Blockchain and Edge Computing Nexus: A Large-scale Systematic Literature Review

Chuhao Qin; Evangelos Pournaras; Fatemeh Banaie; Rabiya Khalid; Zeinab Nezami; Zhuolun Li

REVIEW 3 major objections 4 minor 198 references

Blockchain and Edge Computing Nexus: A Large-scale Systematic Literature Review

T0 review · 3 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read The largest systematic map of blockchain and edge computing research to date, drawn from 921 papers, finds the field splits into four recurring study patterns.

desk verdict Largest coded map of the blockchain-edge literature to date, with open data—but the quantitative core lacks reported inter-rater reliability. read the letter →

arxiv 2506.08636 v1 pith:DJ6TC27I submitted 2025-06-10 cs.DC

Zeinab Nezami , Zhuolun Li , Chuhao Qin , Fatemeh Banaie , Rabiya Khalid , Evangelos Pournaras This is my paper

classification cs.DC

keywords systematicliteraturereviewedgecomputingblockchaindistributedledgermultiplecorrespondenceanalysistaxonomypermissionedvspermissionlessresearchpatterns

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper claims to provide the first large-scale systematic literature review of the blockchain-and-edge-computing nexus, covering 921 papers published between 2015 and October 2022. It builds a 22-dimension, 287-attribute taxonomy and applies quantitative analysis plus multiple correspondence analysis to show how the two paradigms interact, co-evolve, and cluster into distinct research styles. A sympathetic reader should care because the result is an empirical map of an entire research area: which problems dominate, which design choices are actually used, and where the field is maturing. The paper argues that blockchain-assisted edge computing, especially for security and privacy, is the prevalent direction, while edge-assisted blockchain work remains less developed.

What carries the argument

The load-bearing apparatus is the taxonomy itself: 22 dimensions covering scope, application, problem, contribution, AI method, allocation, metrics, technology, TRL, open data, communication, evaluation, security, privacy, sustainability, blockchain platform, type, permission, consensus, chain, and reward, together containing 287 attributes. The taxonomy is constructed following a published taxonomy-development method and then applied to every paper. The quantitative engine is multiple correspondence analysis (MCA), which reduces the categorical coding into meta-dimensions; the paper retains four meta-dimensions and interprets them as the four study patterns. The taxonomy supplies the raw structure, and MCA supplies the grouping that turns manual labels into discrete patterns.

What would settle it

Take a random sample of roughly 100 of the 921 papers, have an independent team re-code them with the same published taxonomy, and measure inter-rater agreement (for example, Cohen's kappa). If agreement falls well below conventional thresholds, the reported percentages, temporal trends, and the four MCA patterns cannot be treated as stable facts about the literature.

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Extended reading notes

Core claim

The central claim is that the blockchain-edge computing literature, far from being an undifferentiated mass, separates into four distinguishable study patterns: private permissioned design, technology implementation, proof-of-concept research, and public permissionless design. The paper further claims that 75% of the reviewed papers take the perspective of blockchain assisting edge computing, while only 19% use edge computing to assist blockchain and 6% pursue a full synergy. It reports that permissioned blockchains dominate the field, that privacy is the fastest-rising problem area, and that the choice of permissioned versus permissionless design is the key determinant of how the two paradigms are combined. These claims rest on a manually constructed taxonomy and a machine-learning analysis of attribute co-occurrence across the 921 papers.

Load-bearing premise

The entire quantitative analysis depends on the five authors' manual coding of 921 papers into the taxonomy being accurate and consistent, and no inter-rater reliability measure is reported for that coding.

