Bio-Inspired Mobility Prediction Clustering Algorithm for Ad Hoc UAV Networks

—Clustering is an effective method which can increase the performance of large-scale ad hoc Unmanned Aerial Vehicle (UAV) networks. However, the ad hoc UAV networks have the feature of high mobility and quick network topology change, using conventional clustering algorithm will lead to the decrease of link connection lifetime and cluster head lifetime, frequent updates of cluster topology would cause the instability of cluster structure and the increase of control overhead. In order to solve the problem that traditional clustering algorithm cannot adapt to the highly dynamic largescale ad hoc UAV networks, Bio-Inspired Mobility Prediction Clustering (BIMPC) algorithm is proposed. This algorithm transplants the foraging model of physarum polycephalum to the field of ad hoc UAV networks, and combines with the mobility characteristic of UAV which can get from the signal feature of Hello packets. Making use of the modified model, we can conduct the cluster formation and maintenance effectively. Simulations have shown that the BIMPC algorithm outperforms the classical clustering algorithm in terms of average link connection lifetime and average cluster head lifetime, which can make the cluster structure more stable. As a result, this algorithm is ideal for highly dynamic large-scale ad hoc UAV networks.