Security and Privacy in Fog Computing: Challenges

Mukherjee, Mithun; Matam, Rakesh; Shu, Lei; Μαγλαράς, Λέανδρος; Ferrag, Mohamed Amine; Choudhury, Nikumani; Kumar, Vikas

doi:10.1109/access.2017.2749422

Cited by 433 publications

(248 citation statements)

References 51 publications

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“…For instance, Fog nodes can provide cryptographic services to devices that lack the processing power to perform complex computations [33]. Relevant services such as authentication and reputation-based trust management are also good candidates to be implemented by local Fog services running on stable and trusted computers [34]. Privacy policies can also be enforced by Fog nodes that decide which data can be transported outside of the local Fog environment.…”

Section: Discussion and Future Researchmentioning

confidence: 99%

Opportunistic Mobile Sensing in the Fog

Menchaca-Mendez

Luna-Nuñez

Yee-Rendón

et al. 2018

Wireless Communications and Mobile Computing

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The increasing adoption of mobile personal devices and Internet of Things devices is leveraging the emergence of a wide variety of opportunistic sensing applications. However, the designers of this type of applications face a set of technical challenges related to the limitations and heterogeneity of the hardware and software platforms and to the dynamics of the scenarios where they are deployed. In this paper, we introduce a Semantic-Centric Fog-based framework aimed at effectively and efficiently supporting this type of applications. The proposed framework is composed of local and distributed algorithms that support the establishment and coordination of sensing tasks in the Fog. First, it performs ontology-driven in-network processing to locate the most adequate devices to carry out a given sensing task and then, it establishes efficient multihop routes that are used to coordinate relevant devices and to transport the collected sensory data to Fog sinks. We present a set of theorems that prove that the proposed algorithms are correct and the results of a series of detailed simulation-based experiments in NS3 that characterize the performance of the proposed platform. The results show that the proposed framework outperforms traditional sensing platforms that are based on centralized services.

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Section: Discussion and Future Researchmentioning

confidence: 99%

Opportunistic Mobile Sensing in the Fog

Menchaca-Mendez

Luna-Nuñez

Yee-Rendón

et al. 2018

Wireless Communications and Mobile Computing

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“…Fog computing facilitates the operation of smart IoT devices in the IoT by using cloud computing data centers. Zhang et al presented the EMINC framework, to build a multidimensional index for cloud computing system, based on the combination of two popular multidimensional index, namely R‐tree and KD‐tree.…”

Section: Related Workmentioning

confidence: 99%

An efficient indexing for Internet of Things massive data based on cloud‐fog computing

Benrazek

Kouahla

Farou

et al. 2020

Trans Emerging Tel Tech

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In recent years, the number of sensor and actuator nodes in the Internet of Things (IoT) networks has increased, generating a large amount of data. Most research techniques are based on dividing target data into subsets. On a large scale, this volume increases exponentially, which will affect search algorithms. This problem is caused by the inherent deficiencies of space partitioning. This paper introduces a new and efficient indexing structure to index massive IoT data called BCCF‐tree (Binary tree based on containers at the cloud‐fog computing level). This structure is based on recursive partitioning of space using the k‐means clustering algorithm to effectively separate space into nonoverlapping subspace to improve the quality of search and discovery algorithm results. A good topology should avoid a biased allocation of objects for separable sets and should not influence the structure of the index. BCCF‐tree structure benefits to the emerging cloud‐fog computing system, which represents the most powerful real‐time processing capacity provided by fog computing due to its proximity to sensors and the largest storage capacity provided by cloud computing. The paper also discusses the effectiveness of construction and search algorithms, as well as the quality of the index compared to other recent indexing data structures. The experimental results showed good performance.

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“…Thus, trust must be ensured in a fog network for the collaboration of nodes in the network. The heterogeneous nature of fog nodes complicates the trust context in fog computing . Trust allows the fog nodes and/or clients, to predict the expected behaviour of its peers and thus can aid the decision‐making process.…”

Section: Fog Computing Aspectsmentioning

confidence: 99%

“…The behaviour of the objects may also vary with time. Thus, trust values must be calculated dynamically at defined intervals Subjective trust : depending on the characteristics of each object in the network, the security requirements also vary.…”

Section: Fog Computing Aspectsmentioning

confidence: 99%

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Elucidating the challenges for the praxis of fog computing: An aspect‐based study

Martin

Kandasamy

Chandrasekaran

et al. 2019

Int J Communication

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Summary The evolutionary advancements in the field of technology have led to the instigation of cloud computing. The Internet of Things paradigm stimulated the extensive use of sensors distributed across the network edges. The cloud datacenters are assigned the responsibility for processing the collected sensor data. Recently, fog computing was conceptuated as a solution for the overwhelmed narrow bandwidth. The fog acts as a complementary layer that interplays with the cloud and edge computing layers, for processing the data streams. The fog paradigm, as any distributed paradigm, has its set of inherent challenges. The fog environment necessitates the development of management platforms that effectuates the orchestration of fog entities. Owing to the plenitude of research efforts directed toward these issues in a relatively young field, there is a need to organize the different research works. In this study, we provide a compendious review of the research approaches in the domain, with special emphasis on the approaches for orchestration and propose a multilevel taxonomy to classify the existing research. The study also highlights the application realms of fog computing and delineates the open research challenges in the domain.

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Security and Privacy in Fog Computing: Challenges

Cited by 433 publications

References 51 publications

Opportunistic Mobile Sensing in the Fog

Opportunistic Mobile Sensing in the Fog

An efficient indexing for Internet of Things massive data based on cloud‐fog computing

Elucidating the challenges for the praxis of fog computing: An aspect‐based study

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