An optimal fog-cloud offloading framework for big data optimization in heterogeneous IoT networks

Bebortta, Sujit; Tripathy, Subhranshu Sekhar; Modibbo, Umar Muhammad; Ali, Irfan

doi:10.1016/j.dajour.2023.100295

Cited by 27 publications

(6 citation statements)

References 34 publications

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“…A fog node includes multiple physical devices that offer resources and services and link the edge and cloud environments [24]. Fog nodes are responsible for processing, stor-ing, and transmitting data supporting the offloading towards the network edge [25]. Fog nodes can be placed close to the data source to reduce the latency compared to traditional cloud computing or can be closer to the cloud to provide higher computing power and storage capabilities.…”

Section: Fog Computingmentioning

confidence: 99%

Edge Offloading in Smart Grid

Arcas,

Cioara,

Anghel

et al. 2024

Smart Cities

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The management of decentralized energy resources and smart grids needs novel data-driven low-latency applications and services to improve resilience and responsiveness and ensure closer to real-time control. However, the large-scale integration of Internet of Things (IoT) devices has led to the generation of significant amounts of data at the edge of the grid, posing challenges for the traditional cloud-based smart-grid architectures to meet the stringent latency and response time requirements of emerging applications. In this paper, we delve into the energy grid and computational distribution architectures, including edge–fog–cloud models, computational orchestration, and smart-grid frameworks to support the design and offloading of grid applications across the computational continuum. Key factors influencing the offloading process, such as network performance, data and Artificial Intelligence (AI) processes, computational requirements, application-specific factors, and energy efficiency, are analyzed considering the smart-grid operational requirements. We conduct a comprehensive overview of the current research landscape to support decision-making regarding offloading strategies from cloud to fog or edge. The focus is on metaheuristics for identifying near-optimal solutions and reinforcement learning for adaptively optimizing the process. A macro perspective on determining when and what to offload in the smart grid is provided for the next-generation AI applications, offering an overview of the features and trade-offs for selecting between federated learning and edge AI solutions. Finally, the work contributes to a comprehensive understanding of edge offloading in smart grids, providing a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis to support cost–benefit analysis in decision-making regarding offloading strategies.

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Section: Fog Computingmentioning

confidence: 99%

Edge Offloading in Smart Grid

Arcas,

Cioara,

Anghel

et al. 2024

Smart Cities

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“…Also, the hardware used must be capable of capturing biometric data under various environmental conditions, which can be challenging in remote or diverse settings typical of cloud-based services [29]. Another variable of concern is the notion that speed is particularly crucial in cloud environments, where delays in data processing can impact the overall user experience and system efficiency [30,31].…”

Section: Accuracy Variability Across Cloud Platformsmentioning

confidence: 99%

AI for Identity and Access Management (IAM) in the Cloud: Exploring the Potential of Artificial Intelligence to Improve User Authentication, Authorization, and Access Control within Cloud-Based Systems

Olabanji,

Olaniyi,

Adigwe

et al. 2024

AJRCoS

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This comprehensive study explores the integration and effectiveness of Artificial Intelligence (AI) in Identity and Access Management (IAM) within cloud environments. It primarily focuses on how AI can enhance user authentication, authorization, and access control, addressing the challenges and possibilities in cloud computing. The study adopts a mixed-methods approach, employing both quantitative and qualitative analyses. A survey involving 582 cybersecurity experts provides insights into the current state and potential of AI in IAM, while multiple regression analysis examines the impact of various factors on system effectiveness. Four hypotheses are explored: the impact of hardware and software configurations on system accuracy (H1), the influence of computational environments on reliability (H2), the role of demographic factors in user acceptance (H3), and the effect of technological enhancements on system performance and acceptance (H4). Findings indicate significant correlations between these factors and the effectiveness of AI in IAM. Notably, hardware configurations and security concerns influence system accuracy; computational environment variations affect system reliability; demographic factors impact user acceptance; and enhancements such as user feedback, advancements in AI technology, continuous learning algorithms, and system transparency improve performance and acceptance. These insights underscore the need for advanced hardware, standardized software, user-centric design, and continuous improvement in AI technologies for effective IAM in cloud environments. The study provides actionable recommendations for cloud service providers and developers, emphasizing the importance of involving users in development processes, ensuring transparency, and adopting adaptive algorithms. Future research directions include longitudinal studies on the impact of technological advancements and exploring demographic-specific responses to AI-integrated IAM solutions.

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“…In [18], the authors Sujit Bebortta et al (2023) proposed an adaptive integer linear programme technique for allowing the best employment transferring which allocates resources that from the cloud computing the layer to IoT gadgets and takes into consideration the constraints on timely completion of tasks and availability of resources. This method improves system efficiency with regard to latency and power consumption while providing an effective and workable solution to the problems caused by IoT devices and fog computing.…”

Section: Related Workmentioning

confidence: 99%

Optimization based Data Aggregation and Fault Tolerance with Energy Management for Fog Enabled Heterogeneous IoT Environment

Nalayini,

jenifer,

Meena

2024

Preprint

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Fog computing is one of the efficient technologies used for overcoming the issues related to handling large quantities of Heterogeneous Internet of Things (HIoT) data like distributed network and health monitoring by dispensing the applications which gets applied nearer to the network edge. In fog based network model, distributed fog server closeness to the terminal devices allows currently available data to be transmitted effectually. But the increasing size of fog enabled HIoT environments leads to the design of effective routing and path selection to accomplish minimum delay and power consumption. With this motivation, this paper presents Optimization based Data Aggregation and fault Tolerance with Energy Management (ODFTEM) technique for fog enabled HIoT. This idea is segmented into three categories; they are a quasi-oppositional chimp optimization based data aggregation for routing, fault tolerance amongst the fog nodes and a Markov-chain-based probabilistic model for energy management. Initially, data aggregation is employed to gather the data and accumulate it for removing repetitive data and conserving energy. The goal of data aggregation is to reduce quantity of data dissemination and extend lifetime. A quasi-oppositional chimp optimization based data aggregation in routing is performed to choose an optimal collection of routes and perform data aggregation and it follows two stages like relay node selection and data aggregation. Secondly, to boost the network performance the best cost path is chosen where it selects the best nearest neighbor so that the packet transmission time can get reduced. Finally to forecast the energy consumption a Markov chain based probabilistic approach is used and this method can provide a comfortable energy saving model to the fog network. A detailed experimental validation of the ODFTEM technique reported the better performance in terms of energy consumption, throughput and transmission delay of the devices in the Fog enabled HIoT environment which is compared with the baseline methods like IHCFC, DAIFC and ODIFC.

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An optimal fog-cloud offloading framework for big data optimization in heterogeneous IoT networks

Cited by 27 publications

References 34 publications

Edge Offloading in Smart Grid

Edge Offloading in Smart Grid

AI for Identity and Access Management (IAM) in the Cloud: Exploring the Potential of Artificial Intelligence to Improve User Authentication, Authorization, and Access Control within Cloud-Based Systems

Optimization based Data Aggregation and Fault Tolerance with Energy Management for Fog Enabled Heterogeneous IoT Environment

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