Adaptive resource allocation scheme for cognitive radio vehicular ad-hoc network in the presence of primary user emulation attack

被引：19

作者：

Das D. ^{[1
]}

Das S. ^{[1
]}

机构：

[1] Department of Electrical Engineering, NIT, Rourkela

来源：

IET Networks | 2017年 / 6卷 / 01期

关键词：

Genetic algorithms - Radio transmission - Data transfer - Vehicle transmissions - Cognitive radio - Energy efficiency;

D O I：

10.1049/iet-net.2016.0033

中图分类号：

学科分类号：

摘要：

Following the characteristics of the cognitive radio (CR) and the exponential increase of the vehicles, it is envisioned to deploy CR in vehicular ad-hoc network (VANET) in near future. Due to the mobility of the vehicles, it is quite challenging to find the vacant band and to reuse it for data transmission purpose without affecting the primary network. Further, the presence of primary user emulation attack (PUEA) in the VANET makes this task more complicated. Hence, an accurate detection technique and proper power allocation to the vehicular secondary users (VSUs) are the two major factors which need to be addressed for reliable data transmission. Hence this study attempts to evaluate the performance metrics of the CR-VANET considering the spatial correlation among the local decisions of the VSUs in the presence of PUEA. The energy efficiency maximisation with adaptive power allocation to the VSUs is achieved by the authors' proposed scheme based on genetic algorithm under the constraints of interference power to the primary receiver, minimum achievable data rate and maximum transmission power limit. The system performance is investigated in detail through the simulation-based study and analysis.

引用

页码：5 / 13

页数：8

共 26 条

[1]

Singh K.D., Rawat P., Bonnin J.-M., Cognitive radio for vehicular ad hoc networks (CR-VANETs): approaches and challenges, EURASIP J. Wirel. Commun. Network, 2014, 1, pp. 1-22, (2014)

[2]

Di Felice M., Doost-Mohammady R., Chowdhury K.R., Et al., Smart radios for smart vehicles: cognitive vehicular networks, IEEE Veh. Technol. Mag, 7, 2, pp. 26-33, (2012)

[3]

Khan A.A., Rehmani M.H., Reisslein M., Cognitive radio for smart grids: survey of architectures, spectrum sensing mechanisms, and networking protocols, IEEE Commun. Surv. Tutor, 18, 1, pp. 860-898, (2016)

[4]

Yu R., Zhang Y., Gao C., Et al., Energy-efficient and reliability-driven cooperative communications in cognitive body area networks, Mobile Netw. Appl, 16, 6, pp. 733-744, (2011)

[5]

Lin S.-C., Chen K.-C., Cognitive and opportunistic relay for QoS guarantees in machine-to-machine communications, IEEE Trans. Mob. Comput, 15, 3, pp. 599-609, (2016)

[6]

Das D., Das S., Optimal resource allocation for soft decision fusion-based cooperative spectrum sensing in cognitive radio networks, Comput. Electr. Eng., 52, pp. 362-378, (2016)

[7]

Jin F., Varadharajan V., Tupakula U., Improved detection of primary user emulation attacks in cognitive radio networks, Telecommunication Networks and Applications Conf. (ITNAC), pp. 274-279, (2015)

[8]

Sharifi A.A., Sharifi M., Niya M.J.M., Secure cooperative spectrum sensing under primary user emulation attack in cognitive radio networks: attack-aware threshold selection approach, AEU-Int. J. Electron. Commun, 70, 1, pp. 95-104, (2016)

[9]

Garcia-Otero M., Poblacion-Hernandez A., Location aided cooperative detection of primary user emulation attacks in cognitive wireless sensor networks using nonparametric techniques, J. Sens., (2015)

[10]

Yu R., Zhang Y., Liu Y., Et al., Securing cognitive radio networks against primary user emulation attacks, Netw. IEEE, 29, 4, pp. 68-74, (2015)

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