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AUV Trajectory Planning for Optimized Sensor Data Collection in Internet of Underwater Things

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  • Talal S. Almuzaini

    (School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney 2052, Australia
    School of Electrical Engineering, Islamic University of Madinah, Madinah 42351, Saudi Arabia)

  • Andrey V. Savkin

    (School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney 2052, Australia)

Abstract

Efficient and timely data collection in Underwater Acoustic Sensor Networks (UASNs) for Internet of Underwater Things (IoUT) applications remains a significant challenge due to the inherent limitations of the underwater environment. This paper presents a Value of Information (VoI)-based trajectory planning framework for a single Autonomous Underwater Vehicle (AUV) operating in coordination with an Unmanned Surface Vehicle (USV) to collect data from multiple Cluster Heads (CHs) deployed across an uneven seafloor. The proposed approach employs a VoI model that captures both the importance and timeliness of sensed data, guiding the AUV to collect and deliver critical information before its value significantly degrades. A forward Dynamic Programming (DP) algorithm is used to jointly optimize the AUV’s trajectory and the USV’s start and end positions, with the objective of maximizing the total residual VoI upon mission completion. The trajectory design incorporates the AUV’s kinematic constraints into travel time estimation, enabling accurate VoI evaluation throughout the mission. Simulation results show that the proposed strategy consistently outperforms conventional baselines in terms of residual VoI and overall system efficiency. These findings highlight the advantages of VoI-aware planning and AUV–USV collaboration for effective data collection in challenging underwater environments.

Suggested Citation

  • Talal S. Almuzaini & Andrey V. Savkin, 2025. "AUV Trajectory Planning for Optimized Sensor Data Collection in Internet of Underwater Things," Future Internet, MDPI, vol. 17(7), pages 1-22, June.
  • Handle: RePEc:gam:jftint:v:17:y:2025:i:7:p:293-:d:1691347
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    References listed on IDEAS

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    1. Abeer Almutairi & Xavier Carpent & Steven Furnell, 2024. "Recommendation-Based Trust Evaluation Model for the Internet of Underwater Things," Future Internet, MDPI, vol. 16(9), pages 1-26, September.
    2. Salvador López-Barajas & Pedro J. Sanz & Raúl Marín-Prades & Juan Echagüe & Sebastian Realpe, 2025. "Network Congestion Control Algorithm for Image Transmission—HRI and Visual Light Communications of an Autonomous Underwater Vehicle for Intervention," Future Internet, MDPI, vol. 17(1), pages 1-21, January.
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