scholarly journals Fuzzy-based reliable and secure cooperative spectrum sensing for the smart grid

2021 ◽  
Vol 8 (2) ◽  
pp. 92-100
Author(s):  
Laila Nassef ◽  
Reemah Alhebshi ◽  
Keyword(s):  
Cognitive Radio ◽  
Smart Grid ◽  
Spectrum Sensing ◽  
Cooperative Spectrum Sensing ◽  
Probability Of Detection ◽  
Fusion Center ◽  
Secondary Users ◽  
Sensing Data ◽  
Spectrum Detection ◽  
Fuzzy Fusion

Cognitive radio is a promising technology to solve the spectrum scarcity problem caused by inefficient utilization of radio spectrum bands. It allows secondary users to opportunistically access the underutilized spectrum bands assigned to licensed primary users. The local individual spectrum detection is inefficient, and cooperative spectrum sensing is employed to enhance spectrum detection accuracy. However, cooperative spectrum sensing opens up opportunities for new types of security attacks related to the cognitive cycle. One of these attacks is the spectrum sensing data falsification attack, where malicious secondary users send falsified sensing reports about spectrum availability to mislead the fusion center. This internal attack cannot be prevented using traditional cryptography mechanisms. To the best of our knowledge, none of the previous work has considered both unreliable communication environments and the spectrum sensing data falsification attack for cognitive radio based smart grid applications. This paper proposes a fuzzy inference system based on four conflicting descriptors. An attack model is formulated to determine the probability of detection for both honest and malicious secondary users. It considers four independent malicious secondary users’ attacking strategies of always yes, always no, random, and opposite attacks. The performance of the proposed fuzzy fusion system is simulated and compared with the conventional fusion rules of AND, OR, Majority, and the reliable fuzzy fusion that does not consider the secondary user’s sensing reputation. The results indicate that incorporating sensing reputation in the fusion center has enhanced the accuracy of spectrum detection and have prevented malicious secondary users from participating in the spectrum detection fusion

A SSDF-Attack-Exclusion Cooperative Spectrum Sensing Scheme in Cognitive Radio Networks

2014 ◽  
Vol 556-562 ◽  
pp. 5219-5222
Author(s):  
Wei Wu ◽  
Xiao Fei Zhang ◽  
Xiao Ming Chen
Keyword(s):  
Cognitive Radio ◽  
Utility Function ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Cooperative Spectrum Sensing ◽  
Radio Networks ◽  
Fusion Center ◽  
Sensing Data ◽  
Single User ◽  
Simulation Results

Compared with the single user spectrum sensing, cooperative spectrum sensing is a promising way to improve the detection precision. However, cooperative spectrum sensing is vulnerable to a variety of attacks, such as the spectrum sensing data falsification attack (SSDF attack). In this paper, we propose a concise cooperative spectrum sensing scheme based on a reliability threshold. We analyze the utility function of SSDF attacker in this scheme, and present the least reliability threshold for the fusion center against SSDF attack. Simulation results show that compared with the traditional cooperative spectrum sensing scheme, the SSDF attacker has a much lower utility in our proposed scheme, which drives it not to attack any more.

A Novel Weighted-Cooperative Spectrum Sensing Scheme Using Clustering

2012 ◽  
Vol 195-196 ◽  
pp. 277-282
Author(s):  
Hui Heng Liu ◽  
Wei Chen
Keyword(s):  
Spectrum Sensing ◽  
Cooperative Spectrum Sensing ◽  
Cluster Head ◽  
Probability Of Detection ◽  
Fusion Center ◽  
Secondary Users ◽  
Probability Of Error ◽  
Radio System ◽  
Simulation Results ◽  
Observation Results

This paper proposes a novel weighted-cooperative spectrum sensing scheme using clustering for cognitive radio system. We firstly classify the secondary users into a few clusters according to several existent methods, and then use cluster-head to collect the observation results come from different secondary users in the same cluster and make a cluster-decision. Considering the different distances between the clusters and the fusion center, different weightings are used to weight the cluster-decisions before combining. The simulation results show that our proposed method improve the probability of detection and reduce the probability of error.

scholarly journals Energy-Efficient Cluster-Based Cooperative Spectrum Sensing in a Multiple Antenna Cognitive Radio Network

2021 ◽  
pp. 176-185
Author(s):  
Samson I. Ojo ◽  
◽  
Zachaeus K. Adeyemo ◽  
Damilare O. Akande ◽  
Ayobami O. Fawole
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Energy Efficient ◽  
Cooperative Spectrum Sensing ◽  
Cluster Head ◽  
Fusion Rule ◽  
Probability Of Detection ◽  
Multiple Antenna ◽  
Secondary Users ◽  
Sensing Time

