A Fog Computing Architecture to Share Sensor Data by Means of Blockchain Functionality

Cech, Hendrik; GroBmann, Marcel; Krieger, Udo R.

doi:10.1109/icfc.2019.00013

Cited by 36 publications

(17 citation statements)

References 19 publications

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“…An interesting combination of blockchain and fog computing technologies is proposed in the work of Cech, Großmann & Krieger (2019) . Their decentralized architecture is based on MultiChain nodes embedded in more powerful fog cells and a P2P network.…”

Section: Related Workmentioning

confidence: 99%

FogFrame: a framework for IoT application execution in the fog

Skarlat¹,

Schulte²

2021

PeerJ Computer Science

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Recently, a multitude of conceptual architectures and theoretical foundations for fog computing have been proposed. Despite this, there is still a lack of concrete frameworks to setup real-world fog landscapes. In this work, we design and implement the fog computing framework FogFrame—a system able to manage and monitor edge and cloud resources in fog landscapes and to execute Internet of Things (IoT) applications. FogFrame provides communication and interaction as well as application management within a fog landscape, namely, decentralized service placement, deployment and execution. For service placement, we formalize a system model, define an objective function and constraints, and solve the problem implementing a greedy algorithm and a genetic algorithm. The framework is evaluated with regard to Quality of Service parameters of IoT applications and the utilization of fog resources using a real-world operational testbed. The evaluation shows that the service placement is adapted according to the demand and the available resources in the fog landscape. The greedy placement leads to the maximum utilization of edge devices keeping at the edge as many services as possible, while the placement based on the genetic algorithm keeps devices from overloads by balancing between the cloud and edge. When comparing edge and cloud deployment, the service deployment time at the edge takes 14% of the deployment time in the cloud. If fog resources are utilized at maximum capacity, and a new application request arrives with the need of certain sensor equipment, service deployment becomes impossible, and the application needs to be delegated to other fog resources. The genetic algorithm allows to better accommodate new applications and keep the utilization of edge devices at about 50% CPU. During the experiments, the framework successfully reacts to runtime events: (i) services are recovered when devices disappear from the fog landscape; (ii) cloud resources and highly utilized devices are released by migrating services to new devices; (iii) and in case of overloads, services are migrated in order to release resources.

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Section: Related Workmentioning

confidence: 99%

FogFrame: a framework for IoT application execution in the fog

Skarlat¹,

Schulte²

2021

PeerJ Computer Science

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“…The following is a summary of the recommendations that can help to achieve security and privacy in the Fog environment. 65,[69][70][71][72][73][74][75][76][77][78][79][80] TA B L E 6 Data processing challenges and solutions in Fog computing…”

Section: Rq3-challenges and Future Directionsmentioning

confidence: 99%

“…67 Moreover, deployment of the social network, enabling IoT users to make the decision of data sharing, and deployment of the blockchain technology need to be considered. 3,69,72,91,[118][119][120][121][122] Establishing secure and private fine-grained data sharing within the heterogeneous environment is one of the motivating tasks that need to be considered in Fog computing research. 29,69 Therefore, exploring different data distribution strategies, as entities federation, and efficient encryption scheme for unified and secure integration of current protocols and standards become a necessity for achieving secure and private data distribution network.…”

Section: Data and Content Distributionmentioning

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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“…In 2019, authors of [28] proposed CrowdChain, a Fogassisted Blockchain-based crowd sensing framework, where BC is deployed for performing the payments/rewards, and record the identities in a chain located in the fog. Proof-of-Concept (PoC) [65] was used by [29] in the MultiChain 4 platform, where Blockchain-based Function-as-a-Service was proposed to have immutable, local storage of protected streamed real-time sensor data. Another approach for integrating FC and BC proposed in [30], where the concept of Plasma BCs was deployed.…”

Section: A Internet Of Things Applications (Iot)mentioning

confidence: 99%

A Survey on Blockchain-Fog Integration Approaches

Baniata

Kertész

2020

IEEE Access

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Fog computing (FC) is the extension of Cloud Computing (CC), from the core of the internet architecture to the edge of the network, with the aim to perform processes closer to end-users. This extension is proven to enhance security, and to reduce latency and energy consumption. Blockchain (BC), on the other hand, is the base technology behind crypto-currencies, yet is implemented in wide range of different applications. The security and reliability, along with the distributed trust management criteria proposed in BC, excited the research community to integrate it with FC, in a step towards reaching a distributed and trusted, Data, Payment, Reputation, and Identity management systems. In this survey we present the upto-date state-of-the-art of FC-BC integration with a detailed literature review and classification. We discuss and categorize the related papers according to the year of publication, domain, used algorithms, BC roles, and the placement of the BC in the FC architecture. Our research presents detailed observations, analysis, and open challenges for the BC-FC integration. We believe such conclusions may clarify the vision of the BC-FC integration, and calibrate the compass towards open issues and future research directions.

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A Fog Computing Architecture to Share Sensor Data by Means of Blockchain Functionality

Cited by 36 publications

References 19 publications

FogFrame: a framework for IoT application execution in the fog

FogFrame: a framework for IoT application execution in the fog

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

A Survey on Blockchain-Fog Integration Approaches

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