Distributed Detection and Data Fusion
Distributed Detection and Data Fusion
Wireless Communications: Principles and Practice
Wireless Communications: Principles and Practice
Wireless Communications
Cognitive Wireless Communication Networks
Cognitive Wireless Communication Networks
Asynchronous in-network prediction: Efficient aggregation in sensor networks
ACM Transactions on Sensor Networks (TOSN)
In-band spectrum sensing in cognitive radio networks: energy detection or feature detection?
Proceedings of the 14th ACM international conference on Mobile computing and networking
ACM SIGMOBILE Mobile Computing and Communications Review
Dynamic Spectrum Access and Management in Cognitive Radio Networks
Dynamic Spectrum Access and Management in Cognitive Radio Networks
EURASIP Journal on Advances in Signal Processing - Special issue on advanced signal processing for cognitive radio networks
SP '10 Proceedings of the 2010 IEEE Symposium on Security and Privacy
A test framework for secure distributed spectrum sensing in cognitive radio networks
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part II
Cooperative Spectrum Sensing in Cognitive Radio, Part II: Multiuser Networks
IEEE Transactions on Wireless Communications
Cognitive radio: brain-empowered wireless communications
IEEE Journal on Selected Areas in Communications
Defense against Primary User Emulation Attacks in Cognitive Radio Networks
IEEE Journal on Selected Areas in Communications
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Secure distributed spectrum sensing (DSS) schemes in cognitive radio networks (CRNs) have been proposed to reliably detect the signals of the primary users (PUs) even when compromised nodes generate forged sensing reports. However, they have limited sensing accuracy for PU signals because of the absence of exact signal patterns of PUs. This is caused by Federal Communications Commission restriction on (no modification of) PUs, and thus, the CRNs cannot communicate with the PUs to obtain such patterns. We propose a verification framework utilizing primary user emulation signals that can be applied to existing DSS schemes to address this challenge. This will reinforce the robustness against forged sensing values. We then develop a concrete verification scheme based on this framework and an existing secure DSS scheme. We evaluate our approach via in-depth simulation and analysis compared with the existing scheme. Results show that our approach improves sensing accuracy and fusion speed in the cases of attack. Copyright © 2011 John Wiley & Sons, Ltd.