Airborne highly dynamic ad hoc UAV network has features of high node mobility, fast changing network topology, and complex application environment. The performance of traditional routing algorithms is so poor over aspects such as end to end delay, data packet delivery ratio, and routing overhead that they cannot provide efficient communication for multi-UAVs carrying out missions synergistically. A bionic optimization based stability and congestion aware routing algorithm-BSCAR algorithm-is proposed to solve these problems. This algorithm integrates biological behavior and dynamic source routing algorithm, which can sense the congestion level of routes and the stability of routes. Ant colony optimization algorithm and the mathematical model of Physarum's behavior exert effort in the process of route discovery and maintenance. The level of pheromone in routes is chosen as a standard to choose route and calculated by the mathematical model of Physarum's behavior. A new volatilization mechanism of pheromone is also introduced into the algorithm. Meanwhile, the algorithm can make adjustment to the variance of UAV formation to prevent the compromise of the network performance. The simulation results show that the BSCAR algorithm has superiority over traditional algorithms and it is dependable in battlefield environment.
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 associateslower 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.
Abstract. Aiming at the special circumstance in which UAVs swarm are used in the mode of battlefield extending, a message delivery scheme called AWJPMMD (ARIMA-WNN Joint Prediction Model based Message Delivery) is proposed. In this scheme, the LET (Link Expiration Time) of the center node and the proxy node is calculated by high precision GPS information, then the LET at next moment is predicted by ARIMA-WNN (Autoregressive Integrated Moving Average model -Wavelet Neural Network) Joint Prediction Model. Finally, the process of message delivery is affected by the predicted value of LET and other parameters. The target information is sent to the UAVs ground station in form of store-and-forward by the message delivery process. Simulation shows that this scheme can provide higher message delivery ratio and this scheme is more stable.
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