Abstract
The limited battery capacity of sensor nodes (SNs) is one of the challenges for achieving sustainable precision agriculture in 5G. When SNs 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 SNs and collecting sensing data using unmanned aerial vehicles. The problems of cluster head (CH) election and path planning are considered at the same time to maximize charging efficiency in a way that the lifetime of SNs can be prolonged. Moreover, {K}K-means-based CH and charging position selection algorithm and ACO-based charging path-planning algorithm are proposed to optimize charging planning of the 3-D environment. Simulation results show that the proposed mechanism can minimize the number of CHs and maximize the remaining energy of SNs.