Spectrum Hole Detection (SHD) is a major operation in a Cognitive Radio (CR) network to identify empty spectrum for maximum utilization. However, SHD is often affected by multipath effects resulting in interference. The existing techniques used to address these problems are faced by poor detection rate, long sensing time and bandwidth inefficiency. Hence, this paper proposes a cluster-based Energy-Efficient Multiple Antenna Cooperative Spectrum Sensing (EEMACSS) for SHD in CR networks using Energy Detector (ED) with a modified combiner. Multiple secondary users are used to carry out local sensing using ED in multiple antenna configurations. The local sensing results are combined at the cluster head using majority fusion rule to determine the sensing results at each cluster. The sensing results from individual cluster are combined to determine the global sensing result using OR fusion rule. The proposed EEMACSS is evaluated using Probability of Detection (PD), Sensing Time (ST) and Spectral Efficiency (SE) by comparing with existing techniques. The results reveal that the proposed technique shows better performance.

scholarly journals An SETM Algorithm for Combating SSDF Attack in Cognitive Radio Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
S. Tephillah ◽  
J. Martin Leo Manickam
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Trust Management ◽  
Cooperative Spectrum Sensing ◽  
Radio Networks ◽  
Second Phase ◽  
Common Channel ◽  
Secondary Users ◽  
Management Algorithm ◽  
Different Types

Security is a pending challenge in cooperative spectrum sensing (CSS) as it employs a common channel and a controller. Spectrum sensing data falsification (SSDF) attacks are challenging as different types of attackers use them. To address this issue, the sifting and evaluation trust management algorithm (SETM) is proposed. The necessity of computing the trust for all the secondary users (SUs) is eliminated based on the use of the first phase of the algorithm. The second phase is executed to differentiate the random attacker and the genuine SUs. This reduces the computation and overhead costs. Simulations and complexity analyses have been performed to prove the efficiency and appropriateness of the proposed algorithm for combating SSDF attacks.

scholarly journals Cooperative Spectrum Sensing Based on HML and Vector Quantization for Cognitive Radio Networks

2018 ◽  
Vol 7 (2.20) ◽  
pp. 335
Author(s):  
Shweta Alpna ◽  
Amrit Mukherjee ◽  
Amlan Datta
Keyword(s):  
Communication Network ◽  
Cognitive Radio ◽  
Real Time ◽  
Spectrum Sensing ◽  
Vector Quantization ◽  
Cooperative Spectrum Sensing ◽  
Radio Networks ◽  
Secondary Users ◽  
The Real ◽  
Novel Technique

The proposed work illustrates a novel technique for cooperative spectrum sensing in a cognitive radio (CR) network. The work includes an approach of identifying secondary users (SUs) based on Hierarchical Maximum Likelihood (HML) technique followed by Vector Quantization. Initially, the arrangement of the SUs are been observed using HML with respect to a spatial domain and then the active SUs among them are identified using VQ. The approach will not only save the energy, but the decision of the real-time and dynamic cooperative communication network becomes more accurate as we can predict the behavior of SUs movement and spectrum sensing by each individual SU at that particular  place. The results and simulations of the real-time experiment justifies with the proposed approach. 

Deep Cooperative Spectrum Sensing Utilizing Recurrent Convolutional Neural Networks

2020 ◽  
Author(s):  
Rahil Sarikhani ◽  
Farshid Keynia
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Recurrent Neural Network ◽  
Cooperative Spectrum Sensing ◽  
Primary User ◽  
Secondary Users ◽  
Spectrum Holes

Abstract Cognitive Radio (CR) network was introduced as a promising approach in utilizing spectrum holes. Spectrum sensing is the first stage of this utilization which could be improved using cooperation, namely Cooperative Spectrum Sensing (CSS), where some Secondary Users (SUs) collaborate to detect the existence of the Primary User (PU). In this paper, to improve the accuracy of detection Deep Learning (DL) is used. In order to make it more practical, Recurrent Neural Network (RNN) is used since there are some memory in the channel and the state of the PUs in the network. Hence, the proposed RNN is compared with the Convolutional Neural Network (CNN), and it represents useful advantages to the contrast one, which is demonstrated by simulation.

scholarly journals A Double Adaptive Approach to Tackle Malicious Users in Cognitive Radio Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Muhammad Sajjad Khan ◽  
Muhammad Jibran ◽  
Insoo Koo ◽  
Su Min Kim ◽  
Junsu Kim
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Cooperative Spectrum Sensing ◽  
Evidence Theory ◽  
Adaptive Threshold ◽  
Radio Networks ◽  
Maximal Ratio Combining ◽  
Fusion Center ◽  
Adaptive Approach

