Robust Spectrum Sensing With Crowd Sensors

Ding, Guoru; Wang, Jinlong; Wu, Qihui; Zhang, Linyuan; Zou, Yulong; Yao, Yu Dong; Chen, Yingying

doi:10.1109/tcomm.2014.2346775

Cited by 130 publications

(34 citation statements)

References 45 publications

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“…Certain methods have been proposed for improving the quality of data (QoD) in MCS, including estimation and prediction of sensing data, along with statistical processing for identifying and removing outliers in sensing values [4]. Data selection techniques are also used to filter low-quality or irrelevant data and to generate a high-quality dataset for further processing in IoT services [5]. Another approach is the use of a recruitment mechanism for selecting trustworthy users who are expected to contribute high-quality data.…”

Section: Introductionmentioning

confidence: 99%

Trust Evaluation Mechanism for User Recruitment in Mobile Crowd-Sensing in the Internet of Things

Truong

Lee

et al. 2019

IEEE Trans.Inform.Forensic Secur.

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Mobile Crowd-Sensing (MCS) has appeared as a prospective solution for large-scale data collection, leveraging built-in sensors and social applications in mobile devices that enables a variety of Internet of Things (IoT) services. However, the human involvement in MCS results in a high possibility for unintentionally contributing corrupted and falsified data or intentionally spreading disinformation for malevolent purposes, consequently undermining IoT services. Therefore, recruiting trustworthy contributors plays a crucial role in collecting highquality data and providing better quality of services while minimizing the vulnerabilities and risks to MCS systems. In this article, a novel trust model called Experience-Reputation (E-R) is proposed for evaluating trust relationships between any two mobile device users in a MCS platform. To enable the E-R model, virtual interactions among the users are manipulated by considering an assessment of the quality of contributed data from such users. Based on these interactions, two indicators of trust called Experience and Reputation are calculated accordingly. By incorporating the Experience and Reputation trust indicators (TIs), trust relationships between the users are established, evaluated and maintained. Based on these trust relationships, a novel trust-based recruitment scheme is carried out for selecting the most trustworthy MCS users to contribute to data sensing tasks. In order to evaluate the performance and effectiveness of the proposed trust-based mechanism as well as the E-R trust model, we deploy several recruitment schemes in a MCS testbed which consists of both normal and malicious users. The results highlight the strength of the trust-based scheme as it delivers better quality for MCS services while being able to detect malicious users. We believe that the trust-based user recruitment offers an effective capability for selecting trustworthy users for various MCS systems and, importantly, the proposed mechanism is practical to deploy in the real world.

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Section: Introductionmentioning

confidence: 99%

Trust Evaluation Mechanism for User Recruitment in Mobile Crowd-Sensing in the Internet of Things

Truong

Lee

et al. 2019

IEEE Trans.Inform.Forensic Secur.

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“…On the other hand, collaborative or cooperative spectrum sensing (CSS) is necessary to maintain secure communications among a group of SUs [18][19][20][21]. Recently, to defend against spectrum attacks such as the Byzantine failure problem, more general CSS techniques have been studied [22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

“…The exact detection and false alarm probabilities are also obtained in nonhomogeneous environments with noise uncertainty.On the other hand, collaborative or cooperative spectrum sensing (CSS) is necessary to maintain secure communications among a group of SUs [18][19][20][21]. Recently, to defend against spectrum attacks such as the Byzantine failure problem, more general CSS techniques have been studied [22][23][24][25].Among those CSS methods, the OR rule has been known to increase detection probability greatly in a Rayleigh fading channel. Thus, we employ the OR rule and analyze the detection and false alarm probabilities of data fusion based on the decision data from each SU.…”

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confidence: 99%

Robust spectrum sensing under noise uncertainty for spectrum sharing

et al. 2019

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Spectrum sensing plays an important role in spectrum sharing. Energy detection is generally used because it does not require a priori knowledge of primary user, (PU) signals; however, it is sensitive to noise uncertainty. An order statistics (OS) detector provides inherent protection against nonhomogeneous background signals. However, no analysis has been conducted yet to apply OS detection to spectrum sensing in a wireless channel to solve noise uncertainty. In this paper, we propose a robust spectrum sensing scheme based on generalized order statistics (GOS) and analyze the exact false alarm and detection probabilities under noise uncertainty. From the equation of the exact false alarm probability, the threshold value is calculated to maintain a constant false alarm rate. The detection probability is obtained from the calculated threshold under noise uncertainty. As a fusion rule for cooperative spectrum sensing, we adopt an OR rule, that is, a 1‐out‐of‐N rule, and we call the proposed scheme GOS‐OR. The analytical results show that the GOS‐OR scheme can achieve optimum performance and maintain the desired false alarm rates if the coefficients of the GOS‐OR detector can be correctly selected.

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“…In [8,9], reputation mechanism is considered in the cooperative spectrum sensing. While the smartphones have been popular, the robust spectrum sensing and cloud-based architecture has been studied, [10][11][12]. The spectrum assignment methods are also studied in [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

A Crowd Cooperative Spectrum Sensing Algorithm Using a Non-Ideal Channel

Zhu

2018

Algorithms

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Spectrum sensing is the prerequisite of the realization of cognitive radio. So it is a significant part of cognitive radio. In order to stimulate the SUs to sense the spectrum, we combine the incentive mechanism of crowd-sensing with cooperative spectrum sensing effectively, and put forward a crowd cooperative spectrum sensing algorithm with optimal utility of secondary users (SUs) under non-ideal channel which we define SUs' utility expectation functions related to rewards, sensing time and transmission power. Then, we construct the optimization problem of maximizing the utilities of SUs by optimizing the sensing time and the transmission power, and prove that this problem is a convex optimization problem. The optimal sensing time and transmission power are obtained by using the Karush-Kuhn-Tucker (KKT) conditions. The numerical simulation results show that the spectrum detection performance of algorithm, which we put forward, is improved.

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Robust Spectrum Sensing With Crowd Sensors

Cited by 130 publications

References 45 publications

Trust Evaluation Mechanism for User Recruitment in Mobile Crowd-Sensing in the Internet of Things

Trust Evaluation Mechanism for User Recruitment in Mobile Crowd-Sensing in the Internet of Things

Robust spectrum sensing under noise uncertainty for spectrum sharing

A Crowd Cooperative Spectrum Sensing Algorithm Using a Non-Ideal Channel

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