scholarly journals Boosted Trees Algorithm as Reliable Spectrum Sensing Scheme in the Presence of Malicious Users

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 1038 ◽  
Author(s):  
Noor Gul ◽  
Muhammad Sajjad Khan ◽  
Su Min Kim ◽  
Junsu Kim ◽  
Atif Elahi ◽  
...  
Keyword(s):  
Spectrum Sensing ◽  
Cooperative Spectrum Sensing ◽  
Decision Fusion ◽  
Machine Learning Techniques ◽  
Soft Decision ◽  
Secondary Users ◽  
Sensing Data ◽  
Learning Techniques ◽  
Higher Weights ◽  
Sensing Performance

Cooperative spectrum sensing (CSS) has the ability to accurately identify the activities of the primary users (PUs). As the secondary users’ (SUs) sensing performance is disturbed in the fading and shadowing environment, therefore the CSS is a suitable choice to achieve better sensing results compared to individual sensing. One of the problems in the CSS occurs due to the participation of malicious users (MUs) that report false sensing data to the fusion center (FC) to misguide the FC’s decision about the PUs’ activity. Out of the different categories of MUs, Always Yes (AY), Always No (AN), Always Opposite (AO) and Random Opposite (RO) are of high interest these days in the literature. Recently, high sensing performance for the CSS can be achieved using machine learning techniques. In this paper, boosted trees algorithm (BTA) has been proposed for obtaining reliable identification of the PU channel, where the SUs can access the PU channel opportunistically with minimum disturbances to the licensee. The proposed BTA mitigates the spectrum sensing data falsification (SSDF) effects of the AY, AN, AO and RO categories of the MUs. BTA is an ensemble method for solving spectrum sensing problems using different classifiers. It boosts the performance of some weak classifiers in the combination by giving higher weights to the weak classifiers’ sensing decisions. Simulation results verify the performance improvement by the proposed algorithm compared to the existing techniques such as genetic algorithm soft decision fusion (GASDF), particle swarm optimization soft decision fusion (PSOSDF), maximum gain combination soft decision fusion (MGCSDF) and count hard decision fusion (CHDF). The experimental setup is conducted at different levels of the signal-to-noise ratios (SNRs), total number of cooperative users and sensing samples that show minimum error probability results for the proposed scheme.

scholarly journals A Genetic Algorithm-Based Soft Decision Fusion Scheme in Cognitive IoT Networks with Malicious Users

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Muhammad Sajjad Khan ◽  
Noor Gul ◽  
Junsu Kim ◽  
Ijaz Mansoor Qureshi ◽  
Su Min Kim
Keyword(s):  
Genetic Algorithm ◽  
Spectrum Sensing ◽  
Dynamic Spectrum Access ◽  
Cooperative Spectrum Sensing ◽  
Decision Fusion ◽  
Weighting Coefficient ◽  
Soft Decision ◽  
Secondary Users ◽  
Coefficient Vector ◽  
Fusion Scheme

Internet of Things (IoT) is a new challenging paradigm for connecting a variety of heterogeneous networks. Since its introduction, many researchers have been studying how to efficiently exploit and manage spectrum resource for IoT applications. An explosive increase in the number of IoT devices accelerates towards the future-connected society but yields a high system complexity. Cognitive radio (CR) technology is also a promising candidate for future wireless communications. CR via dynamic spectrum access provides opportunities to secondary users (SUs) to access licensed spectrum bands without interfering primary users by performing spectrum sensing before accessing available spectrum bands. However, multipath effects can degrade the sensing capability of an individual SU. Therefore, for more precise sensing, it is helpful to exploit multiple collaborative sensing users. The main problem in cooperative spectrum sensing is the presence of inaccurate sensing information received from the multipath-affected SUs and malicious users at a fusion center (FC). In this paper, we propose a genetic algorithm-based soft decision fusion scheme to determine the optimum weighting coefficient vector against SUs’ sensing information. The weighting coefficient vector is further utilized in a soft decision rule at FC in order to make a global decision. Through extensive simulations, the effectiveness of the proposed scheme is evaluated compared with other conventional schemes.

scholarly journals A Novel Semi-Soft Decision Scheme for Cooperative Spectrum Sensing in Cognitive Radio Networks

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2522 ◽  
Author(s):  
Yin Mi ◽  
Guangyue Lu ◽  
Yuxin Li ◽  
Zhiqiang Bao
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cooperative Spectrum Sensing ◽  
Transmission Band ◽  
Primary User ◽  
Detection Performance ◽  
Final Decision ◽  
Soft Decision ◽  
Hidden Terminal ◽  
Sensing Performance

Spectrum sensing (SS) is an essential part of cognitive radio (CR) technology, and cooperative spectrum sensing (CSS) could efficiently improve the detection performance in environments with fading and shadowing effects, solving hidden terminal problems. Hard and Soft decision detection are usually employed at the fusion center (FC) to detect the presence or absence of the primary user (PU). However, soft decision detection achieves better sensing performance than hard decision detection at the expense of the local transmission band. In this paper, we propose a tradeoff scheme between the sensing performance and band cost. The sensing strategy is designed based on three modules. Firstly, a local detection module is used to detect the PU signal by energy detection (ED) and send decision results in terms of 1-bit or 2-bit information. Secondly, and most importantly, the FC estimates the received decision data through a data reconstruction module based on the statistical distribution such that the extra thresholds are not needed. Finally, a global decision module is in charge of fusing the estimated data and making a final decision. The results from a simulation show that the detection performance of the proposed scheme outperforms that of other algorithms. Moreover, savings on the transmission band cost can be made compared with soft decision detection.

