Evidence map›Paper›PMID 33430274›Full record

ArticleSensors (Basel, Switzerland)2021

Hyperledger Fabric Blockchain for Securing the Edge Internet of Things.

Houshyar Honar Pajooh, Mohammad Rashid, Fakhrul Alam, Serge Demidenko

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Houshyar Honar PajoohDepartment of Mechanical and Electrical Engineering, Massey University, Auckland 0632, New Zealand.ORCID 0000-0002-0365-2958
Mohammad RashidDepartment of Mechanical and Electrical Engineering, Massey University, Auckland 0632, New Zealand.ORCID 0000-0002-0844-5819
Fakhrul AlamDepartment of Mechanical and Electrical Engineering, Massey University, Auckland 0632, New Zealand.ORCID 0000-0002-2455-3131
Serge DemidenkoDepartment of Mechanical and Electrical Engineering, Massey University, Auckland 0632, New Zealand.ORCID 0000-0001-9883-9311

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Providing security and privacy to the Internet of Things (IoT) networks while achieving it with minimum performance requirements is an open research challenge. Blockchain technology, as a distributed and decentralized ledger, is a potential solution to tackle the limitations of the current peer-to-peer IoT networks. This paper presents the development of an integrated IoT system implementing the permissioned blockchain Hyperledger Fabric (HLF) to secure the edge computing devices by employing a local authentication process. In addition, the proposed model provides traceability for the data generated by the IoT devices. The presented solution also addresses the IoT systems' scalability challenges, the processing power and storage issues of the IoT edge devices in the blockchain network. A set of built-in queries is leveraged by smart-contracts technology to define the rules and conditions. The paper validates the performance of the proposed model with practical implementation by measuring performance metrics such as transaction throughput and latency, resource consumption, and network use. The results show that the proposed platform with the HLF implementation is promising for the security of resource-constrained IoT devices and is scalable for deployment in various IoT scenarios.

Indexed as

data provenanceedge computinghyperledger fabricInternet of Thingssecurity and privacysmart contract

Identifiers

PMID33430274
PMCPMC7825674

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.