Optimization Method for Energy Consumption in Data Acquisition Assisted by UAV Swarms

To ensure security and reduce energy consumption for data acquisition in Wireless Sensor Network (WSN), Unmanned Aerial Vehicle (UAV) swarms-aided energy consumption optimization for data acquisition algorithm is proposed. The total energy consumption of the system is reduced by optimizing the number of UAVs, the height and the number of data transmissions in the WSN according to this algorithm. Firstly, for data acquisition in WSN, a Reputation baseD Data Dual Compression (RDDC) algorithm is proposed, which divides sensors into clusters according to geographic locations. In a cluster, there are one cluster head which is responsible for model selection, aggregation, and reputation update, and several cluster members which are responsible for training the prediction model and send it to the cluster head. Secondly, a UAV deployment optimization algorithm is proposed to minimize energy consumption of UAV swarms, which is transformed into a circular packing problem and solved by dynamically adjusting the number of UAVs. Moreover, a private blockchain is enabled in the UAV swarm to improve the security of the data acquisition process. Finally, the proposed method is verified by Berkeley Research Laboratory dataset and simulation results show that this method could optimize the deployment of UAVs, achieve small error, low energy consumption and high security.