Deniz Taşımacılığı İzlemek için Sualtı Kablosuz Sensör Ağlarında Otonom Sualtı Aracı ile Dayanıklı ve Enerji Farkında Yol Planlama

Yıl 2024, Cilt: 14 Sayı: 2, 71 - 85, 30.07.2024

Ömer Melih Gül , Tayfun Acarer

Öz

Daha ucuz olması nedeniyle uluslararası ve kıtalararası ticarette deniz taşımacılığı tercih edilmektedir. Ancak gemi kazaları olası tehlikelerdir. Sualtı kablosuz sensör ağları (SKSA), deniz trafiği risklerini azaltmak için deniz ortamını izleyebilir. Enerji, sınırlı güce sahip olumsuz bölgelerdeki SKSA’lar için hayati öneme sahiptir. SKSA’nın uzun süre dayanabilmesi için, sürekli çevresel izleme ve gözetleme verilerinin toplanması ve iletişimini sağlamak amacıyla enerji kısıtlamalarının ele alınması gerekir. Su altı sensör düğümleri için yol planlama ve enerji tasarruflu otonom su altı aracı (OSA) şarjı, SKSA enerji ve pil değiştirme sorunlarını çözebilir. Bu makale, OSA ile enerjiye duyarlı yol planlama problemini çözmek için en yakın komşu algoritmasını kullanmaktadır. OSA yol planlama simülasyonları, en yakın komşu algoritmasının genetik algoritmaya ve bozkurt eniyileştirme algoritmasına göre daha hızlı yakınsadığını ve daha iyi bir çözüm ürettiğini göstermektedir. Sensör verilerini daha az enerjiyle hızlı bir şekilde toplamak için sağlam ve enerji açısından verimli yol planlama algoritmaları sunarak izleme sisteminin gemi felaket tehlikelerine daha hızlı yanıt vermesini sağlıyoruz. Sensörlerin daha yakından iletişim kurması enerji kullanımını en aza indirir ve SKSA ağınının ömrünü artırır.

Anahtar Kelimeler

otonom su altı aracı, sualtı kablosuz sensör ağları, enerjiye duyarlı yol planlaması, su izleme, gemi yönetim sistemleri, denizcilik otoritesi için güvenli seyir planlaması, denizcilik endüstrisi, deniz ticareti

Etik Beyan

(benzerlik sınırı ise 20% iken) Bu makalenin benzerlik oranı 2%'dir. Bu çalışma orijinal olup başka bir dergiye veya konferansa yüklenmemiştir.

Robust and Energy-Aware Path Planning by Autonomous Underwater Vehicle in Underwater Wireless Sensor Networks for Monitoring Maritime Transportation

Öz

Since it’s cheaper, sea transportation has been preferred for international and intercontinental trade. However, ship mishaps are possible dangers. Underwater wireless sensor networks (UWSN) can monitor the maritime environment to reduce maritime traffic risks. Energy is crucial for UWSNs in adverse areas with limited power. So UWSN can last, energy constraints must be handled to enable continuous environmental monitoring and surveillance data gathering and communication. Path planning and energy-saving autonomous underwater vehicle (AUV) charging for underwater sensor nodes can solve UWSN energy and battery replacement issues. This paper uses nearest neighbour algorithm to solve the energy-aware path planning problem with AUV. AUV path planning simulations show that the nearest neighbour algorithm converges faster and produces a better solution than the genetic algorithm and grey wolf optimizer algorithm. We offer robust and energy-efficient path planning algorithms to swiftly collect sensor data with less energy, allowing the monitoring system to respond faster to ship disaster hazards. Communicating sensors closer minimises their energy usage and improves UWSN network lifetime.

