Sualtı Kablosuz Sensör Ağlarında Otonom Sualtı Aracı Tarafından Yenilikçi bir Enerji Farkında Yol Planlaması

Abstract

Kablosuz sensör ağları, anormallikleri tespit etmek ve deniz trafiği riskini azaltmak için çevreyi izleyebilir. Kablosuz sensör ağlarının kullanıldığı düşük güç koşulları için enerji gereklidir. Enerji kısıtlamalarının ömrünün sağlanması ve sürekli çevresel gözlem, veri toplama ve iletişim sağlanması yönetim gerektirir. Pil değişimi ve enerji tüketimi sorunları, sensör düğümleri için yol planlaması ve enerji açısından verimli otonom su altı araç şarjı ile çözülebilir. Bu çalışmada, otonom bir su altı aracının enerji farkında yol planlama problemini çözmek için en yakın komşu tekniği kullanılmıştır. Yol planlama simülasyonları, en yakın komşu stratejisinin daha hızlı birleştiğini ve genetik algoritmadan daha iyi sonuç ürettiğini göstermektedir. Daha az enerji tüketirken sensör verilerini verimli bir şekilde toplayan ve izleme sisteminin anormalliklere daha hızlı yanıt vermesini sağlayan sağlam ve enerji açısından verimli yol planlama algoritmaları geliştiriyoruz. Artan sensör bağlantısı enerji kullanımını düşürür ve ağ ömrünü artırır. Bu çalışma ayrıca çeşitli nedenlerle belirli düğüm çiftleri arasında doğrudan seyahat yolları kullanmaktan kaçınılmasının önerildiği durumu da ele almaktadır. Bu öneri bu çalışmada dikkate alınmıştır. Bu daha zorlu senaryoyu ele almak için En Yakın Komşu yönteminden değiştirilmiş En Yakın Komşu tabanlı Yaklaşıma dayalı bir strateji sunuyoruz. Bu tür düğümler arasındaki doğrudan yollar bu tekniğin bağlamında kısıtlanmıştır. En Yakın Komşu tabanlı yaklaşımın değiştirilmiş versiyonu, o belirli durumda bile iyi performans gösterir.

Keywords

• Otonom su altı aracı
• yapay zeka
• çevresel izleme
• enerji bilinçli yol planlama
• kablosuz sensör ağları
• su altı iletişimi

A Novel Energy-Aware Path Planning by Autonomous Underwater Vehicle in Underwater Wireless Sensor Networks

Abstract

Wireless sensor networks can monitor the environment to detect anomalies and reduce the risk of maritime traffic. Energy is necessary for low-power conditions where wireless sensor networks are used. Ensuring the lifespan of energy constraints and providing continuous environmental observation, data collecting, and communication requires management. Battery replacement and energy consumption issues can be resolved with path planning and energy-efficient autonomous underwater vehicle charging for sensor nodes. The nearest neighbour technique is used in this study to solve the energy-aware path planning problem of an autonomous underwater vehicle. Path planning simulations show that the nearest neighbour strategy converges faster and produces a better result than the genetic algorithm. We develop robust and energy-efficient path-planning algorithms that efficiently acquire sensor data while consuming less energy, allowing the monitoring system to respond to anomalies more rapidly. Increased sensor connectivity lowers energy usage and increases network longevity. This study also considers the situation when it is recommended to avoid taking direct travel paths between particular node pairs for a variety of reasons. This recommendation is considered in this study. We present a strategy based on a modified Nearest Neighbour-based Approach from the Nearest Neighbour method to address this more challenging scenario. The direct pathways between such nodes are constrained within the context of this technique. The modified version of Nearest Neighbor-based approach performs well even in that particular situation.

Keywords

• Autonomous underwater vehicle
• artificial intelligence
• environmental monitoring
• energy-aware path planning
• wireless sensor networks
• underwater communication

