Evidence map›Paper›PMID 42135427›Full record

ArticleScientific reports2026

HTO-UWSN: a hierarchical task offloading protocol for underwater wireless sensor networks in aquatic sports monitoring systems.

Keren Ji, Yakun Gao, Wenwen Hu

Abstract read
In one paragraph

Article in Scientific reports, 2026. 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

3 authors.

Keren Ji *School of Physical Education and Health Engineering, Hebei University of Engineering, Handan, 056038, Hebei, China.
Yakun Gao *School of Physical Education and Health Engineering, Hebei University of Engineering, Handan, 056038, Hebei, China.
Wenwen Hu *Department of Physical Education, Shijiazhuang University, Shijiazhuang, 050035, Hebei, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Underwater sensor network (UWSN) has transformed aquatic sports like swimming, kayaking, and rowing through applications in sensing, motion analysis, and real-time feedback. However, the execution of complex and time-critical sports analytics tasks degrades its efficiency due to constrained hardware and energy resources. To overcome these constraints, this study proposes a hierarchical task offloading algorithm for underwater sensor networks (HTO-UWSN). The protocol is dynamic in nature and establishes a hierarchical cluster-based network architecture. It operates by deploying sensor nodes in the aquatic environment and wearable inertial sensor nodes attached to athletes. Nodes have been organized into a number of hierarchy levels in the form of a cluster headed by a resource-rich cluster head. The work of the leaf-level nodes includes performing on-body motion sensing and underwater data acquisition, while those at the intermediate nodes are for data forwarding and computation. When a task exceeds the processing or energy capacity of a lower-level node, it is offloaded to the nearest higher hierarchical level. The system uses a two-step hierarchical process. Firstly, ensemble learning classifies tasks for smart decision-making. Secondly, a graph partitioning algorithm is used to break complex tasks into parallel threads. Simulation results demonstrate that HTO-UWSN improves task execution rate by 67%, reliability by 55%, and scalability by 76%, suggesting its potential suitability for real-time aquatic sports monitoring systems.

Indexed as

Aquatic sports monitoringEnergy-efficient communicationHierarchical task offloadingReal-time data processingUnderwater wireless sensor networksWearable inertial sensors

Identifiers

PMID42135427
PMCPMC13315731

What Socratic holds

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LicenceCC BY-NC-ND
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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 Socratic graph.