Editorial extensions

If this is right

Researchers can use the taxonomy as a checklist to position new work and to notice under-explored combinations, such as the small 6% share of studies that genuinely integrate both paradigms.
The prevalence of permissioned blockchains in about 75% of papers implies that blockchain-edge systems are mostly studied in controlled, consortium-style settings, while scalability-focused work almost always assumes permissioned design.
The sharp rise in privacy-related papers, with a 74% relative increase from 2020 to 2021, signals that privacy protection is becoming a central motivation for combining blockchain with edge computing.
Edge-assisted blockchain research (Perspective 2) is less mature in both methodological contribution and technology readiness, marking it as a comparatively open research direction.
The four MCA patterns give the field a compact vocabulary: private permissioned design, technology implementation, proof-of-concept research, and public permissionless design, each with distinct technology and consensus choices.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If the manual coding is reliable, the released open dataset could be used to train an automated classifier that tracks the blockchain-edge literature beyond October 2022, effectively extending the review without a full re-coding effort.
The paper's cutoff predates the recent surge of Decentralized Physical Infrastructure Networks; applying the same taxonomy to 2023-2025 publications would test whether the four patterns persist or whether the permissionless, incentive-heavy pattern grows.
The observed dominance of blockchain-for-edge security may partly reflect publication incentives in the security research community rather than engineering demand, meaning the 75/19/6 perspective split should not be read as a measure of real-world deployment.
The four MCA patterns could serve as sampling strata for future deep-dive meta-analyses, letting reviewers compare findings within each pattern rather than averaging across a heterogeneous corpus.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

First thing to know: this is the largest systematic review of the blockchain–edge computing nexus I've seen, and the first to apply MCA to a coded corpus this size. It puts the dataset online, which is the right way to do this kind of work. The headline result—75% of papers take the blockchain-helps-edge perspective, permissioned designs dominate, security/privacy is the leading motivation—is plausible and roughly consistent with the qualitative surveys in the area.

What it does well: the scale. 921 papers, 22 dimensions, 287 attributes, and a transparent PRISMA-style search. The taxonomy is fine-grained and mostly well defined. The MCA is a legitimate tool for this data, and the four patterns (private permissioned design, technology implementation, proof-of-concept research, public permissionless design) are readable and connect to the descriptive statistics. Table I is a fair comparison with prior reviews.

The soft spot is the one the stress-test flags: no inter-rater reliability for the coding. Five authors coded 921 papers independently in screening rounds, but no kappa is reported for the taxonomy classification. Some dimension boundaries are genuinely fuzzy—security vs. privacy, framework vs. architecture, TRL levels. This matters most for the MCA patterns, which can shift with label noise. I don't think it breaks the paper, because the dominant proportions are large and the qualitative conclusions are corroborated by prior reviews. But the absence of any reliability estimate is a real gap, and the authors should either compute one for a sample or add a sensitivity analysis.

Second issue: the claim in Section IV-A that papers from 2022–2025 'confirm the key trends' is a bare assertion, no citation or data. Given that the review stops in October 2022, this needs either evidence or deletion.

Minor: some typos in the figures. Self-citation is present but not abused; the cited taxonomy paper [8] is a reasonable methodological basis.

Bottom line: this is a useful field map for anyone entering blockchain-edge research, and the open dataset is a genuine contribution. It deserves peer review. The reliability gap is addressable and the post-2022 claim is easy to fix. I'd send it to a serious venue with the expectation of a revision.

Referee Report

3 major / 4 minor

Summary. This manuscript reports a systematic literature review of the blockchain-edge computing nexus, covering 5,903 papers retrieved from Scopus, ScienceDirect, and Web of Science, of which 921 were retained after screening. The authors construct a taxonomy with 22 dimensions and 287 attributes and use it to code the retained papers. They report descriptive statistics, temporal trends, and a Multiple Correspondence Analysis from which they derive four 'study patterns': private permissioned design, technology implementation, proof of concept research, and public permissionless design. The paper also states that blockchain-assisted edge computing for privacy and security is the dominant research direction. The authors make the coded dataset and analysis code publicly available.

