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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.
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Keren Ji
Yakun Gao
Wenwen Hu
Scientific Reports
Hebei University of Engineering
Hebei GEO University
Shijiazhuang University
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Ji et al. (Thu,) studied this question.
www.synapsesocial.com/papers/6a080af2a487c87a6a40d034 — DOI: https://doi.org/10.1038/s41598-026-52734-x