![]() Thus, such UAVs are automatically eliminated from the UAV pool. ![]() In the case of malicious classification, the fog node reduces the tokens, resulting in the UAV not being able to charge fully for the duration of the trip. An intrusion detection system is deployed at the fog nodes that utilize machine learning models to classify UAV behavior as malicious or benign. These tokens can later be redeemed to charge the UAVs for their subsequent trips. The framework adopts the concept of a charging token, where upon completing a trip, UAVs receive tokens from the fog node. In this paper, a fog computing-based smart farming framework is proposed that utilizes UAVs to gather data from IoT sensors deployed in farms and offloads it at fog sites deployed at the network edge. Furthermore, it impacts other UAVs competing for charging times at the station, thus disrupting the entire data collection mechanism. Due to limited battery life and flight times requiring frequent recharging, a compromised UAV wastes precious energy when performing unnecessary functions. However, due to an open environment, UAVs can be hacked to malfunction and report false data. In large-scale agriculture, the role of unmanned aerial vehicles (UAVs) has increased in remote monitoring and collecting farm data at regular intervals. ![]() ![]() Precision agriculture and smart farming have received significant attention due to the advancements made in remote sensing technology to support agricultural efficiency. ![]()
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