research-article

Cooperative Spectrum Sensing with Trust Assistance for Cognitive Radio Vehicular Ad hoc Networks

Authors:

Azzedine BoukercheAuthors Info & Claims

DIVANet '15: Proceedings of the 5th ACM Symposium on Development and Analysis of Intelligent Vehicular Networks and Applications

Pages 27 - 33

https://doi.org/10.1145/2815347.2815350

Published: 02 November 2015 Publication History

Abstract

As Vehicular Ad hoc Networks (VANETs) attract a lot of research attention, VANETs with Cognitive Radio (CR-VANETs) are also studied as supplement and enhancement of VANETs, which are used to solve the issues of spectrum shortage in VANETs. With this CR technology, VANETs can satisfy more bandwidth demanding for amounts of applications based on communication between vehicles. Cooperative spectrum sensing is naturally suitable for CR-VANETs due to its autonomous characteristic. Cooperative spectrum sensing opens a door for malicious attacks in spectrum sensing. It assumes that every vehicle in the network is always honest and benign. However, the security attacks exist in all kinds of networks. Thus, solving this security issue becomes critical for CR-VANETs, which are using cooperative spectrum sensing. In this paper, a distributed cooperative spectrum sensing scheme is proposed to solve the security issue in CR-VANETs. In this scheme, a weighted consensus-based spectrum sensing algorithm with trust assistance is used to protect the spectrum sensing process in a hostile CR-VANET. The effectiveness of the proposed scheme is validated by extensive simulations.

References

[1]

K. Abrougui, A. Boukerche, and R. Pazzi. Location-aided gateway advertisement and discovery protocol for VANETs. IEEE Trans. Veh. Tech., 59(8):3843--3858, Oct. 2010.

[2]

I. F. Akyildiz, W. Y. Lee, M. C. Vuran, and S. Mohanty. Next generation/dynamic spectrum access/cognitive radio wireless networks: a survey. Computer Networks, 50:2127--2159, 2006.

Digital Library

[3]

M. Azizian, E. D. Ngangue Ndih, and S. Cherkaoui. Improved multi-channel operation for safety messages dissemination in vehicular networks. In Proceedings of the Fourth ACM International Symposium on Development and Analysis of Intelligent Vehicular Networks and Applications, DIVANet '14, pages 81--85, New York, NY, USA, 2014. ACM.

Digital Library

[4]

S. Basagni, M. Conti, S. Giordano, and I. Stojmenovic. Mobile Ad Hoc Networking: The Cutting Edge Directions. Wiley-IEEE Press; 2nd Edition, 2013.

[5]

S. Bu, F. R. Yu, P. Liu, P. Manson, and H. Tang. Distributed combined authentication and intrusion detection with data fusion in high-security mobile ad hoc networks. IEEE Trans. Veh. Tech., 60(3):1025 --1036, Mar. 2011.

[6]

S. Bu, F. R. Yu, X. P. Liu, and H. Tang. Structural results for combined continuous user authentication and intrusion detection in high security mobile ad-hoc networks. IEEE Trans. Wireless Commun., 10(9):3064 --3073, Sept. 2011.

[7]

K. C. Chen, Y. J. Peng, N. Prasad, Y. C. Liang, and S. Sun. Cognitive radio network architecture: part ii - trusted network layer structure. In Proc. of the 2nd international conference on Ubiquitous information management and communication, New York, NY, USA, Apr. 2008.

Digital Library

[8]

R. Chen, J.-M. Park, and Y. T. Hou. Toward secure distributed spectrum sensing in cognitive radio networks. IEEE Comm. Mag., April 2008.

Digital Library

[9]

R. Chen, J.-M. Park, and B. Kaigui. Robust distributed spectrum sensing in cognitive radio networks. In Proc. IEEE INFOCOM'08, Phoenix, AZ, USA, Apr. 2008.

[10]

H. Cheng, X. Fei, M. Almulla, and A. Boukerche. A knapsack constrained steiner tree model for continuous coverage over urban VANETs. In Proc. IEEE ICC'14, pages 130--135, June 2014.

[11]

J. H. Cho, A. Swami, and I. R. Chen. A survey on trust management for mobile ad hoc networks. IEEE Communications Surveys and Tutorials, 13(4):562--583, 2011.

[12]

O. Fatemieh, R. Chandra, and C. A. Gunter. Secure collaborative sensing for crowdsourcing spectrum data in white space networks. In Proc. IEEE DySPAN'2010, Singapore, Apr. 2010.

