基于神经网络的无人机云服务质量控制方法研究

doi:10.3969/j.issn.1000-1093.2018.09.013

兵工学报 ›› 2018, Vol. 39 ›› Issue (9): 1762-1771.doi: 10.3969/j.issn.1000-1093.2018.09.013

基于神经网络的无人机云服务质量控制方法研究

高昂^1,2, 段渭军^1,2, 李立欣¹, 张会生¹, 胡延苏³

(1.西北工业大学电子信息学院，陕西西安 710072; 2.物联网技术及应用国家地方联合工程实验室，陕西西安 710072;3.长安大学电子与控制工程学院，陕西西安 710064)

收稿日期:2017-09-28 修回日期:2017-09-28 上线日期:2018-10-25
作者简介:高昂(1984—)，男，副教授，硕士生导师。E-mail：gaoang@nwpu.edu.cn
基金资助:
中国博士后科学基金项目（2017M623243）；陕西省重点研发计划项目（2017ZDXM-GY-101）；上海航天科技创新基金项目（SAST2016034、SAST2017049）；中央高校基础研究基金项目（3102017ZY029、300102328105)；西安市科技计划项目（201805042YD20CG26（4）)

A Network Control-based QoS-enhanced MAC for UAV Cloud

GAO Ang^1,2, DUAN Wei-jun^1,2, LI Li-xin¹, ZHANG Hui-sheng¹, HU Yan-su³

（1.School of Electronics and Information School, Northwestern Polytechnical University，Xi'an 710072，Shaanxi，China；2.State-Province Joint Engineering Laboratory of IoT Technology and Application，Xi'an 710072，Shaanxi，China； 3.School of Electronic Control, Chang'an University，Xi'an 710064，Shaanxi, China）

Received:2017-09-28 Revised:2017-09-28 Online:2018-10-25

摘要/Abstract

摘要： 无人机云通过动态卸载任务到云端进行高效处理，能够极大地提高无人机的智能水平。由于设计理念、任务环境、突发事件等因素，导致卸载的任务对网络服务质量（QoS）需求不尽相同。从控制角度研究无人机云系统的网络传输QoS控制问题，提出并实现了一种基于BP神经网络的双闭环接入控制方法，在最大化能量利用率的同时，实现绝对QoS和相对QoS保证。硬件实验结果表明，该方法不仅能够在任务动态变化时提供相对QoS和绝对QoS保证，并且在网络高负载下具有更高的吞吐量和能量利用率，在网络低负载下具有更低的能耗。

关键词: 无人机云, 服务质量, 接入控制, 神经网络

Abstract: Unmanned aerial vehicle (UAV) cloud can greatly enhance the intelligence of unmanned system by dynamically uploading the compute-intensive applications to the cloud. The different UAV missions may have different quality of service (QoS) requirements due to the uncertainty of UAV missions and the fast-changing battlefield environment. A BP neuron network-based feedback differentiated control approach for QoS-aware (BPFD)-MAC in UAV cloud is proposed, which can support both absolute and relative QoS guarantees with the consideration of energy saving. The hardware experiments demonstrate the feasibility of BPFD-MAC. Under heavy loads, BPFD has better throughput and power use efficiency; and under light load, BPFD has lower total energy consumption.Key

Key words: UAVcloud, qualityofservice, mediumaccesscontrol, neuronnetwork

中图分类号:

V279⁺.2

高昂, 段渭军, 李立欣, 张会生, 胡延苏. 基于神经网络的无人机云服务质量控制方法研究[J]. 兵工学报, 2018, 39(9): 1762-1771.

GAO Ang, DUAN Wei-jun, LI Li-xin, ZHANG Hui-sheng, HU Yan-su. A Network Control-based QoS-enhanced MAC for UAV Cloud[J]. Acta Armamentarii, 2018, 39(9): 1762-1771.

