EVACON-Rainsnow Computing: An Amalgamation of Cloud and Its Inherited Computing to Encourage End User for Both Localized and Globalized Remote Computing

Yadav, Santosh Kumar; Kumar, Rakesh

doi:10.1007/s11277-023-10741-5

Wireless Pers Commun

2023

DOI: 10.1007/s11277-023-10741-5

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EVACON-Rainsnow Computing: An Amalgamation of Cloud and Its Inherited Computing to Encourage End User for Both Localized and Globalized Remote Computing

Santosh Kumar Yadav,

Rakesh Kumar

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2024

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Cited by 3 publications

References 51 publications

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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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Cognitive radio resource scheduling using an adaptive multiobjective evolutionary algorithm

Wang,

Zeng

et al. 2024

Appl Intell

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Scalable energy optimization of resources for mobile cloud computing using sensor enabled cluster based system

Yadav,

Kumar

2024

Wireless Netw

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Network security situational awareness and early warning architecture based on big data

Zhao

2024

Int J Syst Assur Eng Manag

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EVACON-Rainsnow Computing: An Amalgamation of Cloud and Its Inherited Computing to Encourage End User for Both Localized and Globalized Remote Computing

Cited by 3 publications

References 51 publications

Cognitive radio resource scheduling using an adaptive multiobjective evolutionary algorithm

Cognitive radio resource scheduling using an adaptive multiobjective evolutionary algorithm

Scalable energy optimization of resources for mobile cloud computing using sensor enabled cluster based system

Network security situational awareness and early warning architecture based on big data

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