 0.9631, with an MAE of 1.3985 and an RMSE of 1.3909 and these results were compared with the well-known Particle Swarm Optimization technique for the same test bed. The significant reduction in error metrics demonstrates the effectiveness of CFO in modeling wireless channel propagation. Convergence analysis supports the reliability of the optimization process. These findings underscore the importance of advanced channel modeling techniques for enhancing WSN performance in outdoor environments.</p>

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CFO-Driven RSSI Analysis for Enhanced Channel Propagation in Outdoor Wireless LoRaWAN Networks

  • 期刊论文

  • 发表日期：

    2026年4月6日

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  • Rohit Shekdar,
  • Vijay Rayar,
  • Prabhakar Manage

摘要

Wireless Sensor Networks (WSNs) require accurate channel modeling to ensure reliability and effectiveness in complex outdoor environments, such as open fields and gardens. This study investigates the performance of WSNs through Received Signal Strength Indicator (RSSI) analysis over varying distances. Utilizing Crystal Formation Optimization (CFO), polynomial and exponential functions were curve-fitted to model wireless channel propagation. The polynomial function with CFO (PCFO) achieved an R2 value of 0.9956, with a Mean Absolute Error (MAE) of 0.6068 and a Root Mean Square Error (RMSE) of 0.8633. The exponential function with CFO (ECFO) resulted in an R2 value of 0.9631, with an MAE of 1.3985 and an RMSE of 1.3909 and these results were compared with the well-known Particle Swarm Optimization technique for the same test bed. The significant reduction in error metrics demonstrates the effectiveness of CFO in modeling wireless channel propagation. Convergence analysis supports the reliability of the optimization process. These findings underscore the importance of advanced channel modeling techniques for enhancing WSN performance in outdoor environments.