Using blockchain to enhance the security of fog-assisted crowdsensing systems

Gu, Xin; Peng, Jun; Yu, Wentao; Cheng, Yijun; Fu, Jiang; Zhang, Xiaoyong; Huang, Zhiwu; Cai, Lin

doi:10.1109/isie.2019.8781332

Cited by 16 publications

(6 citation statements)

References 16 publications

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“…Other studies utilizing blockchain for MCS work in a loosely coupled fashion, where the blockchain network is used to facilitate some specific MCS procedure(s) as an independent function module rather than acting as the distributed MCS platform. In [27], [28], blockchain was adopted to evaluate or verify the submissions from workers so as to eliminate their malicious behaviors and resist information tampering. Jia et al [29] designed a blockchain-powered confusion mechanism to hide the real locations of workers in MCS.…”

Section: A Blockchain-based Mobile Crowdsensingmentioning

confidence: 99%

Blockchain and Federated Edge Learning for Privacy-Preserving Mobile Crowdsensing

Wang

Cheng

2023

IEEE Internet Things J.

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Mobile crowdsensing (MCS) counting on the mobility of massive workers helps the requestor accomplish various sensing tasks with more flexibility and lower cost. However, for the conventional MCS, the large consumption of communication resources for raw data transmission and high requirements on data storage and computing capability hinder potential requestors with limited resources from using MCS. To facilitate the widespread application of MCS, we propose a novel MCS learning framework leveraging on blockchain technology and the new concept of edge intelligence based on federated learning (FL), which involves four major entities, including requestors, blockchain, edge servers and mobile devices as workers. Even though there exist several studies on blockchain-based MCS and blockchain-based FL, they cannot solve the essential challenges of MCS with respect to accommodating resource-constrained requestors or deal with the privacy concerns brought by the involvement of requestors and workers in the learning process. To fill the gaps, four main procedures, i.e., task publication, data sensing and submission, learning to return final results, and payment settlement and allocation, are designed to address major challenges brought by both internal and external threats, such as malicious edge servers and dishonest requestors. Specifically, a mechanism design based data submission rule is proposed to guarantee the data privacy of mobile devices being truthfully preserved at edge servers; consortium blockchain based FL is elaborated to secure the distributed learning process; and a cooperation-enforcing control strategy is devised to elicit full payment from the requestor. Extensive simulations are carried out to evaluate the performance of our designed schemes.

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Section: A Blockchain-based Mobile Crowdsensingmentioning

confidence: 99%

Blockchain and Federated Edge Learning for Privacy-Preserving Mobile Crowdsensing

Wang

Cheng

2023

IEEE Internet Things J.

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“…To deal with participants' security threats, a blockchain‐based crowd‐sensing framework called CrowdChain is proposed in Reference 41 which provides security for the crowd‐sensing system through resisting record tampering or identifying cheating employs. In the proposed framework, the interactions between participants and fog nodes are recorded permanently and it is difficult for attackers to tamper data without consuming large computing power.…”

Section: Related Workmentioning

confidence: 99%

A distributed reliable collusion‐free algorithm for selecting multiple coordinators in IOTA using fog computing

Alavizadeh,

Erfani,

Mirabi

et al. 2023

Concurrency and Computation

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SummaryIOTA is a distributed ledger technology with a new structure called Tangle, which offers high scalability, no fees, and near‐instant transfers for the internet‐of‐things (IoT) networks. The most important issue of IOTA is consensus achievement, which is handled by a single node acting as a coordinator. A single and default coordinator node exposes IOTA to the single point of failure and incomplete distribution issues. In this paper, a novel algorithm for selecting multiple coordinators to participate in the consensus process at milestones is proposed (i.e., MCS algorithm) to overcome the problem of a single coordinator in IOTA network. The MCS algorithm is applied in a two‐layered architecture including IoT devices (i.e., Layer 1) and fog nodes (i.e., Layer 2). We define and formulate four different properties as metrics to select multiple coordinators in this architecture. Moreover, a collusion list is defined to decrease the risk of collusion between fog nodes in the network. Experimental results show that using multiple fog nodes as coordinators in IOTA network can improve the average response time and average throughput while does not considerably sacrifice the total cost and system utilization in comparison with the use of a single default coordinator in IOTA network.

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“…Gu et al 17 investigated how to improve the safety of fog‐facilitate crowd sensing machines by applying blockchain and paying more attention to the sensing capability differences between users. A blockchain‐leaning attendance operation model for community feeling, Crowd Chain, was used in this study.…”

Section: Blockchain Technology On Energy‐aware Mobile Crowd Sensingmentioning

confidence: 99%

Blockchain technology for energy‐aware mobile crowd sensing approaches in Internet of Things

Sisi

Souri

2021

Trans Emerging Tel Tech

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Today, Blockchain technology is emerging to support high secured performance computing. To improve privacy and security factors of allocate, storage, and manage resources in edge computing, blockchain technology can be used to guarantee safe data transmission and communications in Internet of Things (IoT). Due to heterogeneity of resources to cover a range number of IoT applications, resource allocation has a key role for increasing performance of the IoT systems in edge computing. Also, a blockchain‐based resource allocation model can support Quality of Service (QoS) for IoT applications to ensure a high secured communication environment in edge computing. This paper reviews different existing green energy strategies based on blockchain technology in mobile crowd sensing. Mobile crowd sensing refers to all computational aspects related to smart city, smart home‐care, smart grid, smart transportations, smart medical systems, smart industry, manufacturing, and smart farming in IoT environments. This review aims to classify principles and technical aspects of green energy strategies based on blockchain technology in mobile crowd sensing with respect to technical aspects, applied algorithms, evaluation factors, and evaluation environments. Based on Systematic Literature Review (SLR), we selected 37 high potential and key relevant research studies to consider in this review. Based on analytical results, 35% of them were considered as private‐based blockchain methods, 33% of them were applied to the public‐based blockchain methods, 24% of them were considered as consortium‐based blockchain methods, and 8% of them were as hybrid‐based blockchain methods.

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Using blockchain to enhance the security of fog-assisted crowdsensing systems

Cited by 16 publications

References 16 publications

Blockchain and Federated Edge Learning for Privacy-Preserving Mobile Crowdsensing

Blockchain and Federated Edge Learning for Privacy-Preserving Mobile Crowdsensing

A distributed reliable collusion‐free algorithm for selecting multiple coordinators in IOTA using fog computing

Blockchain technology for energy‐aware mobile crowd sensing approaches in Internet of Things

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