Cognitive radio (CR) is being considered as a vital technology to provide solution to spectrum scarcity in next generation network, by efficiently utilizing the vacant spectrum of the licensed users. Cooperative spectrum sensing in cognitive radio network has a promising performance compared to the individual sensing. However, the existence of the malicious users’ attack highly degrades the performance of the cognitive radio networks by sending falsified data also known as spectrum sensing data falsification (SSDF) to the fusion center. In this paper, we propose a double adaptive thresholding technique in order to differentiate legitimate users from doubtful and malicious users. Prior to the double adaptive approach, the maximal ratio combining (MRC) scheme is utilized to assign weight to each user such that the legitimate users experience higher weights than the malicious users. Double adaptive threshold is applied to give a fair chance to the doubtful users to ensure their credibility. A doubtful user that fails the double adaptive threshold test is declared as a malicious user. The results of the legitimate users are combined at the fusion center by utilizing Dempster-Shafer (DS) evidence theory. Effectiveness of the proposed scheme is proved through simulations by comparing with the existing schemes.

An Improved SNR-Based Cooperative Spectrum Sensing in Cognitive Radio Networks

2014 ◽  
Vol 631-632 ◽  
pp. 874-877
Author(s):  
Jie Guo ◽  
Yan Gu ◽  
Da Hai Jing
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Network ◽  
Cooperative Spectrum Sensing ◽  
New Technology ◽  
Radio Networks ◽  
Fusion Center ◽  
Detector Performance ◽  
Performance Simulation ◽  
Optimal Detector

Spectrum sensing is a new technology in cognitive radio network, whose main purpose is to design an optimal detector. This paper studies a soft linear cooperative spectrum sensing method. We propose a SNR-based algorithm for weight setting at fusion center to improve the detector performance. Simulation results shows that the SNR-based method has better detector performance than the others.

scholarly journals Reliable Machine Learning Based Spectrum Sensing in Cognitive Radio Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Hurmat Ali Shah ◽  
Insoo Koo
Keyword(s):  
Machine Learning ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Posterior Probability ◽  
Nearest Neighbor ◽  
Probability Of Detection ◽  
Fusion Center ◽  
Training Phase ◽  
K Nearest Neighbor ◽  
Combination Scheme

Spectrum sensing is of crucial importance in cognitive radio (CR) networks. In this paper, a reliable spectrum sensing scheme is proposed, which uses K-nearest neighbor, a machine learning algorithm. In the training phase, each CR user produces a sensing report under varying conditions and, based on a global decision, either transmits or stays silent. In the training phase the local decisions of CR users are combined through a majority voting at the fusion center and a global decision is returned to each CR user. A CR user transmits or stays silent according to the global decision and at each CR user the global decision is compared to the actual primary user activity, which is ascertained through an acknowledgment signal. In the training phase enough information about the surrounding environment, i.e., the activity of PU and the behavior of each CR to that activity, is gathered and sensing classes formed. In the classification phase, each CR user compares its current sensing report to existing sensing classes and distance vectors are calculated. Based on quantitative variables, the posterior probability of each sensing class is calculated and the sensing report is classified into either representing presence or absence of PU. The quantitative variables used for calculating the posterior probability are calculated through K-nearest neighbor algorithm. These local decisions are then combined at the fusion center using a novel decision combination scheme, which takes into account the reliability of each CR user. The CR users then transmit or stay silent according to the global decision. Simulation results show that our proposed scheme outperforms conventional spectrum sensing schemes, both in fading and in nonfading environments, where performance is evaluated using metrics such as the probability of detection, total probability of error, and the ability to exploit data transmission opportunities.

scholarly journals An Optimized and Trained Model of Cooperative Sensing for Cognitive Radio Networks

2019 ◽  
Vol 8 (3) ◽  
pp. 5176-5182
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cooperative Spectrum Sensing ◽  
Search Algorithm ◽  
Decision Threshold ◽  
Secondary Users ◽  
Communication Problems ◽  
Local Decision ◽  
Huge Impact ◽  
Radio Technology

Sensing based spectrum allocation is one of the solutions to bridge the gap between spectrum scarcity and underutilization of allocated spectrum. In this context, cognitive radio technology has become the prominent solution for future wireless communication problems. To accurately detect the spectrum availability, CRN uses cooperative spectrum sensing where N number of selected nodes will be involved in making a decision on spectrum occupation. Various sensing parameters such as sensing duration (τ), decision threshold (λ), number of nodes (N) and decision rule (K) have huge impact on the performance of cooperative spectrum sensing. In addition, there are constraints on energy consumption and protection of licensed user’s needs to be considered. Our work focuses on optimization of sensing parameters to maximize the throughput of the cognitive radio network maintaining the energy efficiency and protecting the licensed users from the interference caused by the secondary users. The proposed work uses convex optimization to optimize sensing duration and two-dimensional search algorithm to find the values N and K. Further optimization is done by comparing local decision with cooperative decision.

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