Smart contract-based secure cooperative spectrum sensing algorithm

2021 ◽  
Vol 17 (12) ◽  
pp. 155014772110586
Author(s):  
Chu Ji ◽  
Qi Zhu
Keyword(s):  
Spectrum Sensing ◽  
Cooperative Spectrum Sensing ◽  
Base Station ◽  
Secondary User ◽  
Secondary Users ◽  
Sensing Data ◽  
Blockchain Technology ◽  
Smart Contract ◽  
Voting Rule ◽  
Asymmetric Encryption

Spectrum sensing is the key technology of cognitive radio. In this article, we apply blockchain technology in spectrum sensing process and propose a related algorithm based on reputation. The algorithm builds a system model based on smart contract in blockchain and applies blockchain asymmetric encryption algorithm and digital signature technology in the process of secondary users’ transmitting local judgments to the secondary user base station. The algorithm can resist spectrum sensing data falsification (SSDF) attack launched by malicious users. This article comprehensively considers the channel error rate, detection probability, secondary user base station budget and remaining energy of the secondary users (SUs) and then establishes the SU’s utility function as well as the game model. By solving the Nash equilibrium, the SU determines whether it uploads sensing data. Finally, the SU base station selects registered SUs by calculating and updating their reputation, obtaining the final judgment by voting rule. With simulations, we prove that the algorithm proposed in this article increases the accuracy and security of spectrum sensing and can effectively resist SSDF attack.

scholarly journals One-to-Many Relationship Based Kullback Leibler Divergence against Malicious Users in Cooperative Spectrum Sensing

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Noor Gul ◽  
Ijaz Mansoor Qureshi ◽  
Sadiq Akbar ◽  
Muhammad Kamran ◽  
Imtiaz Rasool
Keyword(s):  
Spectrum Sensing ◽  
Cooperative Spectrum Sensing ◽  
Primary User ◽  
Performance Degradation ◽  
Fusion Center ◽  
Secondary Users ◽  
Malicious User ◽  
Leibler Divergence ◽  
Higher Weights ◽  
The Individual

The centralized cooperative spectrum sensing (CSS) allows unlicensed users to share their local sensing observations with the fusion center (FC) for sensing the licensed user spectrum. Although collaboration leads to better sensing, malicious user (MU) participation in CSS results in performance degradation. The proposed technique is based on Kullback Leibler Divergence (KLD) algorithm for mitigating the MUs attack in CSS. The secondary users (SUs) inform FC about the primary user (PU) spectrum availability by sending received energy statistics. Unlike the previous KLD algorithm where the individual SU sensing information is utilized for measuring the KLD, in this work MUs are identified and separated based on the individual SU decision and the average sensing statistics received from all other users. The proposed KLD assigns lower weights to the sensing information of MUs, while the normal SUs information receives higher weights. The proposed method has been tested in the presence of always yes, always no, opposite, and random opposite MUs. Simulations confirm that the proposed KLD scheme has surpassed the existing soft combination schemes in estimating the PU activity.

Efficient Cooperative Spectrum Sensing with Minimum Sensing Error Probability in Heterogeneous Cognitive Radio Networks

Frequenz ◽  
2012 ◽  
Vol 66 (7-8) ◽  
Author(s):  
Hang Hu ◽  
Ning Li ◽  
Youyun Xu
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Error Probability ◽  
Cooperative Spectrum Sensing ◽  
Radio Networks ◽  
Decision Threshold ◽  
Cognitive Network ◽  
Secondary Users ◽  
Sensing Performance

AbstractTo improve the sensing performance, cooperation among secondary users can be utilized to collect space diversity. We focus on the optimization of cooperative spectrum sensing in which multiple secondary users efficiently cooperate to achieve superior detection accuracy with minimum sensing error probability in heterogeneous cognitive radio (CR) networks. Rayleigh fading and Nakagami fading are considered respectively in cognitive network I and cognitive network II. For each cognitive network, we derive the optimal randomized rule for different decision threshold. Then, the optimal decision threshold is derived according to the rule of minimum sensing error (MSE). MSE rule shows better performance on improving the final false alarm and detection probability simultaneously. By simulations, our proposed strategy optimizes the sensing performance for each secondary user which is randomly distributed in the heterogeneous cognitive radio networks.

Sign in / Sign up

Export Citation Format

Share Document