Anahtar Kelimeler

autonomous underwater vehicle, underwater wireless sensor networks, energy-aware path planning, water monitoring, ship management systems, safe sailing planning for maritime authority, maritime industry, maritime commerce

Kaynakça

• [1] Acarer T., The Turkish Model For Improving Imo Survey Results And Reducing Ship Accidents, Dokuz Eylül Üniversity Maritime Faculty Journal, Vol:11, Issue:1, Year:2019.
• [2] Aygün, C. (2012). Türkiye ile Avrupa Birliği’nde Uygulanan Deniz Ulaştırma Politikaları ve Ekonomiye Etkileri, T:C: İstanbul Üniversitesi Deniz Bilimleri ve İşletmeciliği Enstitüsü, Yüksek Lisans Tezi, İstanbul.
• [3] International Chamber of Shipping, (2020). Description of the Subject International Chamber of Shipping (ICS). http://en.reingex. com/Chamber-Shipping.shtml#:~:text=The% 20purpose%20of%20the%20International, transporting%20all%20types%20of%20cargo
• [4] UNCTAD/ RMT/ 2018: REVIEW OF MARITIME TRANSPORT 2018, United Nations publication issued by the United Nations Conference on Trade and Development, ISBN 978-92-1- 112928-1, UNCTAD: Geneva, Switzerland, 2018, https://unctad.org/en/PublicationsLibrary/ rmt2018_en.pdf.
• [5] Butt N., Johnson D., Pike K., Pryce-Roberts N., Vigar., N., (2012): 15 Years of Shipping Accidents: A review for WWF, Southampton Solent University
• [6] Rodrigue J.P., (2017): The Geography of Transport Systems, The spatial organization of transportation and mobility, Chapter 5 – Transportation Modes, Maritime Transportation, Main Maritime Shipping Routes, 2017. https://transportgeography.org/ ?page_id=2067
• [7] Kodak, G., Acarer, T. (2021). İstanbul Boğazı’nda deniz trafik düzenlemelerinin kaza oranına etkisinin değerlendirilmesi. Aquatic Research, 4(2), 181-207. https://doi.org/10.3153/AR21015
• [8] BBC News, "Egypt’s Suez Canal blocked by huge container ship", 24 March 2021, available at https:// www.bbc.com/news/world-middle-east-56505413
• [9] J. M. Lee, E. Y. Wong. Suez Canal blockage: an analysis of legal impact, risks and liabilities to the global supply chain. MATEC Web Conf. 339 01019 (2021).
• [10] O. Ozkanlisoy, E. Akkartal, The Effect of Suez Canal Blockage on Supply Chains. Dokuz Eylül Üniversitesi Denizcilik Fakültesi Dergisi. Haziran 2022;14(1):51- 79.
• [11] Tianhang Gao & Jing Lu (2019). The impacts of strait and canal blockages on the transportation costs of the Chinese fleet in the shipping network, Maritime Policy & Management, 46:6, 669-686.
• [12] S. Fan, Z. Yang, J. Wang, J. Marsland, "Shipping accident analysis in restrictedwaters: Lesson from the Suez Canal blockage in 2021", Ocean Engineering, vol. 266, Part 5, 2022, 113119.
• [13] T. Acarer. Endüstri’deki Gelişmelerin Denizcilik İşletmelerine Ait Gemilerin Yönetiminde Temin Ettiği Yeni Olanaklar ve İnsansız Gemiler, Mersin Üniversitesi Denizcilik ve Lojistik Araştırmaları Dergisi, Cilt:5 Sayı:2 Yıl:2023, Sayfa:122-153.
• [14] Acarer Tayfun, VHF Kısa Mesafe Deniz Haberleşmesinin Data İletişimine Dönüşmesinin Deniz İşletmelerinin Gemi Yönetimleri İçin Temin Edeceği Olanaklar, Denizcilik Araştırmaları Dergisi: Amfora, Cilt 2 – Sayı 3 – Haziran 2023 / Volume 2 - Issue 3 - June 2023.
• [15] Felemban, E.; Shaikh, F.K. Underwater sensor network applications: A comprehensive survey. Int. J. Distrib. Sens. Netw. 2015, 11, 896832–896845.
