Optimizing Data Transmission System Performance in Underwater Wireless Sensor Networks Using the Search and Rescue Algorithm
Saif Kadhim Mutar
1
(
)
Azam Andalib
2
(
)
Hossein Azgomi
3
(
)
Seyed Ali Sharifi
4
(
)
Keywords: Underwater Wireless Sensor Networks, Controlled Depth, Packet Delivery Rate, Clustering, Chaotic Search and Rescue Optimization Algorithm, Multi-Hop Data Transmission and Energy Balancing ,
Abstract :
Underwater Wireless Sensor Networks (UWSNs) have emerged as essential infrastructures for applications such as disaster management, environmental monitoring, and industrial underwater inspections. These networks consist of low-cost, resource-constrained floating sensor nodes operating in deep-sea environments. UWSNs face unique challenges, including limited bandwidth, high underwater pressure, and high error probability. Furthermore, maintaining node positions and managing energy consumption due to dynamic topologies and 3D deployment complicate their operation. To address these challenges, this study proposes an energy-efficient clustering-based routing method enhanced by a chaotic search and rescue evolutionary algorithm. The approach adjusts node depth, replaces low-energy nodes with higher-energy ones, and balances energy consumption through multi-hop data transmission. The simulation, conducted in a 10 × 10 × 10 km 3D underwater environment with water currents of 1–3 m/s, involved 100–500 sensors. Each sensor had 100 J of energy, 2 km communication range, and 200-byte packets. Performance was assessed using PDR, AEC, delay, dead nodes, and throughput across varying densities and data rates. The proposed method outperformed nine existing protocols, achieving the highest PDR, lowest energy use, and best overall efficiency. Simulation results demonstrate improvements in packet delivery and reception rates, reduced energy consumption, and increased network lifespan, indicating the proposed method’s reliability and effectiveness. Managers should prioritize higher sensor densities to enhance performance and energy efficiency in underwater networks. Adaptive data rate strategies can further improve throughput and reduce communication delays. These insights support cost-effective, reliable deployments for long-term underwater monitoring.
Abdelminam, D. S., Said, M., & Hossein, E. H. (2021). Turbulent flow of water-based optimization using new objective function for parameter extraction of six photovoltaic models. IEEE Access, 9, 35382–35398.
Abdelimnaam, D. S., Hossein, E. H., Said, M., Oliva, D., & Nabil, A. (2022). An efficient heap-based optimizer for parameters identification of modified photovoltaic models. Ain Shams Engineering Journal, 13(5), 101728.
Akyildiz, I. F., Pompili, D., & Melodia, T. (2005). Underwater acoustic sensor networks: Research challenges. Ad Hoc Networks, 3(3), 257–279.
Al-Betar, M. A., Awedallah, M. A., & Krishan, M. M. (2020). A non-convex economic load dispatch problem with valve loading effect using a hybrid grey wolf optimizer. Neural Computing and Applications, 32(16), 12127–12154.
Al-Betar, M. A., Awedallah, M. A., Zitar, R. A., & Assaleh, K. (2022). Economic load dispatch using memetic sine cosine algorithm. Journal of Ambient Intelligence and Humanized Computing, 2022, 1–29.
Alkoffash, M. S., Awedallah, M. A., Alweshah, M., Zitar, R. A., Assaleh, K., & Al Betar, M. A. (2021). A non-convex economic load dispatch using hybrid salp swarm algorithm. Arabian Journal for Science and Engineering, 46(9), 8721–8740.
Ayaz, M., Baig, I., Abdullah, A., & Faye, I. (2011). A survey on routing techniques in underwater wireless sensor networks. Journal of Network and Computer Applications, 34(6), 1908–1927.
Banothu, R. N., & ArunKumar, A. (2022). A study on FBR protocol for IoT in underwater environments. International Journal of Modern Trends in Science and Technology, 8, 47–54.
Basavaraju, P. H., Lokesh, G. H., Mohan, G., Jhanjhi, N. Z., & Flammini, F. (2022). Statistical channel model and systematic random linear network coding based QoS oriented and energy efficient UWSN routing protocol. Electronics, 11, 2590.