[13]

A. Ghasemi and E. Sousa. Collaborative spectrum sensing for opportunistic access in fading environments. In Proc. IEEE DySPAN'2005, Baltimore, MD, USA, Nov. 2005.

[14]

A. Grzybek, G. Danoy, M. Seredynski, and P. Bouvry. Evaluation of dynamic communities in large-scale vehicular networks. In Proceedings of the Third ACM International Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications, DIVANet '13, pages 93--100, New York, NY, USA, 2013. ACM.

Digital Library

[15]

S. Haykin. Cognitive radio: brain-empowered wireless communications. IEEE J. Sel. Areas Commun., 23(2):201--220, 2005.

Digital Library

[16]

P. Kaligineedi, M. Khabbazian, and V. K. Bhargava. Malicious user detection in a cognitive radio cooperative sensing system. IEEE Trans. Wireless Commun., 9(8):2488--2497, 2010.

Digital Library

[17]

S. Khakpour, R. W. Pazzi, and K. El-Khatib. A prediction based clustering algorithm for target tracking in vehicular ad-hoc networks. In Proceedings of the Fourth ACM International Symposium on Development and Analysis of Intelligent Vehicular Networks and Applications, DIVANet '14, pages 39--46, New York, NY, USA, 2014. ACM.

Digital Library

[18]

G. Li, Z. Gu, X. Lin, H. Pu, and Q.-s. Hua. Deterministic distributed rendezvous algorithms for multi-radio cognitive radio networks. In Proceedings of the 17th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, MSWiM '14, pages 313--320, New York, NY, USA, 2014. ACM.

Digital Library

[19]

H. Li and Z. Han. Catch me if you can: an abnomility detection approach for collaborative spectrum sensing in cognitive radio networks. IEEE Trans. Wireless Commun., 9(11):3554--3565, 2010.

Digital Library

[20]

Z. Li, F. R. Yu, and M. Huang. A distributed consensus-based cooperative spectrum-sensing scheme in cognitive radios. IEEE Trans. Veh. Tech., 59(1):383--393, 2010.

[21]

C. Liang and F. R. Yu. Wireless network virtualization: A survey, some research issues and challenges. IEEE Commun. Surveys Tutorials, 17(1):358--380, Firstquarter 2015.

Digital Library

[22]

C. Liang, F. R. Yu, and X. Zhang. Information-centric network function virtualization over 5G mobile wireless networks. IEEE Network, 29(3):68--74, May 2015.

Digital Library

[23]

C. Luo, F. R. Yu, H. Ji, and V. Leung. Distributed relay selection and power control in cognitive radio networks with cooperative transmission. In Proc. IEEE ICC'10, May 2010.

[24]

Y. K. Matar, L. A. Villas, and R. W. Pazzi. Dual-mode optimum distance routing scheme for vehicular ad hoc networks. In Proceedings of the Third ACM International Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications, DIVANet '13, pages 15--22, New York, NY, USA, 2013. ACM.

Digital Library

[25]

L. Moreau. Stability of multiagent systems with time-dependent communication links. IEEE Trans. Auto. Control, 50(2):169--181, Feb. 2005.

[26]

S. Moursi and M. ElNainay. A multi-metric routing protocol with service differentiation for cognitive radio ad-hoc networks. In Proceedings of the 16th ACM International Conference on Modeling, Analysis & Simulation of Wireless and Mobile Systems, MSWiM '13, pages 129--134, New York, NY, USA, 2013. ACM.

Digital Library

[27]

L. Nassar, M. Kamel, and F. Karray. Evaluating vanet information retrieval context aware systems using the average distance measure adm. In Proceedings of the Fourth ACM International Symposium on Development and Analysis of Intelligent Vehicular Networks and Applications, DIVANet '14, pages 61--66, New York, NY, USA, 2014. ACM.

Digital Library

[28]

C. Rezende, A. Boukerche, H. Ramos, and A. Loureiro. A reactive and scalable unicast solution for video streaming over VANETs. IEEE Trans. Computers, 64(3):614--626, Mar. 2015.

[29]

J. Riihijarvi, J. Nasreddine, and P. Mahönen. Influence of spatial statistics of spectrum use on the performance of cognitive wireless networks. In Proceedings of the 15th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, MSWiM '12, pages 5--14, New York, NY, USA, 2012. ACM.

Digital Library

[30]

B. Sun, F. Yu, K. Wu, Y. Xiao, and V. Leung. Enhancing security using mobility-based anomaly detection in cellular mobile networks. IEEE Trans. Veh. Tech., 55(4):1385--1396, July 2006.

[31]

Z. Tian. Compressed wideband sensing in cooperative cognitive radio networks. In Proc. IEEE GLOBECOM'08, New Orleans, LA, USA, Nov. 2008.

