DFRA: Demodulation-free random access for UAV ad hoc networks

Due to the agility, low-cost and robustness, UAV (Unmanned Aerial Vehicle) Ad Hoc Networks formed by small UAVs have popular application in the battlefield. Considering the high mobility of UAV which may exit and join in the networks frequently, random access is critical for UAV Ad Hoc Networks. Due to the complex and serious electromagnetic environment in the battlefield, how to identify the MAC protocol when demodulation is unrealistic and switch to this MAC protocol adaptively is challenging. In this paper, we propose Demodulation-free Random Access (DFRA) scheme which can help UAVs join in the UAV ad hoc networks without demodulating the property field of MAC protocol header. First, we propose an adaptive feature extraction algorithm and use it for machine learning based MAC protocol identification. Then, DFRA adopts an adaptive MAC switching framework to access the networks. We implement DFRA with USRP N210 and evaluate the performance by experiments. The results show that DFRA can guarantee access accuracy rate over 95% when demodulation is unrealistic.

[1]  Michal Pechoucek,et al.  Autonomous UAV Surveillance in Complex Urban Environments , 2009, 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology.

[2]  Hai Wang,et al.  Design and Implementation of Adaptive MAC Framework for UAV Ad Hoc Networks , 2016, 2016 12th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN).

[3]  Mithun Acharya,et al.  Intelligent Jamming Attacks , Counterattacks and ( Counter ) 2 Attacks in 802 . 11 b Wireless Networks , 2005 .

[4]  Carlos Eduardo Pereira,et al.  UAV relay network to support WSN connectivity , 2010, International Congress on Ultra Modern Telecommunications and Control Systems.

[5]  Hazem H. Refai,et al.  Energy detection and machine learning for the identification of wireless MAC technologies , 2015, 2015 International Wireless Communications and Mobile Computing Conference (IWCMC).

[6]  Feng Jiang,et al.  Dynamic UAV relay positioning for the ground-to-air uplink , 2010, 2010 IEEE Globecom Workshops.

[7]  Lei Tian,et al.  Development of a low-cost agricultural remote sensing system based on an autonomous unmanned aerial vehicle (UAV) , 2011 .

[8]  Jiming Chen,et al.  Full-View Area Coverage in Camera Sensor Networks: Dimension Reduction and Near-Optimal Solutions , 2016, IEEE Transactions on Vehicular Technology.

[9]  S. Milner,et al.  Autonomous reconfiguration and control in directional mobile ad hoc networks , 2009, IEEE Circuits and Systems Magazine.

[10]  D. Staelin Fast folding algorithm for detection of periodic pulse trains , 1969 .

[11]  Ilker Bekmezci,et al.  Flying Ad-Hoc Networks (FANETs): A survey , 2013, Ad Hoc Networks.

[12]  Shaojie Tang,et al.  Demodulation-free protocol identification in heterogeneous wireless networks , 2015, Comput. Commun..

[13]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[14]  Zhuo Yang,et al.  MAC protocol identification using support vector machines for cognitive radio networks , 2014, IEEE Wireless Communications.