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High dynamic topology and limited bandwidth of the airborne network make it difficult to provide reliable information interaction services for diverse combat mission of aviation swarm operations. Therefore, it is necessary to identify the elephant flows in the network in real time to optimize the process of traffic control and improve the performance of airborne network. Aiming at this problem, a timeliness-enhanced traffic identification method based on machine learning Bayesian network model is proposed. Firstly, the data flow training subset is obtained by preprocessing the original traffic dataset, and the sub-classifier is constructed based on Bayesian network model. Then, the multi-window dynamic Bayesian network classifier model is designed to enable the early identification of elephant flow. The simulation results show that compared with the existing elephant flow identification method, the proposed method can effectively improve the timeliness of identification under the condition of ensuring the accuracy of identification.

Copyright of Journal of National University of Defense Technology / Guofang Keji Daxue Xuebao is the property of NUDT Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to enhance the anti-jamming capability of aeronautic swarm tactical network in the complicated electromagnetic environment, we address the problem of bandit-based cognitive anti-jamming strategy for enabling reliable information transmission. We first present an adversarial multiuser multi-armed bandit model for the aeronautic swarm network employing airborne cognitive radios with the same-frequency simultaneous transmit and receive feature. Then, we utilize the improved energy detection method to perform jamming sensing and derive the closed expression of false alarm probability, false detection probability, and the optimal decision threshold in the case of single and multi-jammer. Finally, using the jamming sensing output to calculate reward and with the objective of maximizing the throughput of each airborne radio, a decentralized selfish doubling trick kl-UCB++ anti-jamming strategy is developed to allocate an optimal configuration of transmitting power and spectrum channel to each radio. This anytime bandit strategy is simultaneously minimaxed optimal and asymptotically optimal. The simulation results validate that the aggregate average throughput, cumulative regret obtained with the proposed anti-jamming strategy outperform the well-known UCB, kl-UCB++ bandit algorithm.

Aeronautic swarm was a new kind of aeronautic operationa1 system inspired by bio1ogica1 swarm behaviors.Whi1e 1eading to a new usage way of future aeronautic operationa1 resources，aeronautic swarm wou1d bring new cha11enges to the airborne tactica1 network（ATN），which was as a bond between the swarm members.Based on the operationa1 demand of aeronautic swarm，the airborne tactica1 network of aeronautic swarm（ATNAS）was introduced，The basic capabi1ity requirements of ATNAS were ana1yzed，and the advantages of app1ying the SDN network design idea to the construction of ATNAS were a1so summarized.Then on the bases above，software defined airborne tactica1 networking for aeronautic swarm（SDATN-AS）was proposed.The basic architecture of SDATN-AS was described，and a series of future research directions and cha11enges re1ating to SDATN-AS were a1so summarized.A new exp1oration of the ATN's morpho1ogy was made which supported the efficient information interaction of aeronautic swarm members，and reference and guidance were offered for deciding the deve1opment orientations of ATN in the future.

Copyright of Journal of National University of Defense Technology / Guofang Keji Daxue Xuebao is the property of NUDT Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the problem of the effective connectivity of a large number of mobile combat units in the future aeronautic swarm operation, this paper proposes an idea of using UAV(Unmanned Aerial Vehicle) to build, and studies the deployment of the network. User coverage and network connectivity are important for a relay network planning which are studied separately in traditional ways. In order to effectively combine these two factors while the network’s survivability is taken into account. Firstly, the concept of node aggregation degree is proposed. Secondly, a performance evaluation parameter for UAV relay network is proposed based on node aggregation degree, then analyzes the lack of deterministic deployment and presents one a PSO (VFA-PSO) deployment algorithm based on virtual force. Finally, compared with the existing algorithms, the validity and stability of the algorithm are verified. The experimental results show that the VFA-PSO algorithm can effectively improve the network coverage and the survivability of the network under the premise of ensuring the network connectivity, and has better deployment effect.