Efficient unmanned aerial vehicle-based data collection for IoT smart farming

The extensive range of unmanned aerial vehicles (UAVs) is essential for efficiently gathering data in inaccessible areas. The data received is processed near the end-user to reduce processing delays that could affect time-sensitive applications. This study employs a novel method for data gathering and an effective scheduling framework for intelligent agricultural systems. The suggested paradigm for UAV-based IoT data collection encompasses three primary components: optimal UAV trajectory, data gathering, and data scheduling. These components are specifically designed to incorporate trajectory, energy efficiency, and resource optimization to improve performance. IoT sensors are strategically placed throughout the data collection process to ensure optimal clustering. The clustering is determined by considering various parameters such as distance, latency, energy usage, trust, and quality of service (QoS), among other important factors. The integration of the Lion Algorithm (LA) and Cat Mouse-Based Optimization (CMBO) techniques has led to the proposal of a unique optimization methodology called Lion Mated with Cats Optimization (LMCO). This approach aims to identify the optimal cluster head (CH) in a new and innovative way. To gather data from all clusters, the UAV plans the most efficient route, following a direct path while also avoiding any possible collisions via the LMCO model. The trajectory is computed using received signal strength indicator (RSSI) measurements. The base station (BS) receives the data transmitted by the UAV in close vicinity. Subsequently, the BS selects the most appropriate cloud node based on factors such as response rate, ideal five-fold power efficiency, quality of service (QoS), execution time, and availability. The simulation results are showcased to verify the effectiveness of the suggested approach and are compared with the existing model to demonstrate the superiority of the proposed method.