A robust conscious model for enhancing cognitive radio quality of service

Periola, A. A.; Falowo, Olabisi E.

doi:10.1109/pimrc.2016.7794894

Cited by 8 publications

(3 citation statements)

References 19 publications

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“…The Weibull distribution is commonly used to characterize the failure distribution of system components. This suitability of the Weibull distribution is recognized in (Periola and Falowo 2016a;b) and is used in this paper.…”

Section: Discussion Of Simulation Resultsmentioning

confidence: 99%

Cloud platform incorporating dynamic modularity for improved service to subscribers

Periola

Ajisafe

Oyediran

2021

J Ambient Intell Human Comput

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Cloud platforms play an important role in future computing networks and should deliver their expected functionality at all epochs. They are modular and use multivendor subsystems that should be fully interoperable. However, the realization of multivendor component interoperability and functionality may not exist when upgraded components do not function with existing server components. This results in a loss of the functionality of the cloud platform server. The identified challenge is addressed via a phased realization of multivendor component interoperability. In this approach (motivated by biological transplantation), interoperability is selectively realizable at different epochs within a server and using other server's components over a network. The performance is formulated using the cloud link availability factor (CLAF) and link capacity. Evaluation shows that the proposed mechanism enhances the CLAF and link capacity by 54% and 20.7% on average, respectively.

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Section: Discussion Of Simulation Resultsmentioning

confidence: 99%

Cloud platform incorporating dynamic modularity for improved service to subscribers

Periola

Ajisafe

Oyediran

2021

J Ambient Intell Human Comput

Self Cite

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“…Artificial neural networks are suitable for designing robust and adaptive systems [26]. The concept of learning diversity is presented in [26]. Learning diversity implies the use of multiple learning algorithms to achieve a given goal at different epochs.…”

Section: Motivation and Related Backgroundmentioning

confidence: 99%

“…Learning diversity implies the use of multiple learning algorithms to achieve a given goal at different epochs. The suitability of learning diversity is examined for cognitive radios in [26]. The use of multiple learning algorithms is beneficial because different learning algorithms are suitable for executing prediction in different scenarios with varying prediction accuracy.…”

Section: Motivation and Related Backgroundmentioning

confidence: 99%

Intelligent learning diversity mechanism for unmanned aerial vehicles applications

Periola¹,

Obayiuwana²

2020

Nig. J. Tech.

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The increased use of drones and aerial vehicles in applications poses challenges of airspace safety for aviation organizations. It is important to ensure the safety of the airspace when a significant number of unmanned aerial vehicles are deployed by civilian users. A solution that meets this requirement is important to promote innovation in the commercialization of air space for civilian users deploying unmanned aerial vehicle. The discussion in this paper proposes a mechanism that uses artificial intelligence to address this challenge. The proposed mechanism utilizes a low altitude platform (LAP) and entities in terrestrial wireless networks. The low altitude platform (LAP) observes, develops insights and training data (with human aid). The training data is used to develop learning mechanisms which determine the suitable unmanned aerial vehicles flight parameters in different scenarios. The use of the LAP reduces the burden of communicating with terrestrial base stations. The unmanned aerial vehicles have a reduced altitude between the LAPs in comparison to terrestrial base stations. This reduces the free space path loss and rain-induced attenuation. The performance benefit of the proposed mechanism in comparison to existing solution is examined via MATLAB simulations. Evaluation shows that the proposed mechanism reduces the network access costs by up to 90% on average. The proposed mechanism also increases available flight power and improves airspace safety by 37.3% and up to 53.2% on average respectively. Keywords: Autonomous unmanned aerial vehicles, Intelligence Paradigm; Aviation Safety, Capital Constrained Aviation Organizations.

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Future cloud: spherical processors for realizing low-cost upgrade in underwater data centers

2021

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A robust conscious model for enhancing cognitive radio quality of service

Cited by 8 publications

References 19 publications

Cloud platform incorporating dynamic modularity for improved service to subscribers

Cloud platform incorporating dynamic modularity for improved service to subscribers

Intelligent learning diversity mechanism for unmanned aerial vehicles applications

Future cloud: spherical processors for realizing low-cost upgrade in underwater data centers

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