[32]

F. Wang, H. Tang, F. R. Yu, and P. C. Mason. A hierarchical identity based key management scheme in tactical mobile ad hoc networks. In Proc. IEEE Milcom'09, Boston, MA, USA, Oct. 2009.

Digital Library

[33]

W. Wang, H. Li, Y. Sun, and Z. Han. Catch it: Detect malicious nodes in collaborative spectrum sensing. In Proc. IEEE GLOBECOM'09, Honolulu, HAWAI, USA, Nov. 2009.

Digital Library

[34]

Z. Wei, F. R. Yu, and A. Boukerche. Trust based security enhancements for vehicular ad hoc networks. In Proc. ACM DIVANet'14, Montreal, Canada, Oct. 2014.

Digital Library

[35]

Q. Yan, M. Li, T. Jiang, W. Lou, and Y. T. Hou. Vulnerability and protection for distributed consensus-based spectrum sensing in cognitive radio networks. In Proc. IEEE INFOCOM'12, Orlando, FL, USA, Mar. 2012.

[36]

Z. Yin, F. Yu, S. Bu, and Z. Han. Joint cloud and wireless networks operations in mobile cloud computing environments with telecom operator cloud. IEEE Trans. Wireless Commun., 14(7):4020--4033, July 2015.

Digital Library

[37]

M. Younes and A. Boukerche. Scool: A secure traffic congestion control protocol for VANETs. In Proc. IEEE WCNC'15, pages 1960--1965, Mar. 2015.

[38]

F. R. Yu. Cognitive Radio Mobile Ad Hoc Networks. Springer, New York, 2011.

Digital Library

[39]

F. R. Yu, M. Huang, and H. Tang. Biologically inspired consensus-based spectrum sensing in mobile ad hoc networks with cognitive radios. IEEE Network, 24(3):26 --30, May 2010.

Digital Library

[40]

F. R. Yu, H. Tang, M. Huang, Z. Li, and P. C. Mason. Defense against spectrum sensing data falsification attacks in mobile ad hoc networks with cognitive radios. In Proc. IEEE MilCom'09, Boston, MA, USA, Oct. 2009.

Digital Library

[41]

F. R. Yu, H. Tang, P. Mason, and F. Wang. A hierarchical identity based key management scheme in tactical mobile ad hoc networks. IEEE Trans. on Network and Service Management, 7(4):258 --267, Dec. 2010.

Digital Library

[42]

S. Zhang, F. R. Yu, and V. Leung. Joint connection admission control and routing in IEEE 802.16-based mesh networks. IEEE Trans. Wireless Commun., 9(4):1370 --1379, Apr. 2010.

Digital Library

Cited By

El-haryqy NMadini ZZouine Y(2024)A review of deep learning techniques for enhancing spectrum sensing and prediction in cognitive radio systems: approaches, datasets, and challengesInternational Journal of Computers and Applications10.1080/1206212X.2024.241404246:12(1104-1128)Online publication date: 21-Oct-2024
https://doi.org/10.1080/1206212X.2024.2414042
Idris MAhmedy ISoon TYahuza MTambuwal AAli U(2024)Cognitive radio and machine learning modalities for enhancing the smart transportation system: A systematic literature reviewICT Express10.1016/j.icte.2024.05.001Online publication date: May-2024
https://doi.org/10.1016/j.icte.2024.05.001
Patel DKavaiya SDing ZGuan YSun S(2022)Impact of Primary User Activity Statistics in Cognitive Vehicular NetworksIEEE Transactions on Vehicular Technology10.1109/TVT.2021.313825371:3(2859-2873)Online publication date: Mar-2022
https://doi.org/10.1109/TVT.2021.3138253
Show More Cited By

Index Terms

Cooperative Spectrum Sensing with Trust Assistance for Cognitive Radio Vehicular Ad hoc Networks
1. Information systems

Recommendations

Distributed cooperative spectrum sensing in cognitive radio for ad hoc networks

Cognitive radio refers to an intelligent radio with the capability of sensing the radio environment and dynamically reconfiguring the operating parameters. Recent research has focused on using cognitive radios in ad hoc environments. Spectrum sensing is ...
Group-based management for cooperative spectrum sensing in cognitive radio networks
ICACT'10: Proceedings of the 12th international conference on Advanced communication technology

In cognitive radio networks, secondary users can opportunistically utilize the unused spectrum holes that are originally licensed to primary users. Therefore, spectrum sensing for seeking unutilized spectrum is a key element to establish cognitive radio ...
Fully Distributed Cooperative Spectrum Sensing for Cognitive Radio Ad Hoc Networks
FIT '11: Proceedings of the 2011 Frontiers of Information Technology