Bhatti, A., Rehman, T., Mahmood, K., Ali, M., Umar, Z., Rehman, A., & Ahmed, S. (2023). Time threshold-based energy efficient routing protocol for underwater sensor networks. Journal of Computing & Biomedical Informatics, 5(2), Article ID: 182-0502/2023
Chenthil, T. R., & Jesu Jayarin, P. (2022). An energy-efficient distributed node clustering routing protocol with mobility pattern support for underwater wireless sensor networks. Wireless Networks, 28, 3367–3390.
Dai, B., Wang, F., & Chang, Y. (2022). Multi-objective economic load dispatch method based on data mining technology for large coal-fired power plants. Control Engineering Practice, 121, 105018.
Deb, S., Abdelimnam, D. S., Said, M., & Hossein, E. H. (2021a). Recent methodology-based gradient-based optimizer for economic load dispatch problem. IEEE Access, 9, 44322–44338.
Deb, S., Hossein, E. H., Said, M., & Abdelimnam, D. S. (2021b). Performance of turbulent flow of water optimization on economic load dispatch problem. IEEE Access, 9, 77882–77893.
Dhieman, G. (2020). MOSHEPO: A hybrid multi-objective approach to solve economic load dispatch and micro grid problems. International Journal of Speech Technology, 50(1), 119–137.
Eldesouky, E., Bekhit, M., Fathalla, A., Salah, A., & Ali, A. (2021). A robust UWSN handover prediction system using ensemble learning. Sensors, 21, 5777.
Farsi, M., Deris, M. M., & Razak, S. A. (2019). Energy-efficient routing protocols in underwater wireless sensor networks: A survey. Sensors, 19(5), 1049.
Ghasemi Hamedan, M., Homayounfar, M., & Taleghani, M. (2025). Resilience appraisal for Iranol Oil Company: Application of adaptive neuro-fuzzy inference system. Journal of Optimization in Industrial Engineering, 18(1), 291–303.
Hossain, A., Khan, H. U., Nazir, S., Ullah, I., & Hussain, T. (2021). Taking FANET to next level: The contrast evaluation of moth-and-ant with bee ad-hoc routing protocols for flying ad-hoc networks. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 10, 321–337.
Ismail, A. S., Wang, X., Hawbani, A., Alsamhi, S., & Abdel Aziz, S. (2022). Routing protocols classification for underwater wireless sensor networks based on localization and mobility. Wireless Networks, 28, 797–826.
Kazemi, Z., Homayounfar, M., Fadaei, M., Soufi, M., & Salehzadeh, A. (2024). Multi-objective optimization of blood supply network using the meta-heuristic algorithms. Journal of Optimization in Industrial Engineering, 17(2), 87–104.
Kaboli, S. H. A., & Alqalaf, A. K. (2019). Solving non-convex economic load dispatch problem via artificial cooperative search algorithm. Expert Systems with Applications, 128, 14–27.
Kamboj, V. K., Kumari, C. L., Bath, S. K., Prashar, D., Rashid, M., Alshamrani, S. S., & AlGhamdi, A. S. (2022). A cost-effective solution for non-convex economic load dispatch problems in power systems using slime mould algorithm. Sustainability, 14(5), 2586.
Kaur, R. & Goyal, S. (2024). Flexible localization protocol for underwater wireless sensor networks using hybrid reward evaluation scheme. Peer-to-Peer Networking Applications, 17, 3353–3368.
Kapileswar, N., & Phani Kumar, P. (2022). Energy efficient routing in IOT based UWSN using bald eagle search algorithm. Transactions on Emerging Telecommunications Technologies, 33(3), e4399.
Kumar, R., & Bhardwaj, D. (2022). Decision tree-based event detection framework for UWSN routing to optimize energy consumption during transmission. In Advanced Computing and Intelligent Technologies 99–109. Springer.
Luo, C., Wang, B., Cao, Y., Xin, G., He, C., & Ma, L. (2021). A hybrid coverage control for enhancing UWSN localizability using IBSO-VFA. Ad Hoc Networks, 123, 102694.
Luo, H., Guo, Z., Hong, F., & Liu, Y. (2010). Vector-based forwarding protocol for underwater sensor networks. In Wireless Algorithms, Systems, and Applications. 121–132. Springer.
Mahalle, P. N., Shelar, P. A., Shinde, G. R., & Dey, N. (2021). Applications of UWSN. In The Underwater World for Digital Data Transmission. 55–64. Springer.