• [16] Qiu, T.; Zhao, Z. Underwater Internet of Things in Smart Ocean: System Architecture and Open Issues. IEEE Trans. Ind. Inform. 2020, 16, 4297–4307.
• [17] Akyildiz, I.F.; Pompili, D. Underwater acoustic sensor networks: Research challenges.Ad HocNetw. 2005, 3, 257–279.
• [18] Li, Q.; Du, X. Energy-efficient data compression for underwater wireless sensor networks. IEEE Access 2020, 8, 73395–73406.
• [19] Cheng, F.; Wang, J. Energy-efficient routing protocols in underwater wireless sensor networks: A survey. IEEE Commun. Surv. Tutor. 2014, 16, 277–294.
• [20] Khan, A.U.; Somasundaraswaran, K. Wireless charging technologies for underwater sensor networks: A comprehensive review. IEEE Commun. Surv. Tutor. 2018, 20, 674–709.
• [21] Pendergast, D.R.; DeMauro, E.P. A rechargeable lithium-ion battery module for underwater use. J. Power Sources 2011, 196, 793–800.
• [22] Blidberg, D.R. The development of autonomous underwater vehicles (AUV); a brief summary. In Proceedings of the IEEE ICRA, Seoul, Republic of Korea, 21–26 May 2001; Volume 4, pp. 122–129.
• [23] Ghafoor, H.; Noh, Y. An overview of next-generation underwater target detection and tracking: An integrated underwater architecture. IEEE Access 2019, 7, 98841–98853.
• [24] Xie, L.; Shi, Y. Rechargeable sensor networks with magnetic resonant coupling. Recharg. Sens. Netw. Technol. Theory Appl. Introd. Energy Harvest. Sens. Netw. 2014, 9, 31–68.
• [25] Lee, J.; Yun, N. 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.
• [26] Zenia, N.Z.; Aseeri, M. Energy-efficiency and reliability in MAC and routing protocols for underwater wireless sensor network: A survey. J. Netw. Comput. Appl. 2016, 71, 72–85.
• [27] Khan, M.T.R.; Ahmed, S.H. An energy-efficient data collection protocol with AUV path planning in the internet of underwater things. J.Netw.Comput. Appl. 2019, 135, 20–31.
• [28] Su, Y.; Xu, Y. HCAR: A Hybrid-Coding-Aware Routing Protocol for Underwater Acoustic Sensor Networks. IEEE Internet Things J. 2023, 10, 10790–10801.
• [29] Kumar, V.; Sandeep, D. Multi-hop communication based optimal clustering in hexagon and voronoi cell structuredWSNs. AEU-Int. J. Electron. Commun. 2018, 93, 305–316.
• [30] Xie, R.; Jia, X. Transmission-efficient clustering method for wireless sensor networks using compressive sensing. IEEE Trans. Parallel Distrib. Syst. 2013, 25, 806–815.
• [31] Yadav, S.; Kumar, V. Hybrid compressive sensing enabled energy efficient transmission of multi-hop clustered UWSNs. AEU-Int. J. Electron. Commun. 2019, 110, 152836–152851.
• [32] Sun, Y.; Zheng, M.; Han, X.; Li, S.; Yin, J. Adaptive clustering routing protocol for underwater sensor networks. Ad Hoc Netw. 2022, 136, 102953–102965.
• [33] Fan, R.; Jin, Z. A time-varying acoustic channelaware topology control mechanism for cooperative underwater sonar detection network. Ad Hoc Netw. 2023, 149, 103228.
• [34] Liu, C.F.; Zhao, Z. A distributed node deployment algorithm for underwater wireless sensor networks based on virtual forces. J. Syst. Archit. 2019, 97, 9–19.
• [35] Wei, L.; Han, J. Topology Control Algorithm of Underwater Sensor Network Based on Potential- Game and Optimal Rigid Sub-Graph. IEEE Access 2020, 8, 177481–177494.
• [36] Zhu, R.; Boukerche, A. A trust managementbased secure routing protocol with AUV-aided path repairing for Underwater Acoustic Sensor Networks. Ad Hoc Netw. 2023, 149, 103212–103225.
