Geometric Optimization Algorithm for Path Loss Model of Riparian Zone IoT Networks Based on Federated Learning Framework

• Yu Geng,
• Tiecheng Song,
• Qiang Wang,
• Xiaoqin Song,

Vol. 18, No. 7, July 31, 2024
10.3837/tiis.2024.07.004, Download Paper (Free):

• 3-D geometric factors
• antennas coupling factors
• field IoT
• path loss prediction
• propagation model.

Abstract

In the field of environmental sensing, it is necessary to develop radio planning techniques for the next generation Internet of Things (IoT) networks over mixed terrains. Such techniques are needed for smart remote monitoring of utility supplies, with links situated close to but out of range of cellular networks. In this paper, a three-dimension (3-D) geometric optimization algorithm is proposed, considering the positions of edge IoT devices and antenna coupling factors. Firstly, a multi-level single linkage (MLSL) iteration method, based on geometric objectives, is derived to evaluate the data rates over ISM 915 MHz channels, utilizing optimized power-distance profiles of continuous waves. Subsequently, a federated learning (FL) data selection algorithm is designed based on the 3-D geometric positions. Finally, a measurement example is taken in a meadow biome of the Mexican Colima district, which is prone to fluvial floods. The empirical path loss model has been enhanced, demonstrating the accuracy of the proposed optimization algorithm as well as the possibility of further prediction work.

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