Muhammad, A., Li, F., Khan, Z.U., Khan, F., Khan, J. & Khan, S.U. (2025). Exploration of contemporary modernization in UWSNs in the context of localization including opportunities for future research in machine learning and deep learning. Scientific Reports, 15, 5672.
Nguyen, N. T., Le, T. T., Nguyen, H. H., & Voznak, M. (2021). Energy-efficient clustering multi-hop routing protocol in a UWSN. Sensors, 21, 627.
Pandith, A. M., & Goyal, N. (2022). Emerging data aggregation state-of-art techniques with comparative analysis in UWSN. In Proceedings of the 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE). 1829–1833. IEEE.
Pradeep, S., & Tapas Bapu, B. R. (2022). An efficient energy consumption and delay aware autonomous data gathering routing protocol scheme using a deep learning mobile edge model and beetle antennae search algorithm for underwater wireless sensor network. Concurrency and Computation: Practice and Experience, 34(3), e6621.
Qin, Q., Tian, Y., & Wang, X. (2021). Three-dimensional UWSN positioning algorithm based on modified RSSI values. Mobile Information Systems, 2021, 5554791.
Rehman, Z. U., Iqbal, A., Yang, B., & Hussain, T. (2021). Void hole avoidance based on sink mobility and adaptive two hop vector-based forwarding in underwater wireless sensor networks. Wireless Personal Communications, 120, 1417–1447.
Said, M., El-Reifaie, A. M., Tolba, M. A., Hossein, E. H., & Deb, S. (2021). An efficient chameleon swarm algorithm for economic load dispatch problem. Mathematics, 9(21), 2770.
Said, M., Hossein, E. H., Deb, S., Alhassan, A. A., & Ghoniem, R. M. (2022). A novel gradient-based optimizer for solving unit commitment problem. IEEE Access, 10, 18081–18092.
Sharifi, S. A., & Babamir, S. M. (2019). Evaluation of clustering algorithms in ad hoc mobile networks. Wireless Personal Communications, 109(4), 2147–2186.
Sharifi, S. A., & Babamir, S. M. (2020). The clustering algorithm for efficient energy management in mobile ad-hoc networks. Computer Networks, 166, 106983.
Shovon, I. I., & Shin, S. (2022). Energy efficient routing protocols for UWSN: A survey. In Proceedings of the Korea Telecommunications Society Conference. 275-278.
Singh, D., & Dhillon, J. S. (2019). Ameliorated grey wolf optimization for economic load dispatch problem. Energy, 169, 398–419.
Srivastava, A., & Das, D. K. (2022). An adaptive chaotic class topper optimization technique to solve economic load dispatch and emission economic dispatch problem in power system. Soft Computing, 2022, 1–22.
Subramani, N., Mohan, P., Alotaibi, Y., Alghamdi, S., & Khalaf, O. I. (2022). An efficient metaheuristic-based clustering with routing protocol for underwater wireless sensor networks. Sensors, 22, 415.
Tavakol, P., Nahavandi, B., & Homayounfar, M. (2023). A dynamics approach for modeling inventory fluctuations of the pharmaceutical supply chain in COVID-19 pandemic. Journal of Optimization in Industrial Engineering, 16(1), 105–118.
Vignesh, S. R., Sukumaran, R., Ponraj, T. S., Manikandan, T. T., & Saravanan, M. (2022). Implementation of routing protocol in underwater sensor networks for energy harvesting. In Proceedings of the 2022 4th International Conference on Smart Systems and Inventive Technology (ICSSIT). 354–361. IEEE.
Wahid, A., & Kim, D. (2012). An energy-efficient localization-free routing protocol for underwater wireless sensor networks. International Journal of Distributed Sensor Networks, Article ID 307246.
Xu, N., Wei, W., & Yang, L. (2019). An energy-balanced depth-based routing protocol for underwater wireless sensor networks. Sensors, 19(3), 703.
Zaid, A., & Ibrahim, H. (2020). Cluster-based energy-efficient routing protocol for UWSNs. Wireless Networks, 26(3), 2185–2197.
Zein, H., Rahajo, J., & Mardiyanto, I. R. (2022). A method for completing economic load dispatch using the technique of narrowing down area. IEEE Access, 10, 30822–30831.
