Semantic Communication for the Internet of Underwater Things: Architectures, Applications, Challenges, and Future Directions

Asiya Jehangir; Hanane Lamaazi; Nasir Saeed; Ruhul Amin Khalil; Saddaf Rubab

REVIEW 5 major objections 4 minor 1 cited by

Semantic Communication for the Internet of Underwater Things: Architectures, Applications, Challenges, and Future Directions

T0 review · 5 major / 4 minor · reviewed 2026-08-03 · deepseek-v4-flash

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Pith's one-line read This survey argues that semantic communication can cut underwater data to 0.8–3.3% of its original size while preserving meaning, and lays out the path to make it work in the deep sea.

desk verdict Useful taxonomy, but the survey's quantitative claims don't survive arithmetic — the central comparison table is off by orders of magnitude. read the letter →

arxiv 2601.13289 v2 pith:4YOSEUIT submitted 2026-01-19 eess.SP

Ruhul Amin Khalil , Asiya Jehangir , Hanane Lamaazi , Saddaf Rubab , Nasir Saeed This is my paper · ORCID

classification eess.SP

keywords semanticcommunicationInternetofUnderwaterThingsacousticnetworkscompressiongenerativeAIfederatedlearningknowledgegraphsimagetransmission

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 survey tries to establish that semantic communication (SC) can solve the bandwidth, latency, and energy bottlenecks of the Internet of Underwater Things (IoUT). The load-bearing claim is that SC compresses underwater image data to 0.8–3.3% of its original size, keeps semantic similarity above 0.85 even at 0 dB signal-to-noise ratio, and cuts energy per bit to 0.1–0.3 mJ/bit — making real-time, long-duration underwater missions feasible. The paper organizes existing SC methods into a layered architecture (sensing, network/transport, processing, application), reviews learning-driven tools such as large language models, knowledge graphs, diffusion models, and federated learning, and identifies standardization and testbed validation as the critical next steps. If the quantitative claims hold, SC would turn today's kilobit-rate acoustic links into a practical medium for image and alert delivery in the deep sea.

What carries the argument

The argument rides on a stack of machinery: semantic encoders built from large language models and vision-language models (e.g., BLIP) that localize task-relevant regions and compress them to small text descriptors; generative decoders (e.g., ControlNet, Stable Diffusion) that reconstruct images from those descriptors; knowledge graphs/ontologies (e.g., OWL 2) that prioritize data by application intent; and a four-layer architecture (sensing, network/transport, processing, application) that places edge intelligence and semantic reconstruction at the right points. The key quantitative lever is the semantic efficiency metric SE = I_s/(B·T), which the paper argues replaces bit-level metrics lik

What would settle it

Compute the effective throughput claimed in Table II: a 1–50 kbps acoustic link compressed 30–125× yields at most ~6 Mbps of original-data equivalent, not 1.2–4 Gbps; a sea trial measuring end-to-end latency of an SC image transmission (acoustic propagation alone is ~1 s per km) would also settle whether 8–12 ms latency is physically possible for any non-zero-distance link.

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Extended reading notes

Core claim

The paper's central claim is that meaning-driven communication is not just an incremental improvement but a necessary shift for underwater networks. By transmitting semantic descriptors instead of raw bits, SC systems reportedly reduce data volume by 30–125×, maintain semantic similarity above 0.85 at 0 dB SNR (where conventional coding fails below 10 dB), and lower energy per bit by 10–30× relative to traditional acoustic transmission. To support this, the paper synthesizes a four-layer SC architecture, details semantic channel models and efficiency metrics (e.g., SE = I_s/(B·T)), and reviews AI/ML encoders, federated learning, hybrid acoustic-optical-RF links, and edge intelligence. The pa

Load-bearing premise

The survey's case rests on the accuracy of quantitative performance figures taken from prior studies — 0.8–3.3% compression, 0.85+ semantic similarity at 0 dB SNR, and 0.1–0.3 mJ/bit — and on the internal consistency of its comparison tables, but the paper itself contains conflicting numbers (e.g., 1.22 million vs 1.2 billion market size) and an unreferenced 'Table??'.

Editorial extensions

If this is right

Underwater image and alert transmission over narrowband acoustic links becomes practical; a 10 MB image could travel as a sub-0.5 MB semantic descriptor and be reconstructed at the receiver.
Energy-constrained IoUT deployments could run months on battery, since semantic prioritization cuts transmissions by orders of magnitude.
The field should converge on shared ontologies (e.g., a common 'coral health' definition) before SC gains real interoperability.
Real-world testbeds (lake and ocean) become the gating step; simulation results alone won't validate semantic similarity claims.
Semantic security and privacy must be addressed because compressed semantic data carries mission-sensitive context, not just bits.

