Interference-Aware Resource Scheduling for UAVs-Assisted Agriculture Monitoring

Unmanned aerial vehicles (UAVs) offer a versatile platform for real-time data collection in smart agriculture, where clusters of ground sensors monitor environmental variables across distributed field plots. This paper proposes an interference-aware resource scheduling framework for UAV-assisted agriculture monitoring, jointly optimizing UAV allocation, cluster-level node selection, and collaborative beamforming weight design under energy and practical constraints. Leveraging zero-forcing beamforming, the framework shapes each cluster’s uplink transmission to nullify interference at non-target UAVs while maintaining coherent gain at the intended receiver. We formulate the problem as a mixed-integer program that balances SINR-based throughput, scheduling conflicts, and sensor energy consumption, and develop a lightweight distributed algorithm integrating coordinated scheduling with iterative weight computation. Numerical results demonstrate that our scheme consistently outperforms baselines. It reduces data-collection latency as UAV numbers scale, and sustains high network capacity across varying UAV deployment densities.