Model-Driven Wireless Planning for Farm Monitoring: A Mixed-Integer Optimization Approach

This study presents an optimization-driven design of a wireless communications network to continuously transmit environmental variables—temperature, humidity, weight, and water usage—in poultry farms. The reference site is a four-shed facility in Quito, Ecuador (each shed 120m×12m) with a data center located 200m from the sheds. Starting from a calibrated log-distance path-loss model, coverage is declared when the received power exceeds the receiver sensitivity of the selected technology. Gateway placement is cast as a mixed-integer optimization that minimizes deployment cost while meeting target coverage and per-gateway capacity; a capacity-aware greedy heuristic provides a robust fallback when exact solvers stall or instances become too large for interactive use. Sensing instruments are Tekon devices using the Tinymesh protocol (IEEE 802.15.4g), selected for low-power operation and suitability for elongated farm layouts. Model parameters and technology presets inform a pre-optimization sizing step—based on range and coverage probability—that seeds candidate gateway locations. The pipeline integrates MATLAB R2024b and LpSolve 5.5.2.0 for the optimization core, Radio Mobile for network-coverage simulations, and Wireshark for on-air packet analysis and verification. On the four-shed case, the algorithm identifies the number and positions of gateways that maximize coverage probability within capacity limits, reducing infrastructure while enabling continuous monitoring. The final layout derived from simulation was implemented onsite, and end-to-end tests confirmed correct operation and data delivery to the farm’s data center. By combining technology-aware modeling, optimization, and field validation, the work provides a practical blueprint to right-size wireless infrastructure for agricultural monitoring. Quantitatively, the optimization couples coverage with capacity and scales with the number of endpoints M and candidate sites N (binaries M+N+MN). On the four-shed case, the planner serves 72 environmental endpoints and 41 physical-variable endpoints while keeping the gateway count fixed and reducing the required link ports from 16 to 4 and from 16 to 6, respectively, corresponding to optimization gains of up to 82% and 70% versus dense baseline plans. Definitions and a measurement plan for packet delivery ratio (PDR), one-way latency, throughput, and energy per delivered sample are included; detailed long-term numerical results for these metrics are left for future work, since the present implementation was validated through short-term acceptance tests.