Achieving consensus is a crucial problem for large scale drone swam to perform collaboration tasks. There are some critical issues for the consensus of large-scale drone swarms, such as limited-time convergence and high robustness against drone failure due to stringent requirements of mission cycle and hostile environment. Note that traditional consensus cannot guarantee limited-time convergence and a robust consensus against node failure. Existing finite-time consensus faces challenges of strict prerequisites and high complexity. Moreover, leaderless and leader-follower consensus adopts different models respectively. Toward these ends, a unified consensus model for both leaderless and leader-follower modes is proposed. It achieves limited-time convergence by using distributed energy minimization. A dynamic spanning tree algorithm is designed to ensure consensus under dynamic topology. Furthermore, a robust method against node failure is proposed by combining grey prediction, average consensus, and Molly-Reed criterion. Simulation results show that the proposed methods can be adopted in both leaderless and leader-follower situations with advantages of limited-time convergence and high robustness.

Drones and drone swarms, characterized by their low price and ease of deployment, are being utilized to launch assaults, and presenting a great threat to the public and homeland security in recent years. Countering drones or drone swarms is thus of great significance. Some effective counter approaches for a small group of drones have been studied. However, these approaches may be insufficient to counter a large-scale drone swarm due to the lack of efficient time-saving detecting technologies. Towards this end, this paper proposes a fast counter approach to deprive the drone swarm of its coordination and clustering capabilities in a short time by splitting the drone swarm into several unconnected components. To achieve efficient splitting, two efficient algorithms for searching critical nodes are proposed, namely, the genetic algorithm and the particle swarm optimization algorithm. Extensive simulation results are presented to validate the superior performances of the proposed two algorithms for splitting the drone swarms in different formations. The results show that the drone swarms lose their coordination ability as they are forced to split into multiple components with a constraint of group size. The high accuracy and efficiency of the proposed algorithms are also verified by a series of comparative experiments.

Roadside units (RSUs) play a vital role in intelligent transportation systems (ITS), working as critical elements in delivering superior Internet of Vehicles (IoV) services. A large service coverage and fast accident information diffusion RSU deployment solution can reliably ensure the ITS’ quality of service. Simultaneously, with the development of the city and the ITS, changes in traffic flow lead to RSU load imbalance, which will reduce the benefit of the original RSU deployment, and it is necessary to adjust RSU locations with minimal cost. Besides, due to the high visibility of the ITS, RSUs are highly susceptible to external attacks, which is commonly overlooked in existing RSU deployment work. Specifically, Sybil attack is one of the most dangerous attacks against ITS, and it can reshape the network state by forging multiple identities, interfering with risk sensing, etc. Motivated by these, we respectively propose the PSO-meme joint heuristic deployment algorithm (PJHDA) and the heuristic RSU multi-objective adaptation adjustment algorithm (HRMA3) to carry out deployment and adaptation adjustment of the city’s RSUs, taking into account the constraint of Sybil attack detection. Numerical results demonstrate that the multi-attribute performance of PJHDA is superior to the existing schemes. Compared with benchmark schemes, the HRMA3 excels in achieving advanced service coverage and load balancing while controlling costs, and both proposed schemes exhibit higher Sybil attack detection rate.