A review on trust management in fog/edge computing: Techniques, trends, and challenges

Nikravan, Mohammad; Kashani, Mostafa Haghi

doi:10.1016/j.jnca.2022.103402

Cited by 25 publications

(7 citation statements)

References 94 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this section, we present an overview of the related work in the context of Web and cloud services. Comprehensive literature reviews about the topic are available in [37][38][39][40].…”

Section: Related Workmentioning

confidence: 99%

A reputation assessment model for trustful service recommendation

Tibermacine

Cherif

2023

Computer Standards & Interfaces

View full text Add to dashboard Cite

“…In this section, we present an overview of the related work in the context of Web and cloud services. Comprehensive literature reviews about the topic are available in [37][38][39][40].…”

Section: Related Workmentioning

confidence: 99%

A reputation assessment model for trustful service recommendation

Tibermacine

Cherif

2023

Computer Standards & Interfaces

View full text Add to dashboard Cite

“…A professional inspector may evaluate all games, or the highest-scoring game that meets a criterion may be indicated by Nikravan and Kashani. 9 For enrolment, verification, or identification, multimodal biometric systems utilize more than one physiological or behavioral trait. The NIST report recommends using several biometrics in a tiered strategy.…”

Section: Basic Biometric System Operationsmentioning

confidence: 99%

“…Unless the similarity rating exceeds a certain threshold, any or many matches are displayed. A professional inspector may evaluate all games, or the highest‐scoring game that meets a criterion may be indicated by Nikravan and Kashani 9 . For enrolment, verification, or identification, multimodal biometric systems utilize more than one physiological or behavioral trait.…”

Section: Review Of Related Workmentioning

confidence: 99%

Secure personal authentication in fog devices via multimodal rank‐level fusion

Selvaraj

Thangavel

2023

Concurrency and Computation

View full text Add to dashboard Cite

Summary In recent years, Fog and edge computing have gained enormous increases and usage of Internet of Things (IoT) devices in research and development even though it provides challenges such as security and privacy. One way to ensure this is a fusion of multimodal biometrics authentication systems, which offer highly reliable biometric authentication solutions to reduce the failure to enroll rate, increase the degree of freedom, and deter spoof attacks. Since unimodal biometric systems often face significant limitations due to sensitivity to noise, data quality, non‐universality, and illumination variations. In this research, we propose management of access control to ensure the desired level of security, a Rank level fusion integration method. ORL, GT, CASIA, and VGG‐face databases were used for testing and evaluating the implemented system. Thus, using the LDA with SCE based feature selection approach, the average GAR percent for the facial, hearing, and palm vein is 3.25% for the facial and muffs pulse and 8.55% for the head and palm vein result was the overall performance of the biometric system even in the presence of low‐quality data in the Fog environment.

show abstract

“…The method relies on continuous message expansion. The technique helps to mitigate against smart gateway and eavesdropping attacks, which are frequently conducted against resource allocation algorithms, according to formal analysis results [7]. The suggested technique is also strong because it completely resists key compromise and satisfies user privacy concerns.…”

Section: A Related Work In Edge Computingmentioning

confidence: 99%

“…Privacy-preserving. Fog-enabled aggregation is the name of the smart metering aggregation framework that [7].…”

Section: A Related Work In Edge Computingmentioning

confidence: 99%

Deep Learning for Edge Computing Applications: A Comprehensive Survey

Sanjeetha

Kanagaraj

Herath

et al. 2022

AJCST

View full text Add to dashboard Cite

Edge computing is a modern computer architecture that processes data quickly and efficiently close to its point of origin, hence avoiding slowdowns caused by network latency and capacity limitations. By moving processing power to the network’s perimeter, edge computing decreases the load on central data centers and reduces the time it takes for users to submit data. Therefore, access latency may become a barrier, potentially negating the benefits of edge computing, particularly for applications that need a great deal of data.. Edge computing has some challenges, such as security, incomplete data, investment costs, and maintenance costs. In this research, we undertake a thorough analysis of edge computing, how edge device placement improves performance in IoT networks, compare various edge computing implementations, and explain various difficulties encountered during edge computing implementation. This study aims to promote creative edge-based Internet of Things security design by thoroughly examining existing Internet of Things security solutions at the edge layer and facilitate the dynamic deployment of edge devices.

show abstract

A review on trust management in fog/edge computing: Techniques, trends, and challenges

Cited by 25 publications

References 94 publications

A reputation assessment model for trustful service recommendation

A reputation assessment model for trustful service recommendation

Secure personal authentication in fog devices via multimodal rank‐level fusion

Deep Learning for Edge Computing Applications: A Comprehensive Survey

Contact Info

Product

Resources

About