References

1. Akyildiz, I.F., Pompili, D. (2005). Underwater acoustic sensor networks: Research challenges. Ad Hoc Networks, 3, 257–279.
2. Blidberg, D.R. (2001). The development of autonomous underwater vehicles (AUV); a brief summary. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Seoul, Republic of Korea, 21–26 May 2001, pp. 122–129.
3. Bonabeau, E., Dorigo, M., Theraulaz, G. (1999). Swarm intelligence: from natural to artificial systems (No. 1). Oxford University Press.
4. Eris, C., Gul, O.M., Boluk, P.S. (2023). An Energy-Harvesting Aware Cluster Head Selection Policy in Underwater Acoustic Sensor Networks. In Proceedings of the 2023 International Balkan Conference on Communications and Networking (BalkanCom), Istanbul, Turkey, 5–8 June 2023, pp. 1–5.
5. Eris, C., Gul, O.M., Boluk, P.S. (2024a). A Novel Reinforcement Learning Based Routing Algorithm for Energy Management in Networks. Journal of Industrial and Management Optimization, 20 (12): 3678- 3696.
6. Eriş, Ç., Gül, Ö.M., Bölük, P.S. (2024b). A Novel Medium Access Policy Based on Reinforcement Learning in Energy-Harvesting Underwater Sensor Networks. Sensors. 24 (17): 5791.
7. Cheng, F., Wang, J. (2014). Energy-efficient routing protocols in underwater wireless sensor networks: A survey. IEEE Communications Surveys & Tutorials, 16: 277–294.
8. Cheng, C., Sha, Q. (2021). Path planning and obstacle avoidance for AUV: A review. Ocean Engineering, 235: 109355–109368.

