Self-Aware Fog Computing in Private and Secure Spheres

Tammemäe, Kalle; Jantsch, Axel; Kuusik, Alar; Preden, Jürgo-Sören; Õunapuu, Enn

doi:10.1007/978-3-319-57639-8_5

Cited by 14 publications

(11 citation statements)

References 20 publications

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“…Fog includes all the devices from the cloud to the end devices and has a substantial overlap with Edge Computing [41]. Nevertheless, as some authors indicate, edge computing focus more toward the things side, while fog computing focus more on the infrastructure side [42,43] .…”

Section: Motivationsmentioning

confidence: 99%

“…Other works make use of Agent Based Model, Multivariate Gaussian Model, Hidden Markov Model and Neural Networks as predictive strategies to foretell the behaviour of the different parts of the system [43]. For instance, in Smart Grid, in [132], Erickson et al make use of Agent Based and Multivariate Gaussian model to estimate the occupancy in a large multi-function building and for predicting user mobility patterns in order to efficiently control energy usage.…”

Section: Decision-makingmentioning

confidence: 99%

See 1 more Smart Citation

A survey on fog computing for the Internet of Things

Bellavista

Berrocal

Corradi

et al. 2019

Pervasive and Mobile Computing

228

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Fog computing has emerged to support the requirements of IoT applications that could not be met by today's solutions. Different initiatives have been presented to drive the development of fog, and much work has been done to improve certain aspects. However, an in-depth analysis of the different solutions, detailing how they can be integrated and applied to meet specific requirements, is still required. In this work, we present a unified architectural model and a new taxonomy, by comparing a large number of solutions. Finally, we draw some conclusions and guidelines for the development of IoT applications based on fog.

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Section: Motivationsmentioning

confidence: 99%

Section: Decision-makingmentioning

confidence: 99%

A survey on fog computing for the Internet of Things

Bellavista

Berrocal

Corradi

et al. 2019

Pervasive and Mobile Computing

228

View full text Add to dashboard Cite

show abstract

“…[ 63 ] have developed a platform for managing smart mobility and transport in network edges. Tammemäe et al [ 64 ] proposed service architecture to support the self-awareness in fog and IoE.…”

Section: Background and Related Workmentioning

confidence: 99%

Distributed Artificial Intelligence-as-a-Service (DAIaaS) for Smarter IoE and 6G Environments

Janbi

Katib

Albeshri

et al. 2020

Sensors

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Artificial intelligence (AI) has taken us by storm, helping us to make decisions in everything we do, even in finding our “true love” and the “significant other”. While 5G promises us high-speed mobile internet, 6G pledges to support ubiquitous AI services through next-generation softwarization, heterogeneity, and configurability of networks. The work on 6G is in its infancy and requires the community to conceptualize and develop its design, implementation, deployment, and use cases. Towards this end, this paper proposes a framework for Distributed AI as a Service (DAIaaS) provisioning for Internet of Everything (IoE) and 6G environments. The AI service is “distributed” because the actual training and inference computations are divided into smaller, concurrent, computations suited to the level and capacity of resources available with cloud, fog, and edge layers. Multiple DAIaaS provisioning configurations for distributed training and inference are proposed to investigate the design choices and performance bottlenecks of DAIaaS. Specifically, we have developed three case studies (e.g., smart airport) with eight scenarios (e.g., federated learning) comprising nine applications and AI delivery models (smart surveillance, etc.) and 50 distinct sensor and software modules (e.g., object tracker). The evaluation of the case studies and the DAIaaS framework is reported in terms of end-to-end delay, network usage, energy consumption, and financial savings with recommendations to achieve higher performance. DAIaaS will facilitate standardization of distributed AI provisioning, allow developers to focus on the domain-specific details without worrying about distributed training and inference, and help systemize the mass-production of technologies for smarter environments.

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“…Self-awareness has been one of the promising concepts to improve system's performance and reduce its energy consumption in literature [29]- [33]. In health monitoring this concept is used to reduce energy consumption and extend the battery lifetime of wearable devices.…”

Section: B Self-awareness In Biomedical Applicationsmentioning

confidence: 99%

Resource-Aware Distributed Epilepsy Monitoring Using Self-Awareness From Edge to Cloud

Forooghifar

Aminifar

Atienza

2019

IEEE Trans. Biomed. Circuits Syst.

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The integration of wearable devices in humans' daily lives has grown significantly in recent years and still continues to affect different aspects of high-quality life. Thus, ensuring the reliability of the decisions becomes essential in biomedical applications, while representing a major challenge considering batterypowered wearable technologies. Transferring the complex and energy-consuming computations to fogs or clouds can significantly reduce the energy consumption of wearable devices and result in a longer lifetime of these systems with a single battery charge. In this work, we aim to distribute the complex and energy-consuming machine-learning computations between the edge, fog, and cloud, based on the notion of self-awareness that takes into account the complexity and reliability of the algorithm. We also model and analyze the trade-offs in terms of energy consumption, latency, and performance of different Internet of Things (IoT) solutions. We consider the epileptic seizure detection problem as our realworld case study to demonstrate the importance of our proposed self-aware methodology.

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Self-Aware Fog Computing in Private and Secure Spheres

Cited by 14 publications

References 20 publications

A survey on fog computing for the Internet of Things

A survey on fog computing for the Internet of Things

Distributed Artificial Intelligence-as-a-Service (DAIaaS) for Smarter IoE and 6G Environments

Resource-Aware Distributed Epilepsy Monitoring Using Self-Awareness From Edge to Cloud

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