Significance. If the underlying coded dataset is reliable, this is the largest systematic quantitative review of blockchain-edge computing to date, and it offers a reusable taxonomy and an open dataset that can support future meta-analyses. The three-perspective framing (blockchain-for-edge, edge-for-blockchain, synergistic) is a useful organizing device, and the attempt to use MCA to identify latent study patterns is methodologically distinctive in this literature. The public availability of the dataset and code is a clear strength and supports reproducibility. However, the paper's quantitative claims, including the reported percentages and the four MCA-based patterns, all rest on the manual assignment of 921 papers to 287 taxonomy attributes, and the manuscript provides no evidence of coding reliability. The significance of the contribution is therefore conditional on resolving this verification gap.

major comments (3)

[Section IV-A and Section III] The central quantitative results (e.g., the 75%/19%/6% perspective distribution in Section V-A, the permissioned/permissionless percentages in Section V-B, and the four MCA patterns in Section V-C) are derived entirely from the authors' manual coding of 921 papers along the 22-dimension taxonomy. Although Section IV-A states that five reviewers independently assessed exclusion criteria, no inter-rater reliability statistic (Cohen's kappa, Fleiss' kappa, or Krippendorff's alpha) is reported for the exclusion decisions or for the taxonomy classification itself. Given that several dimension boundaries are genuinely ambiguous (e.g., Problem: Security vs. Problem: Privacy, Contribution: Framework vs. Architecture, and TRL assignments), the stability of the coded dataset is a load-bearing assumption. Please add a formal reliability assessment, for example by having multiple coders independently code a random subsample and reporting agreement statistics, or otherwise provide evidence that the coding is consistent across raters.
[Section V-C and Figure 6a] The claim that 'four combinations are sufficient to capture the variance' is not supported by the reported information. Figure 6a displays eigenvalues on the order of 0.01-0.04, which in MCA typically corresponds to a small fraction of total inertia, yet no cumulative explained variance or eigenvalue-based selection criterion is reported. Without a stated threshold (e.g., cumulative inertia at some percentage, or a scree-test rule), the selection of the top four meta-dimensions and the resulting four patterns is not justified. Please report the cumulative variance accounted for by the selected dimensions and the criterion used to decide that four dimensions are sufficient.
[Section IV-A] The statement that 'Other articles published in the last 3 years confirm the key trends captured within this chosen time period' is made without citation, data, or comparison. Because the paper was published in 2025 but the corpus ends in October 2022, the representativeness of the time window is central to the paper's claim to describe the 'current state' of the field. This assertion needs empirical support, for instance a supplementary search covering 2022-2025 with a comparison of key attribute distributions, or it should be removed and the claims explicitly scoped to 2015-October 2022.

minor comments (4)

[Figure 6] The figure labels are inconsistent and confusing: the caption lists '(c-f) top 20 attributes contributed to the four meta-dimensions,' but the internal labels duplicate '(c) Top 20 attributes contributed to Meta-Dimension 1/2' and then switch to '(e) Central Traits...' and '(f) Central Traits...' for Meta-Dimensions 3 and 4. Please harmonize the subfigure labels with the pattern names used in the text.
[Table II and Figure 6] There are several typographical inconsistencies in technology names: 'V ANET' in Table II, 'Resperry Pi' and 'Resberry Pi' in Figure 6, and 'Raspberry Pi' in the text. Please standardize these spellings.
[Section IV-A and Figure 1] Figure 1 lists an exclusion criterion 'Extended Paper: has been expanded or supplemented in another publication,' but this criterion is not described in the text of Section IV-A. Please either add a description of how this criterion was applied or remove it from the figure for consistency.
[Section V-A] The statement that 'the primary problem addressed in Perspective 1 is security' should be supported by a statistical test or at least a confidence measure, since Figure 3 shows security and performance percentages that are visually close. A difference-of-proportions test would make the claim more robust.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the SLR's quantitative and MCA findings are new empirical artifacts derived from external papers, with self-citations confined to background and methodology.