参考文献

［1］刘重, 高晓光, 符小卫,等. 未知环境下异构多无人机协同搜索打击中的联盟组建［J］. 兵工学报, 2015, 36(12):2284-2297.
LIU Zhong, GAO Xiao-guang, FU Xiao-wei, et al. Coalition formation of multiple heterogeneous unmanned aerial vehicles in cooperative search and attack in unknown environment［J］. Acta Armamentarii, 2015, 36(12): 2284-2297.(in Chinese)
［2］ Luo F, Jiang C, Yu S, et al. Stability of cloud-based UAV systems supporting big data acquisition and processing［J］. IEEE Transactions on Cloud Computing, 2017, 99:1-1.
［3］ Hu G, Tay W P, Wen Y. Cloud robotics: architecture, challenges and applications［J］. IEEE Network, 2012, 26(3):21-28.
［4］ Kamei K, Nishio S, Hagita N, et al. Cloud networked robotics［J］. IEEE Network, 2012, 26(3):28-34.
［5］ Li F, Wan J F, Zhang P, et al. Usage-specific semantic integration for cyber-physical robot systems［J］. ACM Transactions on Embedded Computing Systems, 2016, 15(3):50.
［6］ Chaisiri S, Lee B S, Niyato D. Robust cloud resource provisioning for cloud computing environments［C］∥Proceedings of IEEE International Conference on Service-Oriented Computing and Applications. Irvine, CA, US: IEEE, 2011: 1-8.
［7］ Pandey P, Pompili D, Yi J. Dynamic collaboration between networked robots and clouds in resource-constrained environments［J］. IEEE Transactions on Automation Science & Engineering, 2015, 12(2):471-480.
［8］ Saxena N, Roy A, Shin J. Dynamic duty cycle and adaptive contention window based QoS-MAC protocol for wireless multimedia sensor networks［J］. Computer Networks, 2008, 52(13):2532-2542.
［9］ Subramanian A K, Paramasivam I. PRIN:a priority-based energy efficient MAC protocol for wireless sensor networks varying the sample inter-arrival time［J］. Wireless Personal Communications, 2016, 92(3):1-19.

［10］ Jang Beakcheol, Lim J B, Sichitiu M L. An asynchronous sche- duled MAC protocol for wireless sensor networks［J］. Computer Networks， 2013, 57(1):85-98.
［11］ Naderi M Y, Nintanavongsa P, Chowdhury K R. RF-MAC: a medium access control protocol for rechargeable sensor networks powered by wireless energy harvesting［J］. IEEE Transactions on Wireless Communications, 2014, 13(7):3926-3937.
［12］ Chang B J, Chen S P. Cross-layer-based adaptive congestion and contention controls for accessing cloud services in 5G IEEE 802.11family wireless networks［J］. Computer Communications, 2017, 106:33-45.
［13］ zen Y, Bandrmal N, Baylm?瘙塂 C. Two tiered service differentiation mechanism for wireless multimedia sensor network MAC layers［C］∥Proceedings of Signal Processing and Communications Applications Conference. Malatya, Turkey: IEEE, 2015:2318-2321.
［14］ Natkaniec M, Kosek-Szott K, Szott S, et al. A survey of medium access mechanisms for providing QoS in Ad-Hoc networks［J］. IEEE Communications Surveys & Tutorials, 2013, 15(2):592-620.
［15］ Barua S, Afroz F, Islam S S, et al. Comparative study on priority based QOS aware MAC protocols for WSN［J］. International Journal of Wireless & Mobile Networks, 2014, 6(5):175-181.
［16］ Gao A, Hu Y. A feedback approach for QoS-enhanced MAC in wireless sensor network［J］. Journal of Sensors, 2016(2):1-12.
［17］ Funahashi K I. On the approximate realization of continuous mappings by neural networks［J］. Neural Networks, 1989, 2(3): 183-192.
［18］ Man Z, Wu H R, Liu S, et al. A new adaptive back-propagation algorithm based on Lyapunov stability theory for neural networks［J］. IEEE Transactions on Neural Networks, 2006, 17(6): 1580-1591.
［19］张国翊, 胡铮. 改进 BP 神经网络模型及其稳定性分析［J］. 中南大学学报：自然科学版, 2011, 42(1): 115-124.
ZHANG Guo-yi, HU Zheng. Improved BP neural network model and its stability analysis［J］. Journal of Central South University：Science and Technology，2011, 42(1): 115-124. (in Chinese)

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基于神经网络的无人机云服务质量控制方法研究

A Network Control-based QoS-enhanced MAC for UAV Cloud

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