An Energy - Efficient Approach for Restoring the Coverage Area During Sensor Node Failure

Bharathy, G. T.; Bhavanisankari, S.; Tamilselvi, T.; Bhargavi, G.

doi:10.1007/978-3-030-33846-6_63

Cited by 4 publications

(1 citation statement)

References 6 publications

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“…A collaborative spectrum sensing for CR network is explained in [3]. Detailed analysis and comparison of various spectrum sensing techniques and have presented a scheme for coverage area restoration when the failure of nodes is presented in [4,5]. A novel GIID for cooperative cognitive networks is designed in [6].…”

Section: Related Workmentioning

confidence: 99%

Efficient Centralized Cooperative Spectrum Sensing Techniques for Cognitive Networks

Gnanasivam¹,

Bharathy²,

Rajendran³

et al. 2023

Computer Systems Science and Engineering

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Wireless Communication is a system for communicating information from one point to other, without utilizing any connections like wire, cable, or other physical medium. Cognitive Radio (CR) based systems and networks are a revolutionary new perception in wireless communications. Spectrum sensing is a vital task of CR to avert destructive intrusion with licensed primary or main users and discover the accessible spectrum for the efficient utilization of the spectrum. Centralized Cooperative Spectrum Sensing (CSS) is a kind of spectrum sensing. Most of the test metrics designed till now for sensing the spectrum is produced by using the Sample Covariance Matrix (SCM) of the received signal. Some of the methods that use the SCM for the process of detection are Pietra-Ricci Index Detector (PRIDe), Hadamard Ratio (HR) detector, Gini Index Detector (GID), etc. This paper presents the simulation and comparative performance analysis of PRIDe with various other detectors like GID, HR, Arithmetic to Geometric Mean (AGM), Volume-based Detector number 1 (VD1), Maximumto-Minimum Eigenvalue Detection (MMED), and Generalized Likelihood Ratio Test (GLRT) using the MATLAB software. The PRIDe provides better performance in the presence of variations in the power of the signal and the noise power with less computational complexity.

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