Reading between the lines

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

The reported 'effective throughput' of 1.2–4 Gbps in Table II is arithmetically inconsistent with the underlying 1–50 kbps acoustic links; if real SC systems only achieve modest multiples of the acoustic rate, the central efficiency advantage shrinks but may still be the 30–125× data reduction itself.
The 0.8–3.3% compression ratios likely depend on task-specific semantic priors (e.g., known coral-bleaching patterns), so generalization to novel scenarios may be far worse; a testable extension is to measure compression ratios on out-of-distribution underwater imagery.
If semantic similarity at 0 dB SNR holds under multipath and Doppler, then SC could replace heavy error-correcting codes in real-time AUV communication — a testable prediction for sea trials.
The market-size discrepancy (USD 1.22 million vs USD 1.2 billion) hints that some quantitative context in the survey is borrowed from incompatible sources; standardization of evaluation metrics, not just models, is a prerequisite for credible comparisons.

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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

Quick take: the survey gives a credible map of a niche area, but the numbers it leans on are not reliable. The taxonomy of semantic communication for underwater IoT is genuinely useful — the layered architecture, the learning-driven methods, and the future-directions lists are a reasonable starting point for someone entering the field. The paper also cites the recent literature broadly, and the author's prior work appears mostly as background, not as a crutch.

But the quantitative foundation is a real problem. Table II claims '1.2–4 Gbps effective throughput' from acoustic links of 1–50 kbps with 30–125x compression. Best case, 50 kbps × 125 = 6.25 Mbps, about three orders of magnitude short. The same table's '24–80× bandwidth efficiency gain' does not follow from the cited inputs. The market-size figure shifts from $1.22M in the intro to $1.2B in Section IV-A. There's a missing 'Table??' reference in Section II-B. And several performance numbers are attributed to blogs and marketing pages instead of peer-reviewed sources. That is not a nitpick — these numbers carry the paper's central claim that SC gives order-of-magnitude gains for IoUT.

The qualitative argument is defensible: semantic communication can plausibly cut redundant transmissions in bandwidth-starved underwater links. The paper just does not do the work to make that case quantitatively. A corrected revision, with tables that are internally consistent and traceable to the cited literature, could be a reasonable survey. As it stands, I would not cite it or trust its numbers.

For peer review: the topic is timely and the structural content is salvageable, so a serious editor might send it out — but the referee assignments should explicitly ask reviewers to verify every quantitative claim. If the bar is 'dependable as submitted,' this is not there. I agree with the reader's arithmetic; I give the taxonomy more credit than the reader does, but that does not rescue the paper.

Referee Report

5 major / 4 minor

Summary. The paper is a survey of semantic communication (SC) for the Internet of Underwater Things (IoUT). It argues that SC, by transmitting task-relevant meaning rather than raw data, can mitigate bandwidth, latency, and energy constraints of underwater acoustic channels. The paper reviews SC fundamentals, layered architectures, semantic channel modeling, learning-driven approaches (LLMs, vision-language models, generative models, federated learning), representative applications (environmental monitoring, archaeology, disaster response, AUV coordination), and future research directions. It repeatedly asserts quantitative benefits, including 0.8–3.3% compression ratios, 0.85+ semantic similarity at 0 dB SNR, 0.1–0.3 mJ/bit energy, and Gbps-scale effective throughput, and claims to be the first comprehensive synthesis of SC for IoUT.

Significance. The high-level thesis is defensible and timely: semantic communication is a plausible direction for resource-constrained underwater networks, and the paper assembles a broad set of references and a plausible taxonomy. However, the paper's distinctive value as a survey rests on providing reliable quantitative synthesis and a structured foundation. That value is currently undermined by internal arithmetic inconsistencies, contradictory market figures, missing table references, and citations that do not support the claimed numbers. If these issues are corrected, the survey could be useful as a roadmap; in its present form, its quantitative comparisons cannot be trusted.

major comments (5)

