One‐to‐Many Relationship Based Kullback Leibler Divergence against Malicious Users in Cooperative Spectrum Sensing

Gul, Noor; Qureshi, Ijaz Mansoor; Akbar, Sadiq; Kamran, Muhammad; Rasool, Imtiaz

doi:10.1155/2018/3153915

Cited by 17 publications

(11 citation statements)

References 44 publications

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“…As a result, based on the flexible sensing time slot, τ p s using the KLD award score, each CR-IoT user to sense the PU licensed spectrum more efficiently. Now, we can calculate the KLD award score, δ of the proposed ED method using the flexible sensing time slot, τ p s with interference constraints for scenario II which is defined as between the two normally distributed functions f (ψ) and f (ψ) [5,50,51,59] as follows:…”

Section: Analysis Of Flexible Sensing Timementioning

confidence: 99%

“…In [50], the authors used the Kullback-Leibler divergence (KLD) technique to calculate the weight value for each CR-IoT user according to their local sensing result and the FC uses the local sensing results and weight value of each CR-IoT user to make the final global decision about the appearance and non-appearance of the PU's signal in CR-IoT networks. In [51], each CR-IoT generates their local spectrum sensing result about the appearance and non-appearance of the PU's signal in the network. The FC collected the local detection results.…”

Section: Introductionmentioning

confidence: 99%

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An Energy Efficient Cooperative Spectrum Sensing for Cognitive Radio-Internet of Things with Interference Constraints

Miah

et al. 2021

Preprint

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Spectrum sensing plays a very important role in Cognitive Radio based Internet of Things (CR-IoT) networks for utilization of the licensed spectrum accurately. However, the performance of the conventional Energy Detector (ED) method is compromised in a noise-uncertain environment owing to interference constraints, i.e. the CR-IoT user interference with the licensed Primary User (PU) on the same licensed band. To overcome this drawback, we proposed an energy efficient Cooperative Spectrum Sensing (CSS) for a CR-IoT network with interference constraints using a novel ED method. In this method, each CR-IoT user is capable of spectrum sensing that makes both the local decision and the weight factor based on the sequential approach; we calculate the weight factor against each CR-IoT user based on the Kullback Leibler Divergence award score. After the local decision and the weight factor are made, each CR-IoT user transmits its measured both the local decisions, and the weight factor to a Fusion Center (FC), which is made a final decision about the PU activities based on the hard fusion rule. The simulation results demonstrates that the proposed ED method obtains an improved detection performance, an enhanced sum rate, a spectral efficiency, an energy efficiency, and a lower global error probability when compared to other conventional ED methods under time varying environments.

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Section: Analysis Of Flexible Sensing Timementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Energy Efficient Cooperative Spectrum Sensing for Cognitive Radio-Internet of Things with Interference Constraints

Miah

et al. 2021

Preprint

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“…Many approaches have been reported to defense MUs attack based on weight assigned in recent literature. In [10], according to the difference between the energy of CU and the average energy of all CUs, each CU was given a Kullback Leibler divergence (KLD) score to assign weights for the sensing reports of CUs before sensing data fusion at the FC. The MUs were assigned low weights, whereas the HUs were assigned high weights.…”

Section: Related Workmentioning

confidence: 99%

“…However, such existing techniques of against MUs are limit to some unrealistic assumptions that are easily violated in future or realistic spectrum sensing: 1) The attack ways are assumed to be identical and fixed in [9]. 2) The underlying distribution of PU signal and noise are assumed to be known [10]. Both of assumptions are easily violated in realistic spectrum sensing.…”

Section: Introductionmentioning

confidence: 99%

Clustering Algorithm-Based Data Fusion Scheme for Robust Cooperative Spectrum Sensing

et al. 2020

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In a centralized cooperative spectrum sensing (CSS) system, it is vulnerable to malicious users (MUs) sending fraudulent sensing data, which can severely degrade the performance of CSS system. To solve this problem, we propose sensing data fusion schemes based on K-medoids and Mean-shift clustering algorithms to resist the MUs sending fraudulent sensing data in this paper. The cognitive users (CUs) send their local energy vector (EVs) to the fusion center which fuses these EVs as an EV with robustness by the proposed data fusion method. Specifically, this method takes a Medoids of all EVs as an initial value and searches for a high-density EV by iteratively as a representative statistical feature which is robust to malicious EVs from MUs. It does not need to distinguish MUs from CUs in the whole CSS process and considers constraints imposed by the CSS system such as the lack of information of PU and the number of MUs. Furthermore, we propose a global decision framework based on fast K-medoids or Mean-shift clustering algorithm, which is unaware of the distributions of primary user (PU) signal and environment noise. It is worth noting that this framework can avoid the derivation of threshold. The simulation results reflect the robustness of our proposed CSS scheme. INDEX TERMS Cognitive radio, robust cooperative spectrum sensing, sensing data fusion, K-medoids clustering algorithm, Mean-shift clustering algorithm.

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“…In [36,37], the authors used the KLD technique to evaluate the dissimilarity in the probability distribution functions under the presence and absence hypotheses of the PU's signal. In [38], each CR-IoT user provided an FC with information about their local spectrum observations of the licensed spectrum. The FC collected the local sensing results and made its global decision.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Sensing and Sum-Rate Analysis in a Cognitive Radio-Based Internet of Things

Miah

Ahmed

Islam

et al. 2020

Sensors

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Spectrum sensing plays a vital role in cognitive radio networks (CRNs) for identifying the spectrum hole. However, an individual cognitive radio user in a CRN does not obtain sufficient sensing performance and sum rate of the primary and secondary links to support the future Internet of Things (IoT) using conventional detection techniques such as the energy detection (ED) technique in a noise-uncertain environment. In an environment comprising noise uncertainty, the performance of conventional energy detection techniques is significantly degraded owing to the noise fluctuation caused by the noise temperature, interference, and filtering. To mitigate this problem, we present a cooperative spectrum sensing technique that comprises the use of the Kullback–Leibler divergence (KLD) in cognitive radio-based IoT (CR-IoT). In the proposed method, each unlicensed IoT device that is capable of spectrum sensing, which is called a CR-IoT user, makes a local decision using the KLD technique. The spectrum sensing performed with the KLD requires a smaller number of samples than other conventional approaches, e.g., energy detection, for reliable sensing even in a noise uncertain environment. After the local decision is made, each CR-IoT user sends its own local decision result to the corresponding fusion center, which makes a global decision using the soft fusion rule. The results obtained through simulations show that the proposed KLD scheme achieves a better sensing performance, i.e., higher detection and lower false-alarm probabilities, enhances the sum rate, and reduces the total time as compared to the conventional ED scheme under various fading channels.

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One‐to‐Many Relationship Based Kullback Leibler Divergence against Malicious Users in Cooperative Spectrum Sensing

Cited by 17 publications

References 44 publications

An Energy Efficient Cooperative Spectrum Sensing for Cognitive Radio-Internet of Things with Interference Constraints

An Energy Efficient Cooperative Spectrum Sensing for Cognitive Radio-Internet of Things with Interference Constraints

Clustering Algorithm-Based Data Fusion Scheme for Robust Cooperative Spectrum Sensing

Enhanced Sensing and Sum-Rate Analysis in a Cognitive Radio-Based Internet of Things

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