Cognitive radios are indispensable to shift from conventional spectrum assignment to dynamic spectrum access. These are intelligent radios having the capability of sensing the radio environment and reconfiguring the operating parameters dynamically. ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

DIVANet '15: Proceedings of the 5th ACM Symposium on Development and Analysis of Intelligent Vehicular Networks and Applications

November 2015

124 pages

ISBN:9781450337601

DOI:10.1145/2815347

General Chair:
Robson E. De Grande
NSERC DIVA Research Centre, Canada
,
Program Chairs:
Mirela S.M.A. Notare
Faculdade de Tecnologia em Transporte Aéreo, Brazil
,
Amir Darehshoorzadeh
NSERC DIVA Research Centre, Canada

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGSIM: ACM Special Interest Group on Simulation and Modeling

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 November 2015

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

This work was supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC) DIVA Network.

Conference

MSWiM'15

Sponsor:

SIGSIM

MSWiM'15: 18th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems

November 2 - 6, 2015

Cancun, Mexico

Acceptance Rates

Overall Acceptance Rate 70 of 308 submissions, 23%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

21
Total Citations
View Citations
262
Total Downloads

Downloads (Last 12 months)3
Downloads (Last 6 weeks)0

Reflects downloads up to 18 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

El-haryqy NMadini ZZouine Y(2024)A review of deep learning techniques for enhancing spectrum sensing and prediction in cognitive radio systems: approaches, datasets, and challengesInternational Journal of Computers and Applications10.1080/1206212X.2024.241404246:12(1104-1128)Online publication date: 21-Oct-2024
https://doi.org/10.1080/1206212X.2024.2414042
Idris MAhmedy ISoon TYahuza MTambuwal AAli U(2024)Cognitive radio and machine learning modalities for enhancing the smart transportation system: A systematic literature reviewICT Express10.1016/j.icte.2024.05.001Online publication date: May-2024
https://doi.org/10.1016/j.icte.2024.05.001
Patel DKavaiya SDing ZGuan YSun S(2022)Impact of Primary User Activity Statistics in Cognitive Vehicular NetworksIEEE Transactions on Vehicular Technology10.1109/TVT.2021.313825371:3(2859-2873)Online publication date: Mar-2022
https://doi.org/10.1109/TVT.2021.3138253
Smith PLuong ASarkar SSingh HSingh APatwari NKasera SDerr K(2021)A Novel Software Defined Radio for Practical, Mobile Crowd-sourced Spectrum SensingIEEE Transactions on Mobile Computing10.1109/TMC.2021.3107409(1-1)Online publication date: 2021
https://doi.org/10.1109/TMC.2021.3107409
Furqan HSolaija MTurkmen HArslan H(2021)Wireless Communication, Sensing, and REM: A Security PerspectiveIEEE Open Journal of the Communications Society10.1109/OJCOMS.2021.30540662(287-321)Online publication date: 2021
https://doi.org/10.1109/OJCOMS.2021.3054066
Zargarzadeh SMoghim NGhahfarokhi B(2021)A consensus-based cooperative Spectrum sensing technique for CR-VANETPeer-to-Peer Networking and Applications10.1007/s12083-020-01053-7Online publication date: 3-Jan-2021
https://doi.org/10.1007/s12083-020-01053-7
Gao YXiao YWu MXiao MShao J(2019)Dynamic Social-Aware Peer Selection for Cooperative Relay Management With D2D CommunicationsIEEE Transactions on Communications10.1109/TCOMM.2019.289413867:5(3124-3139)Online publication date: May-2019
https://doi.org/10.1109/TCOMM.2019.2894138
Gao YTang WWu MYang PDan L(2019)Dynamic Social-Aware Computation Offloading for Low-Latency Communications in IoTIEEE Internet of Things Journal10.1109/JIOT.2019.29092996:5(7864-7877)Online publication date: Oct-2019
https://doi.org/10.1109/JIOT.2019.2909299
Li HGu YChen JPei Q(2019)Speed Adjustment Attack on Cooperative Sensing in Cognitive Vehicular NetworksIEEE Access10.1109/ACCESS.2019.29216047(75925-75934)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2921604
Khattab AElgaml NMourad H(2019)Single‐channel slotted contention in cognitive radio vehicular networksIET Communications10.1049/iet-com.2018.517413:8(1078-1089)Online publication date: May-2019
https://doi.org/10.1049/iet-com.2018.5174
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten