Secure D2D caching framework inspired on trust management and blockchain for Mobile Edge Caching

Rocha, Acquila Santos; Pinheiro, Billy; Borges, Vinicius C. M.

doi:10.1016/j.pmcj.2021.101481

Cited by 8 publications

(2 citation statements)

References 25 publications

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“…Recently, mobile edge computing (MEC) has created a remarkable achievement for the cellular communication industry and has simplified humans' lifestyle [1]. With the near user resources of computation and storage, MEC can provide a low-delay service for resourceconstrained user terminals (UTs) on the internet of things (IoT) [2]. However, with the increase of the number of UTs and emerging smart applications, wireless communication networks face a serious challenge.…”

Section: Introductionmentioning

confidence: 99%

Blockchain Based Decentralized and Proactive Caching Strategy in Mobile Edge Computing Environment

Bai,

Zhu,

2024

Sensors

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In the mobile edge computing (MEC) environment, the edge caching can provide the timely data response service for the intelligent scenarios. However, due to the limited storage capacity of edge nodes and the malicious node behavior, the question of how to select the cached contents and realize the decentralized security data caching faces challenges. In this paper, a blockchain-based decentralized and proactive caching strategy is proposed in an MEC environment to address this problem. The novelty is that the blockchain was adopted in an MEC environment with a proactive caching strategy based on node utility, and the corresponding optimization problem was built. The blockchain was adopted to build a secure and reliable service environment. The employed methodology is that the optimal caching strategy was achieved based on the linear relaxation technology and the interior point method. Additionally, in a content caching system, there is a trade-off between cache space and node utility, and the caching strategy was proposed to solve this problem. There was also a trade-off between the consensus process delay of blockchain and the caching latency of content. An offline consensus authentication method was adopted to reduce the influence of the consensus process delay on the content caching. The key finding was that the proposed algorithm can reduce latency and can ensure the security data caching in an IoT environment. Finally, the simulation experiment showed that the proposed algorithm can achieve up to 49.32%, 43.11%, and 34.85% improvements on the cache hit rate, the average content response latency, and the average system utility, respectively, compared to the random content caching algorithm, and it achieved up to 9.67%, 8.11%, and 5.95% increases, successively, compared to the greedy content caching algorithm.

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Section: Introductionmentioning

confidence: 99%

Blockchain Based Decentralized and Proactive Caching Strategy in Mobile Edge Computing Environment

Bai,

Zhu,

2024

Sensors

View full text Add to dashboard Cite

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“…However, this situation creates a bottleneck threat due to continuous growth in the process and may cause delays and malfunctions as a result of blockages in network traffic [6]. With these developments, many problem areas that need to be developed in terms of efficiency, security requirements, resource usage and user security have emerged [3,5]. Many IoT devices used to gather information from the environment do not have enough resources to deal with malicious cyber-attacks.…”

Section: Introductionmentioning

confidence: 99%

Implementation of Computing Infrastructures for Blockchain Based Distributed Learning Models

Gürfi̇dan

Ersoy

2022

Preprint

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Reaching a sufficient number of data sets, learning past experiences from many systems and using this experience in instant or future predictions are among the capabilities of artificial intelligence. The horizontal and vertical growth of industrial systems and the transfer of experience from each location to all other locations increase the quality of the process. However, the rapid growth of IoT (Internet of Things) and OT (Operational Technology) assets in recent years raises questions about data integrity, confidentiality and accessibility. It deploys edge computing and blockchain-based solutions for data security and secure transmission in the IoT ecosystem. In this study, a four-layer IoT ecosystem network is proposed that combines the learning capabilities of artificial intelligence-based systems used in different locations and offers a blockchain-based storage system for data security. These layers consist of node layer, edge layer, decision layer, and training and blockchain layer, respectively. The lowest layer, the node layer, is responsible for collecting the temperature and humidity values in different locations with the developed node devices in order to evaluate them. The data generated in the node devices is transferred to the communicating edge device in the edge layer. The edge layer collects the data from the nodes in the edge system and transfers it to the server centrally. The training and blockchain layer provide the collection of data from edge devices, training the artificial intelligence model and transferring the weights to the decision layer. At the same time, the blockchain-based storage system works at this layer to securely store the processed data. As a result, with this study, it is aimed to develop a framework for transferring the local learning experiences of distributed IoT devices to all IoT devices and for the secure storage of data.

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Distributed content placement in cache-enabled small cell networks in the presence of malicious users

Rashidi

Hakami

2023

Telecommun Syst

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Secure D2D caching framework inspired on trust management and blockchain for Mobile Edge Caching

Cited by 8 publications

References 25 publications

Blockchain Based Decentralized and Proactive Caching Strategy in Mobile Edge Computing Environment

Blockchain Based Decentralized and Proactive Caching Strategy in Mobile Edge Computing Environment

Implementation of Computing Infrastructures for Blockchain Based Distributed Learning Models

Distributed content placement in cache-enabled small cell networks in the presence of malicious users

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