Evidence map›Paper›PMID 41516509›Full record

ArticleSensors (Basel, Switzerland)2025

High-Efficiency Traceability Mechanism for Multimedia Data in Consumer Internet of Things Combined with Blockchain.

Tianyi Yan, Jimin Chen, Xiaorui Zhang, Gang Hu

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Tianyi YanCollege of Computer and Information Engineering, Nanjing Tech University, Nanjing 211816, China.
Jimin ChenCollege of Computer and Information Engineering, Nanjing Tech University, Nanjing 211816, China.
Xiaorui ZhangCollege of Computer and Information Engineering, Nanjing Tech University, Nanjing 211816, China.
Gang HuSchool of Management Science and Engineering, Anhui University of Technology, Ma'anshan 243099, China.

Funding

General Project of the National Natural Science Foundation of China 62272236
6 · The paper itself

Abstract

In the context of the rapid development of Consumer Internet of Things (CIoT), the manipulation and unauthorized distribution of multimedia content have raised serious concerns regarding copyright protection and data authenticity. Ensuring secure traceability and authenticity in complex network environments remains a major challenge. Traditional blockchain mechanisms often suffer from high latency during large-scale data queries, making them unsuitable for real-time CIoT applications. To address this, this paper proposes a high-efficiency blockchain-based multimedia data security traceability method. First, blockchain is integrated with the PROV model to guarantee operation transparency and data credibility. Second, a joint index structure comprising a fast index (using traceability positioning tables for cross-block jumps) and a multi-bucket index (using self-balancing binary trees) is designed. Experimental results demonstrate that compared to traditional blockchain and SEBDB methods, the proposed mechanism remains stable as data volume exceeds 2000 records. Specifically, the query time growth rate is significantly lower than linear scanning methods (12-18% vs. 45-62% for traditional methods), and the number of traversed records is reduced by 60-75% by avoiding full-chain traversal, verifying the method's superiority in handling high-frequency CIoT multimedia queries while providing protection against tampering and unauthorized distribution.

Indexed as

blockchainConsumer Internet of Thingsmultimedia dataPROV model

Identifiers

PMID41516509
PMCPMC12787852

What OpenQuestion holds

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Registered trials

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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.