Applying Case-Based Reasoning to Tactical Cognitive Sensor Networks for Dynamic Frequency Allocation

Park, Jae Hoon; Lee, Won Cheol; Choi, Joo Pyoung; Choi, Jae Boong; Um, Soo Bin

doi:10.3390/s18124294

Cited by 5 publications

(3 citation statements)

References 10 publications

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“…A CR engine platform is proposed in this study [22][23][24] for exploiting available frequency channels for a tactical wireless sensor network while attempting to protect incumbent communication devices, referred to as the principal user (PU), from unintended negative interference. There is a compelling need in tactical communication networks to identify accessible frequencies for opportunistic and dynamic access to channels when the PU is active.…”

Section: Literature Reviewmentioning

confidence: 99%

“…GAs require earlier information, which is based on learning and inferred wellness functions: new rules are developed based on the training instances and trends found in prior search phrases. 22,57,58 The challenge must be set up in such a manner that future generations are encouraged to pick better genes, and the parameters must be chosen to reflect the fitness function. Since the well-being evaluation is computationally demanded, GAs are sluggish.…”

Section: Genetic Algorithmsmentioning

confidence: 99%

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Fuzzy logic, genetic algorithms, and artificial neural networks applied to cognitive radio networks: A review

Alkhayyat

Abedi

Bagwari

et al. 2022

International Journal of Distributed Sensor Networks

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Cognitive radios are expected to play an important role in capturing the constantly growing traffic interest on remote networks. To improve the usage of the radio range, a cognitive radio hub detects the weather, evaluates the open-air qualities, and then makes certain decisions and distributes the executives’ space assets. The cognitive radio works in tandem with artificial intelligence and artificial intelligence methodologies to provide a flexible and intelligent allocation for continuous production cycles. The purpose is to provide a single source of information in the form of a survey research to enable academics better understand how artificial intelligence methodologies, such as fuzzy logics, genetic algorithms, and artificial neural networks, are used to various cognitive radio systems. The various artificial intelligence approaches used in cognitive radio engines to improve cognition capabilities in cognitive radio networks are examined in this study. Computerized reasoning approaches, such as fuzzy logic, evolutionary algorithms, and artificial neural networks, are used in the writing audit. This topic also covers cognitive radio network implementation and the typical learning challenges that arise in cognitive radio systems.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Genetic Algorithmsmentioning

confidence: 99%

Fuzzy logic, genetic algorithms, and artificial neural networks applied to cognitive radio networks: A review

Alkhayyat

Abedi

Bagwari

et al. 2022

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

show abstract

“…With the prosperity of the ubiquitous sensing technology, wireless sensor networks (WSNs) are widely used in environmental monitoring, smart home, medical systems, space exploration and many other areas [1][2][3][4][5][6][7]. As an important form of the underlying network technologies of Internet of Things (IoT) [8,9], the node deployment cost [10,11], limited energy [12] and the shortage of wireless spectrum resources [13] has restricted the development of WSNs.…”

Section: Introductionmentioning

confidence: 99%

Non-Cooperative Spectrum Access Strategy Based on Impatient Behavior of Secondary Users in Cognitive Radio Networks

Zeng

Wang

et al. 2019

Electronics

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In the cognitive radio network (CRN), secondary users (SUs) compete for limited spectrum resources, so the spectrum access process of SUs can be regarded as a non-cooperative game. With enough artificial intelligence (AI), SUs can adopt certain spectrum access strategies through their learning ability, so as to improve their own benefit. Taking into account the impatience of the SUs with the waiting time to access the spectrum and the fact that the primary users (PUs) have preemptive priority to use the licensed spectrum in the CRN, this paper proposed the repairable queueing model with balking and reneging to investigate the spectrum access. Based on the utility function from an economic perspective, the relationship between the Nash equilibrium and the socially optimal spectrum access strategy of SUs was studied through the analysis of the system model. Then a reasonable spectrum pricing scheme was proposed to maximize the social benefits. Simulation results show that the proposed access mechanism can realize the consistency of Nash equilibrium strategy and social optimal strategy to maximize the benefits of the whole cognitive system.

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Cognitive radio resource scheduling using an adaptive multiobjective evolutionary algorithm

Wang,

Zeng

et al. 2024

Appl Intell

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With the proliferation of IoT devices and the increasing popularity of location-oriented services in cyber-physical-social systems, the cognitive engines of these systems have taken on a multitude of parameters across various dimensions, making it impractical and time-consuming to search for the exact optimal solution. To address this challenge, the use of nature-inspired or evolutionary algorithms to find satisfactory solutions in a timely manner has gained significant attention, with reference point-based algorithms being one of the prominent approaches. However, when dealing with nonuniform, degenerate, and discrete Pareto fronts in the target space, using a considerable number of reference points may become ineffective, leading to a loss of diversity in exploration and exploitation during the problem-solving process. Consequently, the distribution of the solutions is adversely affected. To overcome this challenge, this paper presents a strategy to estimate the eigenvalues of the Pareto front in a timely manner. When encountering nonuniform, degenerate, and discrete Pareto fronts, a combination of radial space partitioning and angle selection mechanisms is employed to address these issues. Subsequently, an adaptive selection-based many-objective evolutionary algorithm (ASMaOEA) is proposed. Extensive comparisons with several competing methods on 31 representative benchmark problems demonstrate that ASMaOEA can provide a flexible configuration for decision engines in three typical scenarios involving cyber-physical-social systems. Furthermore, the analysis confirms that ASMaOEA can reduce the bit error rate and improve the system’s throughput, thereby offering substantial benefits to the overall performance of the system.

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Applying Case-Based Reasoning to Tactical Cognitive Sensor Networks for Dynamic Frequency Allocation

Cited by 5 publications

References 10 publications

Fuzzy logic, genetic algorithms, and artificial neural networks applied to cognitive radio networks: A review

Fuzzy logic, genetic algorithms, and artificial neural networks applied to cognitive radio networks: A review

Non-Cooperative Spectrum Access Strategy Based on Impatient Behavior of Secondary Users in Cognitive Radio Networks

Cognitive radio resource scheduling using an adaptive multiobjective evolutionary algorithm

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