Fog Computing, Edge Computing and a return to privacy and personal autonomy

Losavio, Michael

doi:10.1016/j.procs.2020.04.188

Cited by 10 publications

(2 citation statements)

References 8 publications

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“…Transmitting the overwhelming IoT data to the cloud would cause network overhead, consuming bandwidth, and latency issues [ 167 ]. Hence, to cut back on the data transfer cost as well as network delays, service providers are steering towards the fog and edge computing [ 168 ], with an additional opportunity for enforcing security and privacy [ 169 ]. The IoT systems comprise edge equipment, sensors, and actuators with latency, bandwidth, and security necessities [ 166 ].…”

Section: Confluence Of ML and Fog/edgementioning

confidence: 99%

Recent Advances in Evolving Computing Paradigms: Cloud, Edge, and Fog Technologies

Angel

Ravindran

Vincent

et al. 2021

Sensors

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Cloud computing has become integral lately due to the ever-expanding Internet-of-things (IoT) network. It still is and continues to be the best practice for implementing complex computational applications, emphasizing the massive processing of data. However, the cloud falls short due to the critical constraints of novel IoT applications generating vast data, which entails a swift response time with improved privacy. The newest drift is moving computational and storage resources to the edge of the network, involving a decentralized distributed architecture. The data processing and analytics perform at proximity to end-users, and overcome the bottleneck of cloud computing. The trend of deploying machine learning (ML) at the network edge to enhance computing applications and services has gained momentum lately, specifically to reduce latency and energy consumed while optimizing the security and management of resources. There is a need for rigorous research efforts oriented towards developing and implementing machine learning algorithms that deliver the best results in terms of speed, accuracy, storage, and security, with low power consumption. This extensive survey presented on the prominent computing paradigms in practice highlights the latest innovations resulting from the fusion between ML and the evolving computing paradigms and discusses the underlying open research challenges and future prospects.

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Section: Confluence Of ML and Fog/edgementioning

confidence: 99%

Recent Advances in Evolving Computing Paradigms: Cloud, Edge, and Fog Technologies

Angel

Ravindran

Vincent

et al. 2021

Sensors

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“…Due to the distribution of Fog nodes and its closeness to IoT devices, centralized control system like what we have in the Cloud is not possible. Therefore, new innovative techniques and solutions are required to enhance IoT users and data privacy in the Fog environment 24,123,124 such as using the blockchain technology. 26,27,[125][126][127][128] This section discusses the recommendations and future opportunities to mitigate this issue.…”

Section: Iot Device Privacymentioning

confidence: 99%

Fog computing security and privacy for the Internet of Thing applications: State‐of‐the‐art

Alzoubi

Osmanaj

Jaradat

et al. 2020

Security and Privacy

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Fog nodes are implemented near to end‐users Internet of Things (IoT) devices, which mitigate the impact of low latency, location awareness, and geographic distribution unsupported features of many IoT applications. Moreover, Fog computing decreases the data offload into the Cloud, which decreases the response time. Despite these benefits, Fog computing faces many challenges in meeting security and privacy requirements. These challenges occur due to the limitations of Fog computing resources. In fact, Fog computing may add new security and privacy issues. Although many papers have discussed the Fog security and privacy issues recently, most of these papers have discussed these issues at a very high level. This paper provides a comprehensive understanding of Fog privacy and security issue. In this survey, we review the literature on Fog computing to draw the state‐of‐the‐art of the security and privacy issues raised by Fog computing. The findings of this survey reveal that studying Fog computing is still in its infant stage. Many questions are yet to be answered to address the privacy and security challenges of Fog computing.

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Artificial Intelligence, Smart Technologies and the Internet of Things: Caution with the Safety, Security and Privacy Eco-system

Losavio

Elmaghraby

2021

Lecture Notes in Networks and Systems

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Fog Computing, Edge Computing and a return to privacy and personal autonomy

Cited by 10 publications

References 8 publications

Recent Advances in Evolving Computing Paradigms: Cloud, Edge, and Fog Technologies

Recent Advances in Evolving Computing Paradigms: Cloud, Edge, and Fog Technologies

Fog computing security and privacy for the Internet of Thing applications: State‐of‐the‐art

Artificial Intelligence, Smart Technologies and the Internet of Things: Caution with the Safety, Security and Privacy Eco-system

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