[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83503-en":3,"doc-seo-83503-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},83503,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Robust Base Station Placement in Agricultural IoT via Bayesian Optimization","Precision-agriculture private 5G NR networks must maintain reliable connectivity for agricultural IoT sensors across the entire crop growing season, while the radio environment varies as vegetation develops. The work formulates K-base-station placement as a maximin seasonal coverage problem, maximizing the worst-case coverage fraction over multiple growth stages. Because each objective evaluation needs expensive ray-tracing simulations across stages, the approach uses Gaussian-process Bayesian optimization to build a probabilistic surrogate of the robust objective. On a 1 km² multi-crop farm at 3.5 GHz, it attains 72.8% worst-case coverage with K=3 using under fifty ray-tracing evaluations, outperforming budget-matched baselines by at least 4.6 percentage points across four stages.","Robust Base Station Placement in Agricultural IoT  \nvia Bayesian Optimization  \nGourav Prateek Sharma∗ , Durgesh Singh†, James Gross‡  \n∗ Dept. of ECE, National Institute of Technology Kurukshetra, India, [gourav.sharma@nitkkr.ac.in](gourav.sharma@nitkkr.ac.in)[ ](gourav.sharma@nitkkr.ac.in)†Dept. of ECE, Thapar Institute of Engineering & Technology, India, [durgesh.singh@thapar.edu](durgesh.singh@thapar.edu)[ ](durgesh.singh@thapar.edu)‡School of EECS, KTH Royal Institute of Technology, Sweden, [jamesgr@kth.se](jamesgr@kth.se)  \narXiv :2607 .00549v 1 [ cs .NI] 1 Jul 2026  \nAbstract—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 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 1km2 multi-crop farm with three distinct crop zones at 3.5GHz, the proposed scheme achieves 72.8% worst-case coverage with K=3 BSs in fewer than fifty raytracing evaluations, outperforming budget-matched state-of-theart approaches by at least 4.6pp across all four seasonal stages.  \nIndex Terms—5G, agricultural IoT, Bayesian optimization, ray tracing, Sionna  \nI. INTRODUCTION  \nPrecision agriculture relies on a wide range of IoT sensors to monitor parameters such as soil moisture, crop health, and irrigation flow. These sensors are typically deployed in dense and irregular patterns across farmland, alongside mobile agricultural machinery that also requires connectivity. While sub-GHz LPWAN technologies such as LoRa and NB-IoT currently dominate low-data-rate agricultural sensing, emerging use cases including autonomous machinery telemetry and drone-assisted crop scouting demand higher throughput and lower latency, motivating the adoption of private 5G networks for precision agriculture [1] . Ensuring reliable communication for all devices within the farm boundary is therefore essential [2] . However, deploying communication infrastructure in rural areas is often limited by cost, necessitating the use of only a few base stations (BSs) to cover large agricultural regions [3] . A major challenge in such environments is the dynamic nature of the radio propagation channel, which changes significantly over the crop growth cycle [4] . As vegetation develops, factors such as canopy height, water content, and leaf density increase, altering the electromagnetic characteristics of the environment [5] . These variations lead to changes in signal attenuation and scattering, causing coverage patterns to evolve throughout the season. Consequently, a BS placement strategy that is effective early in the season may suffer considerable performance degradation as crops mature. Designing BS deployments that remain robust under these seasonal variations is therefore a critical challenge for agricultural IoT networks.  \nBase station placement has been widely studied in wireless network planning [6] . Classical approaches rely on simplified propagation models and search strategies such as metaheuristics based on Particle Swarm Optimization (PSO) and genetic algorithms [7], [8] . More recently, Bayesian optimization (BO) has emerged as an effective technique for optimizing expensive black-box objectives such as network coverage or capacity. BO has been applied to problems including wireless network planning, antenna configuration and transmitter placement in complex propagation environments [9], [10] . In parallel, modern ray-tracing tools (e.g., Sionna RT [11]) enable physically accurate modelling of radio propagation in detailed environments,","cbCaicZy8gYgZbEX","https://ap.wps.com/l/cbCaicZy8gYgZbEX","pdf",2085423,1,5,"English","en",105,"# Introduction\n## Motivation for private 5G in precision agriculture\n## Robustness challenge from seasonal propagation variation\n## Related work and gaps\n# Methodology\n## K-base-station maximin seasonal coverage formulation\n## Gaussian-process Bayesian optimization with ray tracing\n# Results\n## 1 km² multi-crop farm evaluation and worst-case coverage","[{\"question\":\"What problem does the paper address in agricultural IoT base-station placement?\",\"answer\":\"It targets seasonal-robust placement, where radio propagation changes as crops grow, so a single snapshot-optimized deployment can degrade later in the season.\"},{\"question\":\"How is the optimization objective defined?\",\"answer\":\"Base-station placement is formulated as a maximin seasonal coverage problem that maximizes the worst-case coverage fraction across all crop growth stages.\"},{\"question\":\"Why does the method use Gaussian-process Bayesian optimization?\",\"answer\":\"Each evaluation requires costly ray-tracing simulations over multiple stages, so GP Bayesian optimization uses a probabilistic surrogate to search the placement space efficiently with limited 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problem does the paper address in agricultural IoT base-station placement?","Question",{"text":75,"@type":76},"It targets seasonal-robust placement, where radio propagation changes as crops grow, so a single snapshot-optimized deployment can degrade later in the season.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the optimization objective defined?",{"text":80,"@type":76},"Base-station placement is formulated as a maximin seasonal coverage problem that maximizes the worst-case coverage fraction across all crop growth stages.",{"name":82,"@type":73,"acceptedAnswer":83},"Why does the method use Gaussian-process Bayesian optimization?",{"text":84,"@type":76},"Each evaluation requires costly ray-tracing simulations over multiple stages, so GP Bayesian optimization uses a probabilistic surrogate to search the placement space efficiently with limited 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