Trust-based mechanism design for cooperative spectrum sensing in cognitive radio networks

Ji, Wang; Chen, Ing-Ray; Tsai, Jeffrey J. P.; Wang, Ding-Chau

doi:10.1016/j.comcom.2017.11.010

Cited by 27 publications

(17 citation statements)

References 18 publications

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“…CSS improves detection performance where nodes in different spatial locations share their sensing reports with the FC to overcome uncertainties of individual decision. Either hard or soft fusion strategies can be used [37]. Local binary decisions are transmitted to FC to combine these sensing reports to evaluate performance indicators based on OR, AND, or majority voting strategies [38].…”

Section: Cooperative Hard Fusion In Harsh Environmentmentioning

confidence: 99%

Fuzzy based Reliable Cooperative Spectrum Sensing for Smart Grid Environment

Nassef¹,

Alhebshi²

2020

IJACSA

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The huge demand for spectrum has created immediate need to make available new licensed and/or unlicensed spectrum bands to satisfy the explosive growth of spectrum demands and to satisfy the quality of service requirements of diverse applications. Spectrum shortage and harsh environment have become a challenging bottleneck to achieve reliable communications in the smart grid. Cognitive radio is the emerging technology to achieve both spectrum and reliability awareness. Cooperative spectrum sensing takes advantage of spatial diversity to reduce the impact of receiver uncertainty. However, the harsh smart grid environments limit advantageous of cooperation due to variations of signal to noise ratio on which energy detection technique depends on. This paper proposes a reliable spectrum detection for a cluster based cooperative spectrum sensing in harsh smart grid environment, where cognitive cluster heads and a centralized cognitive radio based fusion center are deployed to solve both spectrum and reliability problems. The proposed fuzzy inference system is based on three fuzzy descriptors of energy difference, link quality, and local probability of detection. The results show the superiority of proposed fuzzy based fusion scheme to enhance accuracy of spectrum decision in harsh environment.

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Section: Cooperative Hard Fusion In Harsh Environmentmentioning

confidence: 99%

Fuzzy based Reliable Cooperative Spectrum Sensing for Smart Grid Environment

Nassef¹,

Alhebshi²

2020

IJACSA

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“…Under the always false attack scenario, the malicious SUs always report the opposite of their sensed outcomes. Under the always random attack scenario, the malicious SUs randomly generates a sensing result to report to the system [37]. There is no mechanism to control the behaviours of the SUs and this threatens the security of the licensed users.…”

Section: A User Recruitment In Mcsmentioning

confidence: 99%

Simulation of Trust-Based Mechanism for Enhancing User Confidence in Mobile Crowdsensing Systems

et al. 2020

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With the rapid development of mobile technology and subsequent mass adoption of mobile devices, mobile crowdsensing (MCS) has gained a lot of research attention. In MCS systems, trust is a key focus in the overall improvement in the participant uptake of the sensing tasks. The trust-based scheme of MCS is studied to predict the damage level, the scores of quality-of-service (QoS), and the levels of qualityof-data (QoD) of MCS systems. Users can participate in MCS sensing based on trustworthy indicators that are related to user experience and system reputation, as well as the knowledge obtained about the MCS systems. This paper illustrates the establishment of user confidence during recruitment in MCS as it is very critical for the success of MCS systems and proposes a simulation trust-based mechanism (SiTBaM) approach. The level of MCS security is enhanced to protect the privacy of participants, so that participants can be assured that the MCS system they are working with during sensing moment is trustworthy. The application of SiTBaM in MCS is verified to yield better results as the simulations show that it offers higher QoS levels, QoD scores, as well as low damage levels in the presence of any task or many malicious users. These results were validated through comparisons with other schemes. INDEX TERMS Mobile crowdsensing, quality-of-data, quality-of-service, security, trust-based scheme.

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“…CRN is the novel technology to improve the spectral efficiency an enormously. The primary target of CR is to discover the unoccupied spectrum and adaptively assign it to the cognitive users [17]. Hence, the complete spectrum is effectively utilized.…”

Section: Cognitive Radio Networkmentioning

confidence: 99%

Modified isotropic orthogonal transform algorithm-universal filtered multicarrier transceiver for 5G cognitive radio application

Dayana

Kumar

2019

IJECE

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Rapid developments in modern wireless communication permit the trade of spectrum scarcity. Higher data rate and wider bandwidth emerge the development in growing demand of wireless communication system. The innovative solution for the spectrum scarcity is cognitive radio (CR). Cognitive radio is the significant technology used to utilize the spectrum effectively. The important aspect of CR is sensing the spectrum band and detects the presence or absence of the primary user in the licensed band. Moreover, another serious issue in next generation (5G) wireless communication is to decide the less complex 5G waveform candidate for achieving higher data rate, low latency and better spectral efficiency. Universal filtered multi-carrier (UFMC) is one of the noticeable waveform candidates for 5G and its applications. In this article, we investigate the spectrum sensing methods in multi-carrier transmission for cognitive radio network applications. Especially, we integrate the sensing algorithm into UFMC transceiver to analyze the spectral efficiency, higher data rates and system complexity. Through the simulation results, we prove that the UFMC based cognitive radio applications outperform the existing Orthogonal Frequency Division Multiplexing (OFDM) based CR applications.

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Trust-based mechanism design for cooperative spectrum sensing in cognitive radio networks

Cited by 27 publications

References 18 publications

Fuzzy based Reliable Cooperative Spectrum Sensing for Smart Grid Environment

Fuzzy based Reliable Cooperative Spectrum Sensing for Smart Grid Environment

Simulation of Trust-Based Mechanism for Enhancing User Confidence in Mobile Crowdsensing Systems

Modified isotropic orthogonal transform algorithm-universal filtered multicarrier transceiver for 5G cognitive radio application

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