AUV Trajectory Planning for Optimized Sensor Data Collection in Internet of Underwater Things
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 p...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Future Internet |
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| Online Access: | https://www.mdpi.com/1999-5903/17/7/293 |
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| author | Talal S. Almuzaini Andrey V. Savkin |
| author_facet | Talal S. Almuzaini Andrey V. Savkin |
| author_sort | Talal S. Almuzaini |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c2ec51f030d2470f892a8de9f825c87a |
| institution | Kabale University |
| issn | 1999-5903 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj-art-c2ec51f030d2470f892a8de9f825c87a2025-08-20T03:32:26ZengMDPI AGFuture Internet1999-59032025-06-0117729310.3390/fi17070293AUV Trajectory Planning for Optimized Sensor Data Collection in Internet of Underwater ThingsTalal S. Almuzaini0Andrey V. Savkin1School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney 2052, AustraliaSchool of Electrical Engineering and Telecommunications, University of New South Wales, Sydney 2052, AustraliaEfficient 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.https://www.mdpi.com/1999-5903/17/7/293internet of underwater things (IoUT)underwater acoustic sensor network (UASN)sensor data collectionautonomous underwater vehicle (AUV)unmanned surface vehicle (USV)value of information (VoI) |
| spellingShingle | Talal S. Almuzaini Andrey V. Savkin AUV Trajectory Planning for Optimized Sensor Data Collection in Internet of Underwater Things Future Internet internet of underwater things (IoUT) underwater acoustic sensor network (UASN) sensor data collection autonomous underwater vehicle (AUV) unmanned surface vehicle (USV) value of information (VoI) |
| title | AUV Trajectory Planning for Optimized Sensor Data Collection in Internet of Underwater Things |
| title_full | AUV Trajectory Planning for Optimized Sensor Data Collection in Internet of Underwater Things |
| title_fullStr | AUV Trajectory Planning for Optimized Sensor Data Collection in Internet of Underwater Things |
| title_full_unstemmed | AUV Trajectory Planning for Optimized Sensor Data Collection in Internet of Underwater Things |
| title_short | AUV Trajectory Planning for Optimized Sensor Data Collection in Internet of Underwater Things |
| title_sort | auv trajectory planning for optimized sensor data collection in internet of underwater things |
| topic | internet of underwater things (IoUT) underwater acoustic sensor network (UASN) sensor data collection autonomous underwater vehicle (AUV) unmanned surface vehicle (USV) value of information (VoI) |
| url | https://www.mdpi.com/1999-5903/17/7/293 |
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