Trust Enforced Computational Offloading for Health Care Applications in Fog Computing

Meena, Vijay Kamal; Gorripatti, Meghana; Praba, T. Suriya

doi:10.1007/s11277-021-08285-7

Cited by 14 publications

(3 citation statements)

References 16 publications

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“…This ensures the system’s capability to handle varying workloads and maintain high availability, even during peak times. Geo-proximity routing, a technique routing data traffic to the nearest data center or server based on the user’s geographical location, is employed to reduce latency and ensure faster data access for users in different regions ( Meena, Gorripatti & Praba, 2021 ). Additionally, data sharding, involving the horizontal partitioning of data across multiple servers or databases, enhances data retrieval efficiency and scalability ( Niya, Beckmann & Stiller, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

An adaptive data-driven architecture for mental health care applications

Sundaram,

Subramaniam,

Ab Hamid

et al. 2024

PeerJ

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Background In the current era of rapid technological innovation, our lives are becoming more closely intertwined with digital systems. Consequently, every human action generates a valuable repository of digital data. In this context, data-driven architectures are pivotal for organizing, manipulating, and presenting data to facilitate positive computing through ensemble machine learning models. Moreover, the COVID-19 pandemic underscored a substantial need for a flexible mental health care architecture. This architecture, inclusive of machine learning predictive models, has the potential to benefit a larger population by identifying individuals at a heightened risk of developing various mental disorders. Objective Therefore, this research aims to create a flexible mental health care architecture that leverages data-driven methodologies and ensemble machine learning models. The objective is to proficiently structure, process, and present data for positive computing. The adaptive data-driven architecture facilitates customized interventions for diverse mental disorders, fostering positive computing. Consequently, improved mental health care outcomes and enhanced accessibility for individuals with varied mental health conditions are anticipated. Method Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, the researchers conducted a systematic literature review in databases indexed in Web of Science to identify the existing strengths and limitations of software architecture relevant to our adaptive design. The systematic review was registered in PROSPERO (CRD42023444661). Additionally, a mapping process was employed to derive essential paradigms serving as the foundation for the research architectural design. To validate the architecture based on its features, professional experts utilized a Likert scale. Results Through the review, the authors identified six fundamental paradigms crucial for designing architecture. Leveraging these paradigms, the authors crafted an adaptive data-driven architecture, subsequently validated by professional experts. The validation resulted in a mean score exceeding four for each evaluated feature, confirming the architecture’s effectiveness. To further assess the architecture’s practical application, a prototype architecture for predicting pandemic anxiety was developed.

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Section: Discussionmentioning

confidence: 99%

An adaptive data-driven architecture for mental health care applications

Sundaram,

Subramaniam,

Ab Hamid

et al. 2024

PeerJ

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“…Healthcare sector is one of the major applications of IoT. A secure offloading scheme for healthcare sector is proposed by V.Meena et al The paper uses algorithms such as optimal service offloader and trust assessment [13] to identify the appropriate fog nodes and to offload data in a secure manner.…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamic Offloading Technique for Latency-Sensitive Internet of Things Applications using Fog Computing

Thomas,

Jose

2023

IJRITCC

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Internet of Things (IoT) has evolved as a novel paradigm that provides com-putation power to different entities connected to it. IoT offers services to multiple sectors such as home automation, industrial automation, traffic management, healthcare sector, agriculture industry etc. IoT generally relies on cloud data centers for extended analytics, processing and storage support. The cloud offers highly scalable and robust platform for IoT applications. But latency sensitive IoT applications suffer delay issues as the cloud lies in remote location. Edge/fog computing was introduced to overcome the issues faced by delay-sensitive IoT applications. These platforms lie close to the IoT network, reducing the delay and response time. The fog nodes are usually distributed in nature. The data has to be properly offloaded to available fog nodes using efficient strategies to gain benefit from the integration. Differ-ent offloading schemes are available in the literature to overcome this prob-lem This paper proposes a novel offloading approach by combining two effi-cient metaheuristic algorithms, Honey Badger Algorithm (HBA) and Fla-mingo Search Algorithm (FSA) termed as HB-FS algorithm. The HB-FS is executed in an iterative manner optimizing the objective function in each it-eration. The performance evaluation of the proposed approach is done with different existing metaheuristic algorithms and the evaluations show that the proposed work outperforms the existing algorithms in terms of latency, response time and execution time. The methodology also offers better degree of imbalance with proper load balancing under different conditions.

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“…On the other hand, the distribution of IoT devices in both the spatial and temporal domains are not usually uniform, especially in the moving IoT environments [54]. This circumstance usually leads to unbalanced loads of traffic, which may be alleviated by the implementation of offloading policies among fog nodes [55], or any other feasible resourceful environment, as a cloud node [56].…”

Section: Fog Computing and Iotmentioning

confidence: 99%

Modeling of a Generic Edge Computing Application Design

Roig

Alcaraz

Gilly

et al. 2021

Sensors

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Edge computing applications leverage advances in edge computing along with the latest trends of convolutional neural networks in order to achieve ultra-low latency, high-speed processing, low-power consumptions scenarios, which are necessary for deploying real-time Internet of Things deployments efficiently. As the importance of such scenarios is growing by the day, we propose to undertake two different kind of models, such as an algebraic models, with a process algebra called ACP and a coding model with a modeling language called Promela. Both approaches have been used to build models considering an edge infrastructure with a cloud backup, which has been further extended with the addition of extra fog nodes, and after having applied the proper verification techniques, they have all been duly verified. Specifically, a generic edge computing design has been specified in an algebraic manner with ACP, being followed by its corresponding algebraic verification, whereas it has also been specified by means of Promela code, which has been verified by means of the model checker Spin.

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Trust Enforced Computational Offloading for Health Care Applications in Fog Computing

Cited by 14 publications

References 16 publications

An adaptive data-driven architecture for mental health care applications

An adaptive data-driven architecture for mental health care applications

Dynamic Offloading Technique for Latency-Sensitive Internet of Things Applications using Fog Computing

Modeling of a Generic Edge Computing Application Design

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