Data Collection Strategy Based on Drone Technology in Wireless Sensor Networks

In recent years, drone technology has developed rapidly. Drone's low cost, fast and flexible deployment, as well as strong mobility have made it possible to use drone-assisted sensor networks for data collection tasks. In this way, data collection nodes can break through the movement path restriction of traditional nodes, broaden the spatial movement range of nodes, and it is more suitable for data collection in complex environments. In this paper, we proposed a data collection strategy based on drone technology in Wireless Sensor Networks. Kmeans++ clustering method is used for auxiliary clustering and cluster head election in the initial state, which significantly improves the final error of the clustering result. Then, we used drone to assist cluster head election and data collection, which comprehensively considering the relative distance of every sensor node in the cluster and their relative remaining energy. In addition, for some nodes that have not been elected in the previous specified round, a reasonable priority is set to make the energy consumption of sensor nodes in the entire network more balanced. At the same time, we excluded the influence of dead nodes. Compared with many new methods proposed in recent years, the data collection strategy proposed delays the death time of the sensor nodes, reduces the overall energy consumption of the sensor nodes, and has a better performance. This work provides new ideas for the future work.