AN ENERGY BALANCE AND MOBILITY PREDICTION CLUSTERING ALGORITHM FOR LARGE-SCALE UAV AD HOC NETWORKS

Creating a clustering structure is considered the performance of radio frequency (RF) stealth for data link in the battlefield environment and the dynamic topology characteristic for larger-scale unmanned aerial vehicle (UAV) ad hoc networks. This problem is of a great importance-to get low intercept probability of the data link and low randomness of clustering structure. An energy balance and mobility prediction (EBMP) clustering algorithm is proposed. In the initial clustering stage, the power management for information transmission is conducted in the network layer and the MAC layer. The Doppler shift is implemented to estimate the relative speeds stability degree between neighboring UAVs when they exchange Hello packets. It can be selected as cluster head (CH) where one UAV associates lower energy consumption with higher relative stability. In the cluster maintaining stage, a CH rotation process for the dynamic topology to improve resource utilization efficiency. The inter-cluster communication is enhanced by dynamic packet forwarding gateway. The simulations and analysis show that this scheme can provide better results for larger-scale UAV ad hoc networks compared to MPBC and MPCR in terms of improving CH lifetime and throughput, reducing average delay.