An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
IEEE Transactions on Computers
Telos: enabling ultra-low power wireless research
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Boundary recognition in sensor networks by topological methods
Proceedings of the 12th annual international conference on Mobile computing and networking
Cognitive PHY and MAC layers for dynamic spectrum access and sharing of TV bands
TAPAS '06 Proceedings of the first international workshop on Technology and policy for accessing spectrum
Performance of power detector sensors of DTV signals in IEEE 802.22 WRANs
TAPAS '06 Proceedings of the first international workshop on Technology and policy for accessing spectrum
Rendered path: range-free localization in anisotropic sensor networks with holes
Proceedings of the 13th annual ACM international conference on Mobile computing and networking
Computational Geometry: Algorithms and Applications
Computational Geometry: Algorithms and Applications
Fault tolerant multiple event detection in a wireless sensor network
Journal of Parallel and Distributed Computing
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
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Spectrum sensing is one of the key enabling technologies in Cognitive Radio Networks (CRNs). In CRNs, secondary users (SUs) are allowed to exploit the spectrum opportunities by sensing and accessing the spectrum, which exhibit many critical limitations in practical environments. In this paper, we propose a new sensing service model that uses dedicated wireless spectrum sensor networks (WSSN) for spectrum sensing. The major challenge in WSSN is the design of data fusion, for which the traditional fusion scheme will produce a large amount of errors. We formulate the problem as a boundary detection problem with notable unknown erroneous inputs. To solve the problem, we propose a novel cooperative boundary detection scheme that intelligently incorporates the cooperative spectrum sensing concept and the recent advances in support vector machine (SVM). Cooperative boundary detection consists of two major components, a declaration calibration algorithm and a boundary derivation algorithm. We prove that cooperative spectrum sensing can asymptotically approach the optimal solution. A prototype system as well as simulation experiments show that compared with the traditional approaches, cooperative boundary detection can reduce the errors by up to 95% with an average reduction about 85%.