[Table II / Section II-E] The '1.2–4 Gbps effective throughput' row is arithmetically inconsistent with the same table's '1–50 kbps acoustic links' and '30–125× reduction'. Even taking the best case, 50 kbps × 125 = 6.25 Mbps, three orders of magnitude below 1.2 Gbps. The '24–80× bandwidth efficiency gain' cannot be recovered from these inputs either. This is a load-bearing number for the SC-for-IoUT case; either derive it explicitly from a cited source or remove it.
[Section I vs. Section IV-A] The paper gives contradictory market sizes: Section I states the IoUT market was USD 1.22 million in 2024 (growing to USD 5.46 million by 2034), while Section IV-A states the oceanographic monitoring market was 'over USD 1.2 billion in 2024' (projected USD 1.9 billion by 2033). These differ by three orders of magnitude. The opening motivation relies on the smaller figure; please reconcile and identify which market and source is meant.
[Section II-B] The text says 'Some fundamental characteristics of IoUT networks and the SC role in the underwater environment are summarized in Table??.' The 'Table??' placeholder indicates an incomplete manuscript. A missing table in the fundamentals section is not a minor cosmetic issue, as it directly affects the completeness of the proposed synthesis.
[Table VI / Section III-D] Several rows in Table VI have traceability problems. The 'Hybrid Acoustic/Optical' and 'Bayesian Error Resilience' rows cite [73], which is an 'Opti-Acoustic Semantic SLAM with Unknown Objects' paper—not a source for hybrid acoustic/optical communication or Markov logic network error resilience. The 'MI-Based MAC Layers' row cites [75], a survey of magnetic induction communications, not an IEEE P1902.1 draft standard, and the claimed '0.1 µJ/bit energy cost' and '0.1 dB/m attenuation' need verification. These unsupported metrics are used in Section III-D to support the paper's quantitative claims.
[Sections II-E and III-A] The repeated quantitative claims—'0.8–3.3% compression', '0.85+ semantic similarity at 0 dB SNR', '0.1–0.3 mJ/bit', '8–12 ms latency', '20% BER tolerance'—are asserted without a clear derivation or a rigorous mapping to the cited sources. Some references, e.g., [53] on deep underwater image compression, do not appear to report federated Vision Mamba or 200+ hour AUV lifetimes. Please either provide traceable derivations for each number or explicitly label them as illustrative values reported under specific, named conditions.

minor comments (4)

[Throughout] There are many typographical and language errors: 'Semamtic', 'paper-real-time', 'fFL', 'glycobiology', 'webcast sensor data', 'leachate', 'AL and ML' (Section IV-E), and inconsistent spacing in inline math. A thorough editorial pass is needed.
[Section II-C] The four-layer architecture description is clear, but the figure (Fig. 4) is not referenced or described in enough detail; also, the 'Processing Layer' is introduced as a third layer after the text says 'four key layers' but the numbered list covers only sensing, network/transport, and application before processing is inserted—please reorganize for consistency.
[References] The bibliography mixes archival papers with numerous non-archival blog posts and commercial pages (e.g., [71], [74], [96], [98], [99], [114], [157], [195], [239]). For a journal survey, please prioritize peer-reviewed or preprint sources, or at least clearly mark non-archival citations.
[Table IV] Some acronyms are defined inconsistently: 'CRITIC' is defined as 'Criteria Importance Through Intercriteria Correlation' in Table IV but as 'Criteria Importance Through Inter-criteria Correlation' in Table I's caption. Also, 'SAGE' is defined only in the acronym list; consider defining at first use.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: the survey's claims are citations to external work; self-citations are background, and internal numerical inconsistencies are correctness risks, not circularity.

full rationale

This is a survey, not a derivation paper. Its quantitative claims about SC—compression to 0.8–3.3% of original size, 0.85+ semantic similarity at 0 dB SNR, 0.1–0.3 mJ/bit, and Table II's '1.2–4 Gbps effective throughput'—are presented as citations to [9], [14], [15], [19], and other external sources, not as predictions derived from fitted parameters or from the authors' own prior results. I checked the load-bearing inference patterns: no quantity is defined in terms of a target claim; no parameter is fitted to a subset of data and then 'predicted'; no uniqueness theorem is imported from the authors' prior work; no ansatz is smuggled via self-citation; and no known result is merely renamed as a new contribution. The self-citations ([2], [21], [27], [31], [34]) support only background IoUT facts such as market need, bandwidth limits, sensing hardware, and hybrid links; they are not the basis of the paper's central synthesis or future-directions agenda. The paper even self-identifies open validation gaps, e.g., Table III: 'As a research gap some proposed concepts require empirical validation' and 'Standardization of semantic models for IoUT remains an open challenge.' The internal inconsistencies flagged in the manuscript—Table II's '1.2–4 Gbps effective throughput' is arithmetically incompatible with the same table's 1–50 kbps acoustic links and 30–125x compression (50 kbps × 125 = 6.25 Mbps), Section II-B's missing 'Table??', the USD 1.22 million vs USD 1.2 billion market figures in Sections I and IV-A, and Table VI's citing an underwater SLAM paper [73] for Bayesian error resilience—are traceability and verification failures, not circular reductions. Because the paper makes no derivation whose output is equivalent to its input, there is no significant circularity; the score reflects that non-finding.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The survey introduces no free parameters and no invented entities. Its central claims rest on standard domain facts, the accuracy of externally cited performance figures, and the feasibility of edge AI deployments. The second and third premises are weakened by the paper's own internal inconsistencies and non-archival sourcing.

