Abstract
The limited battery capacity of sensor nodes is one of the challenges for achieving sustainable precision agriculture in 5G. When the sensor nodes run out of energy, the data center will not be able to grasp the information of the environment, and the corresponding decisions and actions will not be performed. To solve this problem, this study presents a framework for charging sensor nodes and collecting sensing data using Unmanned Aerial Vehicle (UAV). The problems of cluster head election and path planning are considered at the same time to maximize charging efficiency in a way that the lifetime of sensor nodes can be prolonged. Moreover, K-means based Cluster Head and Charging Position Selection algorithm (KCHCPS) and ACO-based Charging Path Planning Algorithm (ACOCPP) are proposed to optimize charging planning of the 3D environment. Simulation results clearly show the advantages of the proposed mechanism as compared to the state-of-the-art works.
Accepted Version
Published Version
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