9. Davendra, D. (2010). Travelling Salesman Problem, Theory and Applications. InTech.
10. Fan, R., Jin, Z. (2023). A time-varying acoustic channel-aware topology control mechanism for cooperative underwater sonar detection network. Ad Hoc Networks, 149: 103228.
11. Felemban, E., Shaikh, F.K. (2015). Underwater sensor network applications: A comprehensive survey. International Journal of Distributed Sensor Networks, 11: 896832–896845.
12. Ghafoor, H., Noh, Y. (2019). An overview of next-generation underwater target detection and tracking: An integrated underwater architecture. IEEE Access, 7: 98841–98853.
13. Goldberg, D.E. (1989). Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley Longman Publishing Co., Inc.
14. Gül, Ö.M., Acarer, T. (2024). Deniz Taşımacılığı İzlemek için Sualtı Kablosuz Sensör Ağlarında Otonom Sualtı Aracı ile Dayanıklı ve Enerji Farkında Yol Planlama. EMO Bilimsel Dergi, 14(2): 71–85.
15. Gul, O.M. (2024). Energy-Aware 3D Path Planning by Autonomous Ground Vehicle in Wireless Sensor Networks. World Electric Vehicle Journal, 15(9): 383.
16. Gul, O.M., Erkmen, A.M. (2020). Energy-efficient cluster-based data collection by a UAV with a limited-capacity battery in robotic wireless sensor networks. Sensors, 20: 5865.
17. Gul, O.M., Erkmen, A.M., Kantarci, B. (2022). UAV-Driven Sustainable and Quality-Aware data collection in robotic wireless sensor networks. IEEE Internet Things Journal, 9(24): 25150–25164.
18. Gul, O.M., Erkmen, A.M. (2023). Energy-Aware UAV-Driven Data Collection with Priority in Robotic Wireless Sensor Network. IEEE Sensors Journal, 23 (15): 17667–17675.
19. Gul, O.M., Erkmen, A.M., Kantarci, B. (2024). NTN-Aided Quality and Energy-Aware Data Collection in Time-Critical Robotic Wireless Sensor Networks. IEEE Internet Things Magazine, 7: 114–120.
20. Gutin, G., Punnen, A. (2002). The Traveling Salesman Problem and Its Variations. Combinatorial Optimization, 12, Kluwer, Dordrecht.
21. Gutin, G. Yeo, A. and Zverovitch, A. (2007). Exponential Neighborhoods and Domination Analysis for the TSP, in The Traveling Salesman Problem and Its Variations, G. Gutin and A.P. Punnen (eds.), Springer.
22. Johnson, D.S., McGeoch, L.A. (1997). The Traveling Salesman Problem: A Case Study, Local Search in Combinatorial Optimization, pp. 215–310. John Wiley & Sons.
23. Gjanci, P., Petrioli, C. (2017). Path finding for maximum value of information in multi-modal underwater wireless sensor networks. IEEE Transactions on Mobile Computing, 17: 404–418.
24. Gjanci, P., Petrioli, C. (2017). Path finding for maximum value of information in multi-modal underwater wireless sensor networks. IEEE Transactions on Mobile Computing, 17: 404–418.
25. Golen, E., Mishra, F. (2010). An underwater sensor allocation scheme for a range dependent environment. Computer Networks, 54: 404–415.
26. Golen, E., Mishra, F. (2010). An underwater sensor allocation scheme for a range dependent environment. Computer Networks, 54: 404–415.
27. Kan, T., Mai, R. (2018). Design and analysis of a Three-Phase wireless charging system for lightweight autonomous underwater vehicles. IEEE Transactions on Power Electronics, 33: 6622–6632.
28. Khan, A.U., Somasundaraswaran, K. (2018). Wireless charging technologies for underwater sensor networks: A comprehensive review. IEEE Communications Surveys & Tutorials, 20: 674–709.
29. Khan, M.T.R., Ahmed, S.H. (2019). An energy-efficient data collection protocol with AUV path planning in the internet of underwater things. Journal of Network and Computer Applications, 135: 20–31.
30. Kumar, V., Sandeep, D. (2018). Multi-hop communication based optimal clustering in hexagon and voronoi cell structured WSNs. AEU - International Journal of Electronics and Communications, 93: 305–316.
31. Kumar, S.V., Jayaparvathy, R. (2020). Efficient path planning of AUVs for container ship oil spill detection in coastal areas. Ocean Engineering, 217: 107932–107945.
32. Lee, J., Yun, N. (2011). A focus on comparative analysis: Key findings of MAC protocols for underwater acoustic communication according to network topology. In Proceedings of the Multimedia, Computer Graphics and Broadcasting: International Conference, Jeju Island, Korea, 8–10 December 2011, pp. 29-37.
33. Li, Q., Du, X. (2020). Energy-efficient data compression for underwater wireless sensor networks. IEEE Access, 8: 73395–73406.
34. Liu, C.F., Zhao, Z. (2019). A distributed node deployment algorithm for underwater wireless sensor networks based on virtual forces. Journal of Systems Architecture, 97: 9–19.
35. Mirjalili, S., Lewis, M. (2014). A. grey wolf optimizer. Advances in Engineering Software, 69: 46–61.
36. Pendergast, D.R., DeMauro, E.P. (2011). A rechargeable lithium-ion battery module for underwater use. Journal of Power Sources, 196: 793–800.
37. Pop, P.C., Cosma, O., Sabo, C., Sitar, C.P. (2024). A comprehensive survey on the generalized traveling salesman problem. European Journal of Operational Research, 314(3): 819-835. doi: 10.1016/j.ejor.2023.07.022.
38. Qiu, T., Zhao, Z. (2020). Underwater Internet of Things in Smart Ocean: System Architecture and Open Issues. IEEE Transactions on Industrial. Informatics, 16: 4297–4307.
39. Ramos, A.G., García-Garrido, V.J. (2018). Lagrangian coherent structure assisted path planning for transoceanic autonomous underwater vehicle missions. Scientific Reports, 8: 4575.
40. Shen, G., Zhu, X. (2020). Research on phase combination and signal timing based on improved K-medoids algorithm for intersection signal control. Wireless Communications and Mobile Computing, 2020: 3240675.
41. Su, Y., Xu, Y. (2023). HCAR: A Hybrid-Coding-Aware Routing Protocol for Underwater Acoustic Sensor Networks. IEEE Internet Things Journal, 10: 10790–10801.
42. Sun, Y., Zheng, M., Han, X., Li, S., Yin, J. (2022). Adaptive clustering routing protocol for underwater sensor networks. Ad Hoc Networks, 136: 102953–102965.
43. Wei, L., Han, J. (2020). Topology Control Algorithm of Underwater Sensor Network Based on Potential-Game and Optimal Rigid Sub-Graph. IEEE Access, 8: 177481–177494.
44. Xie, R., Jia, X. (2013). Transmission-efficient clustering method for wireless sensor networks using compressive sensing. IEEE Transactions on Parallel and Distributed Systems, 25: 806–815.
45. Xie, L., Shi, Y. (2014). Rechargeable sensor networks with magnetic resonant coupling. Rechargeable Sensor Networks: Technology, Theory, and Application, 9, 31–68.
46. Yadav, S., Kumar, V. (2019). Hybrid compressive sensing enabled energy efficient transmission of multi-hop clustered UWSNs. AEU - International Journal of Electronics and Communications, 110: 152836–152851.
47. Yan, J., Yang, X. (2018). Energy-efficient data collection over AUV-assisted underwater acoustic sensor network. IEEE Systems Journal, 12: 3519–3530.
48. Yan, Z., Li, Y. (2023). Data collection optimization of ocean observation network based on AGV path planning and communication. Ocean Engineering, 282: 114912–114927.
49. Yi, Y., Yang, G.S. (2022). Energy balancing and path plan strategy for rechargeable underwater sensor network. In Proceedings of the 2022-4th International Conference on Advances in Computer Technology, Suzhou, China, 22–24 April 2022.
50. Zenia, N.Z., Aseeri, M. (2016). Energy-efficiency and reliability in MAC and routing protocols for underwater wireless sensor network: A survey. Journal of Network and Computer Applications, 71: 72–85.
51. Zhu, R., Boukerche, A. (2023). A trust management-based secure routing protocol with AUV-aided path repairing for Underwater Acoustic Sensor Networks. Ad Hoc Networks, 149: 103212–103225.