full rationale

The paper's central claims are empirical descriptions of 921 externally published papers coded along a 22-dimension taxonomy. The percentages (e.g., 75% Perspective 1), temporal trends, and four MCA patterns are all computed from this coded dataset; they are not defined in terms of the conclusions, nor are any fitted parameters renamed as predictions. The taxonomy is constructed using the Nickerson et al. method and adapted from prior DLT taxonomy work, including the authors' own [8], but the resulting 22 dimensions and 287 attributes are applied to new literature rather than being the output of the review; citation of [8] is methodological background, not a load-bearing premise. Several background self-citations ([5], [6], [12], [17]) support introductory statements about edge computing and smart-city applications and do not enter the derivation chain of the SLR's findings. A reproducibility concern exists: inter-rater reliability for the manual coding is not reported, so a different coding team might shift some labels. That is a validity/reliability risk, but not circularity, because the claims would still be about the coded dataset rather than being forced by the coding scheme's definitions. The unsupported assertion that papers from the last three years 'confirm the key trends' is an evidentiary gap, not a circular step.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The central claims rest on the coded dataset. The main risk is not free parameters or circularity, but the reliability of manual coding and corpus selection. The taxonomy dimensions themselves are derived from prior work and iterative refinement, which is standard for SLRs. No new entities are invented.

assumptions (3)

domain assumption The manual coding of 921 papers along 22 dimensions is consistent and accurate across the five reviewers.
The quantitative claims, including percentages and MCA patterns, are computed from this coding. No inter-rater reliability or coding disagreement measure is reported (Section IV-A).
domain assumption The three databases Scopus, ScienceDirect, and Web of Science, with the specific search query, capture the relevant corpus.
The sample size of 921 is derived from this search; the absence of, e.g., IEEE Xplore and ACM could change the patterns. The authors justify database choice via reference [191], but the query and database set determine the entire corpus (Section IV-A).
ad hoc to paper The time window 2015 to October 2022 is representative of the current state of the field.
The paper states that papers from the last 3 years 'confirm the key trends,' but provides no evidence for this, and the SLR is published in 2025. This is stated in Section IV-A.

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Cite this review

Pith. "Pith review of Blockchain and Edge Computing Nexus: A Large-scale Systematic Literature Review." pith.science (2026). https://pith.science/paper/DJ6TC27I

@misc{pith2026250608636,
  author       = {Pith},
  title        = {Pith review of: Blockchain and Edge Computing Nexus: A Large-scale Systematic Literature Review},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/DJ6TC27I}},
  note         = {Machine review of arXiv:2506.08636}
}

read the original abstract

Blockchain and edge computing are two instrumental paradigms of decentralized computation, driving key advancements in Smart Cities applications such as supply chain, energy and mobility. Despite their unprecedented impact on society, they remain significantly fragmented as technologies and research areas, while they share fundamental principles of distributed systems and domains of applicability. This paper introduces a novel and large-scale systematic literature review on the nexus of blockchain and edge computing with the aim to unravel a new understanding of how the interfacing of the two computing paradigms can boost innovation to provide solutions to timely but also long-standing research challenges. By collecting almost 6000 papers from 3 databases and putting under scrutiny almost 1000 papers, we build a novel taxonomy and classification consisting of 22 features with 287 attributes that we study using quantitative and machine learning methods. They cover a broad spectrum of technological, design, epistemological and sustainability aspects. Results reveal 4 distinguishing patterns of interplay between blockchain and edge computing with key determinants the public (permissionless) vs. private (permissioned) design, technology and proof of concepts. They also demonstrate the prevalence of blockchain-assisted edge computing for improving privacy and security, in particular for mobile computing applications.

Figures

Figures reproduced from arXiv: 2506.08636 by the authors.

**Figure 2.** Key themes and focal points within the interface of [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗

**Figure 3.** Top five problems (left) and applications (right) studied in the three perspectives, shown in percentage of papers [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗

Figures from the paper (3 more)

**Figure 4.** Figure 4: How the dimension attributes in 95% of data studied evolve over time [PITH_FULL_IMAGE:figures/full_fig_p011_4.png]

**Figure 5.** Figure 5: Interplay of design dimensions in the literature on blockchain and edge computing [PITH_FULL_IMAGE:figures/full_fig_p011_5.png]

**Figure 6.** Figure 6: MCA analysis: (a) retained variance across meta-dimensions, (b) design attributes within the top four meta-dimensions, (c-f) [PITH_FULL_IMAGE:figures/full_fig_p012_6.png]

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