Academic Journal
An Advanced Intelligence Protocol for Context-Aware Edge Computing Devices in Distributed Systems.
| Τίτλος: | An Advanced Intelligence Protocol for Context-Aware Edge Computing Devices in Distributed Systems. |
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| Συγγραφείς: | Suryawanshi, Vishnu, Sharma, Nakul, Karlekar, Nandkishor P., Cholke, Abhijeet, Bogam, Vishal, Gurav, Raju Prakash, Devhare, Bharat |
| Πηγή: | Engineering, Technology & Applied Science Research; Jun2026, Vol. 16 Issue 3, p36856-36863, 8p |
| Θεματικοί όροι: | Context-aware computing, Edge computing, Distributed computing, Bayesian analysis, Automated planning & scheduling, Compliance auditing, Intelligent control systems, Reinforcement learning |
| Περίληψη: | The increasing complexity of integrated computing environments requires sustainable architectures that can adapt, evolve, and remain reliable throughout their lifetime. Current edge computing environments are frequently deficient in dynamic context management, adaptive scheduling, and continuous compliance. This work aims to mitigate these limitations and presents an open-source framework that unifies multidimensional contextual analysis, adaptive task scheduling, lifecycle-conscious security, and continuous compliance within a single closed-loop system. The proposed solution utilizes a hybrid methodology consisting of Bayesian inference for probabilistic context reasoning and Deep Reinforcement Learning (DRL) for adaptive decision-making. Experimental analysis shows that the proposed protocol framework is superior to currently available models, resulting in a 24 % reduction in latency, a 14 % decrease in power consumption, and a 96 % compliance rate. These findings confirm that the proposed approach is scalable and enables intelligent, secure, and compliance-aware edge computing systems. [ABSTRACT FROM AUTHOR] |
| Copyright of Engineering, Technology & Applied Science Research is the property of Engineering, Technology & Applied Science Research and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: An Advanced Intelligence Protocol for Context-Aware Edge Computing Devices in Distributed Systems. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Suryawanshi%2C+Vishnu%22">Suryawanshi, Vishnu</searchLink><br /><searchLink fieldCode="AR" term="%22Sharma%2C+Nakul%22">Sharma, Nakul</searchLink><br /><searchLink fieldCode="AR" term="%22Karlekar%2C+Nandkishor+P%2E%22">Karlekar, Nandkishor P.</searchLink><br /><searchLink fieldCode="AR" term="%22Cholke%2C+Abhijeet%22">Cholke, Abhijeet</searchLink><br /><searchLink fieldCode="AR" term="%22Bogam%2C+Vishal%22">Bogam, Vishal</searchLink><br /><searchLink fieldCode="AR" term="%22Gurav%2C+Raju+Prakash%22">Gurav, Raju Prakash</searchLink><br /><searchLink fieldCode="AR" term="%22Devhare%2C+Bharat%22">Devhare, Bharat</searchLink> – Name: TitleSource Label: Source Group: Src Data: Engineering, Technology & Applied Science Research; Jun2026, Vol. 16 Issue 3, p36856-36863, 8p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Context-aware+computing%22">Context-aware computing</searchLink><br /><searchLink fieldCode="DE" term="%22Edge+computing%22">Edge computing</searchLink><br /><searchLink fieldCode="DE" term="%22Distributed+computing%22">Distributed computing</searchLink><br /><searchLink fieldCode="DE" term="%22Bayesian+analysis%22">Bayesian analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Automated+planning+%26+scheduling%22">Automated planning & scheduling</searchLink><br /><searchLink fieldCode="DE" term="%22Compliance+auditing%22">Compliance auditing</searchLink><br /><searchLink fieldCode="DE" term="%22Intelligent+control+systems%22">Intelligent control systems</searchLink><br /><searchLink fieldCode="DE" term="%22Reinforcement+learning%22">Reinforcement learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The increasing complexity of integrated computing environments requires sustainable architectures that can adapt, evolve, and remain reliable throughout their lifetime. Current edge computing environments are frequently deficient in dynamic context management, adaptive scheduling, and continuous compliance. This work aims to mitigate these limitations and presents an open-source framework that unifies multidimensional contextual analysis, adaptive task scheduling, lifecycle-conscious security, and continuous compliance within a single closed-loop system. The proposed solution utilizes a hybrid methodology consisting of Bayesian inference for probabilistic context reasoning and Deep Reinforcement Learning (DRL) for adaptive decision-making. Experimental analysis shows that the proposed protocol framework is superior to currently available models, resulting in a 24 % reduction in latency, a 14 % decrease in power consumption, and a 96 % compliance rate. These findings confirm that the proposed approach is scalable and enables intelligent, secure, and compliance-aware edge computing systems. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Engineering, Technology & Applied Science Research is the property of Engineering, Technology & Applied Science Research and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.48084/etasr.18467 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 8 StartPage: 36856 Subjects: – SubjectFull: Context-aware computing Type: general – SubjectFull: Edge computing Type: general – SubjectFull: Distributed computing Type: general – SubjectFull: Bayesian analysis Type: general – SubjectFull: Automated planning & scheduling Type: general – SubjectFull: Compliance auditing Type: general – SubjectFull: Intelligent control systems Type: general – SubjectFull: Reinforcement learning Type: general Titles: – TitleFull: An Advanced Intelligence Protocol for Context-Aware Edge Computing Devices in Distributed Systems. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Suryawanshi, Vishnu – PersonEntity: Name: NameFull: Sharma, Nakul – PersonEntity: Name: NameFull: Karlekar, Nandkishor P. – PersonEntity: Name: NameFull: Cholke, Abhijeet – PersonEntity: Name: NameFull: Bogam, Vishal – PersonEntity: Name: NameFull: Gurav, Raju Prakash – PersonEntity: Name: NameFull: Devhare, Bharat IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 22414487 Numbering: – Type: volume Value: 16 – Type: issue Value: 3 Titles: – TitleFull: Engineering, Technology & Applied Science Research Type: main |
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