Evaluating Energy-Efficient AUV Path Planning and Data Collection: Insights from a Novel Benchmark Study in UWSNs
Received: 25 May 2025 | Revised: 13 June 2025, 1 July 2025, and 7 July 2025 | Accepted: 9 July 2025 | Online: 6 October 2025
Corresponding author: Chiranth Ramesh
Abstract
Underwater Wireless Sensor Networks (UWSNs) play a significant role in marine applications, including environmental monitoring, ocean floor mapping, and disaster response. However, the energy constraints of sensor nodes and Autonomous Underwater Vehicles (AUVs), as well as the high cost of underwater acoustic communication, present major design challenges. This paper introduces Energy-Conscious Optimization of AUVs (ECO-AUV), a new framework that uses energy-aware K-Means clustering and the A* heuristic search algorithm to improve underwater data collection. ECO-AUV is specially designed to minimize total energy expenditure by improving intra-cluster communication procedures and AUV navigation paths. The framework provides reliable data collection and transfer through dynamic route planning that accounts for environmental conditions such as ocean currents and seafloor anomalies. Extensive simulations were carried out to compare ECO-AUV with two recent hybrid methods: Particle Swarm Optimization (PSO) with Genetic Algorithms (GAs), and Artificial Bee Colony (ABC) with Ant Colony Optimization (ACO). Results demonstrate that ECO-AUV is significantly more energy-efficient, creates more optimized traversal patterns, offering a high Packet Delivery Ratio (PDR) of 98.5%. Additionally, the framework exhibits low computational complexity, enabling its application in real-time applications. These results establish ECO-AUV as a scalable, energy-efficient solution for strong underwater sensing and communication.
Keywords:
autonomous underwater vehicle, energy-aware clustering, hybrid optimization, heuristic routing, marine monitoring systemDownloads
References
Y. Liu, H. Wang, L. Cai, X. Shen, and R. Zhao, "Fundamentals and Advancements of Topology Discovery in Underwater Acoustic Sensor Networks: A Review," IEEE Sensors Journal, vol. 21, no. 19, pp. 21159–21174, Oct. 2021.
N.-C. Wang, Y.-L. Chen, Y.-F. Huang, C.-M. Chen, W.-C. Lin, and C.-Y. Lee, "An Energy Aware Grid-Based Clustering Power Efficient Data Aggregation Protocol for Wireless Sensor Networks," Applied Sciences, vol. 12, no. 19, Oct. 2022, Art. no. 9877.
M. U. Khan, P. Otero, and M. Aamir, "An Energy Efficient Clustering Routing Protocol Based on Arithmetic Progression for Underwater Acoustic Sensor Networks," IEEE Sensors Journal, vol. 24, no. 5, pp. 6964–6975, Mar. 2024.
U. E. Zachariah and L. Kuppusamy, "A hybrid approach to energy efficient clustering and routing in wireless sensor networks," Evolutionary Intelligence, vol. 15, no. 1, pp. 593–605, Mar. 2022.
M. Z. Ghawy et al., "An Effective Wireless Sensor Network Routing Protocol Based on Particle Swarm Optimization Algorithm," Wireless Communications and Mobile Computing, vol. 2022, no. 1, May 2022, Art. no. 8455065.
N. Li, J. Yan, Z. Zhang, J.-F. Martínez-Ortega, and X. Yuan, "Geographical and Topology Control-Based Opportunistic Routing for Ad Hoc Networks," IEEE Sensors Journal, vol. 21, no. 6, pp. 8691–8704, Mar. 2021.
S. P. Singh and S. C. Sharma, "A Novel Energy Efficient Clustering Algorithm for Wireless Sensor Networks," Engineering, Technology & Applied Science Research, vol. 7, no. 4, pp. 1775–1780, Aug. 2017.
A. Fathalla, A. Salah, M. A. Mohamed, N. I. Lestari, and M. Bekhit, "A Novel Dual Prediction Scheme for Data Communication Reduction in IoT-Based Monitoring Systems," in IoT as a Service: 7th EAI International Conference, Sydney, Australia, 2021, pp. 208–220.
W. Mao, Z. Zhao, Z. Chang, G. Min, and W. Gao, "Energy-Efficient Industrial Internet of Things: Overview and Open Issues," IEEE Transactions on Industrial Informatics, vol. 17, no. 11, pp. 7225–7237, Nov. 2021.
K. B. Vikhyath and N. A. Prasad, "Combined Osprey-Chimp Optimization for Cluster Based Routing in Wireless Sensor Networks: Improved DeepMaxout for Node Energy Prediction," Engineering, Technology & Applied Science Research, vol. 13, no. 6, pp. 12314–12319, Dec. 2023.
S. Kamel, A. A. Qahtani, and A. S. M. Al-Shahrani, "Particle Swarm Optimization for Wireless Sensor Network Lifespan Maximization," Engineering, Technology & Applied Science Research, vol. 14, no. 2, pp. 13665–13670, Apr. 2024.
M. Majid et al., "Applications of Wireless Sensor Networks and Internet of Things Frameworks in the Industry Revolution 4.0: A Systematic Literature Review," Sensors, vol. 22, no. 6, Mar. 2022, Art. no. 2087.
T. M. Behera et al., "Energy-Efficient Routing Protocols for Wireless Sensor Networks: Architectures, Strategies, and Performance," Electronics, vol. 11, no. 15, Aug. 2022, Art. no. 2282.
M. A. Jamshed, K. Ali, Q. H. Abbasi, M. A. Imran, and M. Ur-Rehman, "Challenges, Applications, and Future of Wireless Sensors in Internet of Things: A Review," IEEE Sensors Journal, vol. 22, no. 6, pp. 5482–5494, Mar. 2022.
