Cognitive Radio Aided Internet of Vehicles (IoVs) for Improved Spectrum Resource Allocation

Eze, Joy; Zhang, Sijing; Liu, Enjie; Chinedum, Elias E.

doi:10.1109/cit/iucc/dasc/picom.2015.347

Cited by 13 publications

(11 citation statements)

References 21 publications

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“…Although the afore-referenced studies above showed impressive results (both numerical and simulation results) in terms of their ability to significantly improve the fairness in spectrum sharing and the efficiency of multiple CRNs co-existence, better channel utilisation and transmission fairness amongst SUs, their proposed three-state spectrum sensing techniques were only applicable to conventional CRNs, as opposed to CR enabled vehicular ad-hoc networks which are known to have peculiar characteristics totally different from conventional CRNs. To the best of our knowledge, the only studies of three-state spectrum sensing and management model over CR enabled vehicular ad-hoc networks are the studies of Rawat et al [15], and our own studies in [10,11]. However, the study in [15] does not show many details as the paper only presents few results generated from simulation without detailed theoretical analyses to support the simulation results.…”

Section: Related Workmentioning

confidence: 96%

“…This is due to the fact that, with the two-state spectrum sensing model, once CR vehicular SUs from one vehicular ad-hoc CRN sense spectrum hole and occupy the channel(s), CR vehicular SUs from other vehicular ad-hoc CRNs will falsely perceive that the channel(s) is/are occupied by licensed primary user (PU). Furthermore, the challenge of inefficient resource allocation arises as the other CR vehicular SUs will be starved and prohibited from accessing the available additional spectrum resource because the two-state model does not further check if the channel is actually occupied by a licensed PU or just another unlicensed CR vehicular SU [10][11][12].…”

Section: Related Workmentioning

confidence: 99%

“…We adopted the under mentioned evaluation metrics, in addition to the metrics used in [10] to evaluate the performance of our theoretical analyses as well as the simulation experiments:…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…The act of spectrum sensing is one of the crucial roles in CR networks (CRNs) [10]. Generally, all the existing radio spectrum sensing solutions and algorithms are built to detect and identify the presence of a signal on a particular channel.…”

Section: Related Workmentioning

confidence: 99%

“…This scenario is referred to as a two‐state spectrum sensing approach in this study. Although this two‐state spectrum sensing method has been shown to work efficiently well if only one CRN is accessing a given channel, studies have shown that it can significantly restrict or hinder the potentials as well as the fairness of the available spectrum sharing especially if there are more than one CRN co‐existing together [10, 11]. This is due to the fact that, with the two‐state spectrum sensing model, once CR vehicular SUs from one vehicular ad‐hoc CRN sense spectrum hole and occupy the channel(s), CR vehicular SUs from other vehicular ad‐hoc CRNs will falsely perceive that the channel(s) is/are occupied by licensed primary user (PU).…”

Section: Related Workmentioning

confidence: 99%

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Cognitive radio‐enabled Internet of Vehicles: a cooperative spectrum sensing and allocation for vehicular communication

et al. 2018

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Internet of Things (IoTs) era is expected to empower all aspects of Intelligent Transportation System (ITS) to improve transport safety and reduce road accidents. US Federal Communication Commission (FCC) officially allocated 75MHz spectrum in the 5.9GHz band to support vehicular communication which many studies have found insufficient. In this paper, we studied the application of Cognitive Radio (CR) technology to IoVs in order to increase the spectrum resource opportunities available for vehicular communication, especially when the officially allocated 75MHz spectrum in 5.9GHz band is not enough due to high demands as a result of increasing number of connected vehicles as already foreseen in the near era of IoTs. We proposed a novel CR Assisted Vehicular NETwork (CRAVNET) framework which empowers CR enabled vehicles to make opportunistic usage of licensed spectrum bands on the highways. We also developed a novel co-operative three-state spectrum sensing and allocation model which makes CR vehicular secondary units (SUs) aware of additional spectrum resources opportunities on their current and future positions and applies optimal sensing node allocation algorithm to guarantee timely acquisition of the available channels within a limited sensing time. The results of the theoretical analyses and simulation experiments have demonstrated that the proposed model can significantly improve the performance of a cooperative spectrum sensing and provide vehicles with additional spectrum opportunities without harmful interference against the Primary Users (PUs) activities.

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Section: Related Workmentioning

confidence: 96%

Section: Related Workmentioning

confidence: 99%

“…We adopted the under mentioned evaluation metrics, in addition to the metrics used in [10] to evaluate the performance of our theoretical analyses as well as the simulation experiments:…”

Section: Evaluation Metricsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Cognitive radio‐enabled Internet of Vehicles: a cooperative spectrum sensing and allocation for vehicular communication

et al. 2018

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Dependable and reliable cloud‐based architectures for vehicular communications: A systematic literature review

Yang

et al. 2023

Int J Communication

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Traffic congestion, air pollution, fuel wastage, and car accidents are all exacerbated by increased traffic. Thus, vehicular communications, which refer to information transmission between cars, pedestrians, and infrastructures, have lately gained popularity and been extensively explored due to their enormous potential to enable intelligent transportation and various safety applications. Manually piloted cars and automated vehicles can acquire relevant information via vehicular communications to enhance traffic security and boost entertainment services. The basic concept of automobile clouds was originally published in the literature not long ago, and several suggested structural approaches have been presented in this study thus far. Several academics have concentrated on the structural layout to address various problems and, as a result, satisfy user expectations in order to give dependable services. We examined various vehicular cloud topologies in this study. We also offered a complete summary of current network layer research on allowing efficient vehicle communications and examined specific security, architectural, and reliability concerns in vehicular clouds. Also, the taxonomy of vehicular networks was discussed in terms of the service link between vehicular networks and cloud computing. Ultimately, we discussed the research prospects available. The results showed that security and privacy challenges are among the most important challenges.cloud computing, vehicular ad hoc network, vehicular cloud architecture, vehicular communication | INTRODUCTIONBecause of its vast application domains, like commercial-oriented, safety-relevant, and convenience-centered applications, 1-3 vehicular networks have recently gained much study interest. 4 Real-time traffic, notice after a crash, cooperative message transmission, warning of a coordinated collision, and traffic vigilance are some safety-relevant applications that may be categorized. 5 Distant vehicle personalization/diagnostics, digital map downloading, Internet access, value-added commercials and advertisements, and real-time entertainment video relay are included in the commercial-centered application. 6 Route diversions, parking availability, electronic toll collection, and active prediction help are examples of convenience-centered applications. 1,7,8 So, vehicle networks must meet some most stringent standards in all these essential application fields. 2,9 Time-critical Vehicle-to-Vehicle (V2V) safety applications necessitate

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Distributed Channel Assignment in Cognitive-Radio Enabled Internet of Vehicles

Goyal

Patra

2021

Intelligent Systems

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Cognitive Radio Aided Internet of Vehicles (IoVs) for Improved Spectrum Resource Allocation

Cited by 13 publications

References 21 publications

Cognitive radio‐enabled Internet of Vehicles: a cooperative spectrum sensing and allocation for vehicular communication

Cognitive radio‐enabled Internet of Vehicles: a cooperative spectrum sensing and allocation for vehicular communication

Dependable and reliable cloud‐based architectures for vehicular communications: A systematic literature review

Distributed Channel Assignment in Cognitive-Radio Enabled Internet of Vehicles

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