Robust Base Station Placement in Agricultural IoT via Bayesian Optimization
For agricultural network planners, it provides a sample-efficient method to ensure reliable connectivity across crop growth stages.
The paper tackles robust base station placement in agricultural IoT under changing seasonal propagation, achieving 72.8% worst-case coverage with 3 base stations in under 50 ray-tracing evaluations, outperforming baselines by at least 4.6 percentage points.
Precision-agriculture networks based on private 5G NR should ensure reliable connectivity for IoT sensor nodes throughout the crop growing season, yet the propagation environment changes dramatically as vegetation grows and matures. We formulate $K$-base-station~(BS) placement as a \textit{maximin seasonal coverage} problem that maximizes the worst-case coverage fraction across all crop growth stages. Since each objective evaluation requires expensive ray-tracing simulations across all stages, we adopt a Gaussian-process Bayesian optimization~(GPBO) framework that builds a probabilistic surrogate of the robust objective using ray tracing. On a $1\,\text{km}^2$ multi-crop farm with three distinct crop zones at $3.5\,\text{GHz}$, the proposed scheme achieves $72.8\%$ worst-case coverage with $K{=}3$ BSs in fewer than fifty ray-tracing evaluations, outperforming budget-matched state-of-the-art approaches by at least $4.6\,\text{pp}$ across all four seasonal stages.