M. S, B. N M, S. N, M. H N, P. S, and D. B L, "An Efficient Big Data Gathering in Wireless Sensor Network using Reconfigurable Node Distribution Algorithm," in 2022 Fourth International Conference on Cognitive Computing and Information Processing, Bengaluru, India, 2022, pp. 1–6.
A. K. Subramanian, U. Ghosh, S. Ramaswamy, W. S. Alnumay, and P. K. Sharma, "PrEEMAC: Priority based energy efficient MAC protocol for Wireless Body Sensor Networks," Sustainable Computing: Informatics and Systems, vol. 30, June 2021, Art. no. 100510.
A. I. Al-Sulaifanie, B. K. Al-Sulaifanie, and S. Biswas, "Recent trends in clustering algorithms for wireless sensor networks: A comprehensive review," Computer Communications, vol. 191, pp. 395–424, July 2022.
J. Tang, G. Liu, and Q. Pan, "A Review on Representative Swarm Intelligence Algorithms for Solving Optimization Problems: Applications and Trends," IEEE/CAA Journal of Automatica Sinica, vol. 8, no. 10, pp. 1627–1643, Oct. 2021.
Y.-F. Pu, P. Siarry, W.-Y. Zhu, J. Wang, and N. Zhang, "Fractional-Order Ant Colony Algorithm: A Fractional Long Term Memory Based Cooperative Learning Approach," Swarm and Evolutionary Computation, vol. 69, Mar. 2022, Art. no. 101014.
V. A. Memos and K. E. Psannis, "UAV-Based Smart Surveillance System over a Wireless Sensor Network," IEEE Communications Standards Magazine, vol. 5, no. 4, pp. 68–73, Dec. 2021.
R. Kumar, D. Bhardwaj, and M. K. Mishra, "EBH-DBR: energy-balanced hybrid depth-based routing protocol for underwater wireless sensor networks," Modern Physics Letters B, vol. 35, no. 03, Jan. 2021, Art. no. 2150061.
R. Priyadarshi and R. R. Kumar, "Evolution of Swarm Intelligence: A Systematic Review of Particle Swarm and Ant Colony Optimization Approaches in Modern Research," Archives of Computational Methods in Engineering, Mar. 2025.
W. Deng, S. Shang, X. Cai, H. Zhao, Y. Song, and J. Xu, "An improved differential evolution algorithm and its application in optimization problem," Soft Computing, vol. 25, no. 7, pp. 5277–5298, Apr. 2021.
G. Tang, C. Tang, C. Claramunt, X. Hu, and P. Zhou, "Geometric A-Star Algorithm: An Improved A-Star Algorithm for AGV Path Planning in a Port Environment," IEEE Access, vol. 9, pp. 59196–59210, 2021.
L. Li, X. Jin, C. Lu, Z. Wei, and J. Li, "Modelling and Simulation on Acoustic Channel of Underwater Sensor Networks," Wireless Communications and Mobile Computing, vol. 2021, no. 1, Nov. 2021, Art. no. 8263600.
T. M. Shami, A. A. El-Saleh, M. Alswaitti, Q. Al-Tashi, M. A. Summakieh, and S. Mirjalili, "Particle Swarm Optimization: A Comprehensive Survey," IEEE Access, vol. 10, pp. 10031–10061, 2022.
B. Alhijawi and A. Awajan, "Genetic algorithms: theory, genetic operators, solutions, and applications," Evolutionary Intelligence, vol. 17, no. 3, pp. 1245–1256, June 2024.
A. Al-Nasser, R. Almesaeed, and H. Al-Junaid, "A Comprehensive Survey on Routing and Security in Mobile Wireless Sensor Networks," International Journal of Electronics and Telecommunications, vol. 67, no. 3, pp. 483–496, July 2021.
S. El Khediri, "Wireless sensor networks: a survey, categorization, main issues, and future orientations for clustering protocols," Computing, vol. 104, no. 8, pp. 1775–1837, Aug. 2022.
S. Ullah et al., "Reliable and Delay Aware Routing Protocol for Underwater Wireless Sensor Networks," IEEE Access, vol. 11, pp. 116932–116943, 2023.
K. Debasis, L. D. Sharma, V. Bohat, and R. S. Bhadoria, "An Energy-Efficient Clustering Algorithm for Maximizing Lifetime of Wireless Sensor Networks using Machine Learning," Mobile Networks and Applications, vol. 28, no. 2, pp. 853–867, Apr. 2023.
A. K. Gautam and R. Kumar, "A comprehensive study on key management, authentication and trust management techniques in wireless sensor networks," SN Applied Sciences, vol. 3, no. 1, Jan. 2021, Art. no. 50.
N. Subramani, P. Mohan, Y. Alotaibi, S. Alghamdi, and O. I. Khalaf, "An Efficient Metaheuristic-Based Clustering with Routing Protocol for Underwater Wireless Sensor Networks," Sensors, vol. 22, no. 2, Jan. 2022, Art. no. 415.
K. A. Mahmoodi and M. Uysal, "Energy Aware Trajectory Optimization of Solar Powered AUVs for Optical Underwater Sensor Networks," IEEE Transactions on Communications, vol. 70, no. 12, pp. 8258–8269, Dec. 2022.
S. Sandhiyaa and C. Gomathy, "A cross-layer approach for load balancing and energy-efficient QoS-based routing reliability for UWSN," Alexandria Engineering Journal, vol. 85, pp. 333–343, Dec. 2023.
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