assumptions (4)

standard math Shannon's three-level hierarchy of communication (technical, semantic, effectiveness) is the appropriate theoretical frame for SC.
Invoked in Section II-A as the conceptual foundation; no derivation is given beyond citation [23].
domain assumption Underwater acoustic channels have <10 kbps bandwidth, 1.5×10^3 m/s propagation speed, and typical link rates of 1–50 kbps.
Used in Sections I and II-B to motivate SC; treated as given domain facts.
domain assumption Cited quantitative SC results (0.8–3.3% compression, 0.85+ semantic similarity at 0 dB SNR, 0.1–0.3 mJ/bit, 200+ h AUV lifetime) are accurate transcriptions of the original sources.
Underlies Tables I, II, V, VI and Section II-E; several are internally inconsistent or sourced to non-archival pages.
domain assumption Underwater edge nodes can host the AI models within the stated budgets (3W power, 3.2M parameters, 2–4GB RAM).
Feasibility claims in Sections II-E and III depend on these budgets; the paper itself notes Stable Diffusion v2.1 needs 16GB VRAM, conflicting with 2–4GB edge RAM.

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Cite this review

Pith. "Pith review of Semantic Communication for the Internet of Underwater Things: Architectures, Applications, Challenges, and Future Directions." pith.science (2026). https://pith.science/paper/4YOSEUIT

@misc{pith2026260113289,
  author       = {Pith},
  title        = {Pith review of: Semantic Communication for the Internet of Underwater Things: Architectures, Applications, Challenges, and Future Directions},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4YOSEUIT}},
  note         = {Machine review of arXiv:2601.13289}
}

read the original abstract

The Internet of Underwater Things (IoUT) supports marine sensing, environmental monitoring, subsea inspection, and autonomous underwater operations. However, IoUT communication is constrained by limited bandwidth, long propagation delay, time-varying underwater channels, intermittent connectivity, and strict energy budgets. Semantic Communication (SC) offers a promising alternative by transmitting task-relevant meaning rather than raw data, thereby improving communication efficiency in resource-constrained underwater networks. This paper presents a critical and feasibility-aware survey of SC for IoUT, focusing on opportunities, challenges, limitations, and future research directions. We first review the fundamentals of SC-enabled IoUT systems, including semantic representations, layered architectures, semantic channel modeling, and task-oriented evaluation metrics. We then examine learning-driven approaches based on machine learning (ML), knowledge graphs (KGs), vision-language models (VLMs), generative models, and federated learning (FL), with emphasis on their feasibility under underwater edge constraints. Representative applications, including environmental monitoring, marine ecology, subsea infrastructure inspection, disaster response, and autonomous underwater vehicle (AUV) coordination, are analyzed from an SC perspective. Finally, we identify key research directions involving standardized semantic models, reproducible testbeds, compute--communication trade-offs, trustworthy reconstruction, hybrid underwater links, energy-aware edge intelligence, semantic security, digital twins (DTs), and cross-domain interoperability. This survey provides a structured foundation for developing reliable, efficient, and meaning-driven IoUT communication systems.

Figures

Figures reproduced from arXiv: 2601.13289 by the authors.

**Figure 2.** Illustration of SC-based image transmission in an IoUT environment. [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗

**Figure 3.** Systematic organization of this paper. methods supported by advanced AI models, such as LLMs, generative adversarial networks (GANs), and diffusion models. These intelligent encoders perform adaptive semantic compression and content prioritization. These encoders effectively select information useful for different applications, including anomaly detection and habitat analysis, and discard data that adds little valu… view at source ↗

Figures from the paper (2 more)

**Figure 4.** Figure 4: 1) Sensing/Device Layer: In the IoUT, the sensing layer is a base to build the SC structure. This layer contains various underwater sensors and equipment, such as temperature, pressure, and salinity sensors, AUVs, ROVs (Remotely Operated Vehicles), and specialized ima…

**Figure 4.** Figure 4: Semantic Architecture for IoUT system. in the scene and transmit compressed semantic descriptors instead of full-depth images. This reduced data volume allows for efficient communication over bandwidth-limited acoustic channels and minimizes energy consumption. Moreove…

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

Resource-Aware Topology Management for ISAC-Enabled TDOA Localization in IoUT Networks
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A resource-aware D-optimal topology manager plus a three-stage adaptive estimator improves TDOA localization accuracy in simulated underwater ISAC networks.

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