A Systematic Survey on Fog steered IoT: Architecture, Prevalent Threats and Trust Models

Verma, Richa; Chandra, Shalini

doi:10.1007/s10776-020-00499-z

Cited by 20 publications

(4 citation statements)

References 106 publications

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“…However, an IoT device, by its size and other hardware constraints, is limited on the one hand, in energy due to a reduced battery capacity combined with the difficulty of recharging or replacing it and on the other hand in memory, computing power, and storage capacity. As a solution to meet these constraints, it is important to minimise the energy consumption of sensor networks by implementing routing techniques in a cloud computing environment, which will maximise their lifetime [18][19][20][21][22][23][24].…”

Section: Background Knowledgementioning

confidence: 99%

A new clustering‐based optimised energy approach for fog‐enabled IoT networks

2023

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The Internet of Things (IoT) has induced many advances in the modern world, thus always arousing great interest from the scientific community. Due to a large number of connected devices, this paradigm has put into circulation an enormous data quantity to be processed and offloaded while respecting latency and energy constraints that Central Cloud alone cannot meet. Hence, Fog Computing has come to fill these gaps by providing computing, management, and storage resources via small‐distributed data centres located at the network edge. The majority of previous research on centralised clustering in an IoT environment did not consider both residual energy and the location of the IoT devices, as well as the signalling communication frequency, to solve an issue of extreme energy consumption during data exchange between IoT devices and Fog entities. Thus, the objective of this study is to find solutions taking into account these criteria to guarantee the Fog system's energy efficiency while coping with the energy consumption increase related to the handling of massive data volume. Indeed, the proposed study presents a novel approach based on Fog‐IoT architecture to ensure intelligent energy management during communication and offloaded task processing in IoT applications. The simulation results show its effectiveness.

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Section: Background Knowledgementioning

confidence: 99%

A new clustering‐based optimised energy approach for fog‐enabled IoT networks

2023

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“…Although cloud computing is being widely used because of the services it provides as infrastructure, platform, and software. But it being a decentralized platform, almost every computation is performed at the cloud which introduces latency in the generation of responses 5,6 . The application has to communicate with the cloud for the minutest of the job.…”

Section: Introductionmentioning

confidence: 99%

“…The fog layer is present very close to the users and thus is highly vulnerable to attacks. Thus, the management of a huge amount of user sensitive data at the fog layer needs to be considered as an issue of grave concern 3,5,6 . Fog computing may face those security issues that do not even exist in a well‐equipped cloud scenario 13 .…”

Section: Introductionmentioning

confidence: 99%

FogBus3: A scalable and reliable framework for integrated IoT and fog computing scenario

Verma¹,

Chandra²

2022

Trans Emerging Tel Tech

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Fog computing is an advantageous concept that works in consonance with cloud computing to enhance user experience. It presents innumerable benefits such as low latency, bandwidth saving, and location awareness. But, just being opportunistic towards the platform without checking it for security is not at all justifiable. As more and more sensitive data flows through the fog layer, it becomes mandatory to examine it for security. The fog nodes being closer to the user end are highly vulnerable to being malicious. There exists a possibility that a faulty fog node tries to tamper the data generated from the Internet of things (IoT) device layer and restrains the other functioning nodes to operate upon a common perceived value. In this regard, the authors have proposed the FogBus3 framework that guarantees reliable results from the fog level even in the presence of faulty fog nodes. The proposed framework adds a job categorization mechanism and a reliability ensuring IoT-Fog Agreement Algorithm (IFAA) to the fog layer. The proposed framework has been thoroughly examined with the help of a working example and simulated through MATLAB R2021a. Further, the scenario has been tested before and after implementation of the proposed algorithm on the basis of execution time. Experimental results show that FogBus3's IFAA algorithm improves the execution time of the said setup by 41%. FogBus3 has also been evaluated for its scalability and is believed to ensure reliability in the said scenario.

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“…A new computing paradigm known as fog computing was introduced by Cisco in 2012, aiming to resolve the latency issue by providing IT resources physically close to the data generating devices. Fog computing is an extension of cloud computing, not its replacement, since the IT resources provided by fog computing are not that powerful, so cloud resources are needed at the back end (Verma & Chandra, 2020). Fog and cloud computing clubbed together have capabilities to develop frameworks that can provide scalable as well as virtually unlimited resources (Schooler et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

A scalable framework for smart COVID surveillance in the workplace using Deep Neural Networks and cloud computing

et al. 2021

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A smart and scalable system is required to schedule various machine learning applications to control pandemics like COVID‐19 using computing infrastructure provided by cloud and fog computing. This paper proposes a framework that considers the use case of smart office surveillance to monitor workplaces for detecting possible violations of COVID effectively. The proposed framework uses deep neural networks, fog computing and cloud computing to develop a scalable and time‐sensitive infrastructure that can detect two major violations: wearing a mask and maintaining a minimum distance of 6 feet between employees in the office environment. The proposed framework is developed with the vision to integrate multiple machine learning applications and handle the computing infrastructures for pandemic applications. The proposed framework can be used by application developers for the rapid development of new applications based on the requirements and do not worry about scheduling. The proposed framework is tested for two independent applications and performed better than the traditional cloud environment in terms of latency and response time. The work done in this paper tries to bridge the gap between machine learning applications and their computing infrastructure for COVID‐19.

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A Systematic Survey on Fog steered IoT: Architecture, Prevalent Threats and Trust Models

Cited by 20 publications

References 106 publications

A new clustering‐based optimised energy approach for fog‐enabled IoT networks

A new clustering‐based optimised energy approach for fog‐enabled IoT networks

FogBus3: A scalable and reliable framework for integrated IoT and fog computing scenario

A scalable framework for smart COVID surveillance in the workplace using Deep Neural Networks and cloud computing

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