• [37] Yan, Z.; Li, Y. Data collection optimization of ocean observation network based on AUV path planning and communication. Ocean Eng. 2023, 282, 114912–114927.
• [38] Shen, G.; Zhu, X. Research on phase combination and signal timing based on improved K-medoids algorithm for intersection signal control. Wirel. Commun. Mob. Comput. 2020, 2020, 3240675.
• [39] Gjanci, P.; Petrioli, C. Path finding for maximum value of information in multi-modal underwater wireless sensor networks. IEEE Trans. Mob. Comput. 2017, 17, 404–418.
• [40] Yan, J.; Yang, X. Energy-efficient data collection over AUV-assisted underwater acoustic sensor network. IEEE Syst. J. 2018, 12, 3519–3530.
• [41] Kan, T.; Mai, R. Design and analysis of a Three-Phase wireless charging system for lightweight autonomous underwater vehicles. IEEE Trans. Power Electron. 2018, 33, 6622–6632.
• [42] Ramos, A.G.; García-Garrido, V.J. Lagrangian coherent structure assisted path planning for transoceanic autonomous underwater vehicle missions. Sci. Rep. 2018, 8, 4575.
• [43] Cheng, C.; Sha, Q. Path planning and obstacle avoidance for AUV: A review. Ocean Eng. 2021, 235, 109355–109368.
• [44] Kumar, S.V.; Jayaparvathy, R. Efficient path planning of AUVs for container ship oil spill detection in coastal areas. Ocean Eng. 2020, 217, 107932–107945.
• [45] Golen, E.; Mishra, F. An underwater sensor allocation scheme for a range dependent environment. Comput. Netw. 2010, 54, 404–415.
• [46] Yi, Y.; Yang, G.S. 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. 8
• [47] Cui Y, Zhu P, Lei G, Chen P, Yang G. Energy-Efficient Multiple Autonomous Underwater Vehicle Path Planning Scheme in Underwater Sensor Networks. Electronics. 2023; 12(15):3321.
• [48] Acarer, T. (2024). Energy-Aware Path Planning by Autonomous Underwater Vehicle in Underwater Wireless Sensor Networks for Safer Maritime Transportation. International Journal of Interactive Multimedia and Artificial Intelligence.
• [49] P. C. Pop, O. Cosma, C. Sabo, C. P. Sitar, "A comprehensive survey on the generalized traveling salesman problem", European Journal of Operational Research, Volume 314, Issue 3, 2024, pp. 819-835.
• [50] Davendra, D.: Travelling Salesman Problem, Theory and Applications. InTech (2010)
• [51] Johnson, D.S., McGeoch, L.A.: The Traveling Salesman Problem: A Case Study, Local Search in Combinatorial Optimization, pp. 215–310. JohnWiley & Sons (1997)
• [52] Gutin, G., Punnen, A. (eds.): The Traveling Salesman Problem and Its Variations. Combinatorial Optimization, vol. 12. Kluwer, Dordrecht (2002)
• [53] Hoffman, K.L., Padberg, M. (2001). Traveling salesman problem . In: Gass, S.I., Harris, C.M. (eds) Encyclopedia of Operations Research and Management Science. Springer, New York, NY. https://doi.org/10.1007/1-4020-0611-X_1068
• [54] G. Gutin, A. Yeo and A. Zverovitch, Exponential Neighborhoods and Domination Analysis for the TSP, in The Traveling Salesman Problem and Its Variations, G. Gutin and A.P. Punnen (eds.), Kluwer (2002) and Springer (2007)
• [55] Mirjalili, S.; Mirjalili, S.M.; Lewis, A. grey wolf optimizer. Adv. Eng. Softw. 2014, 69, 46–61.
• [56] D.E.Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison- Wesley Longman Publishing Co., Inc.,1989.
• [57] Bonabeau, E., Dorigo, M., & Theraulaz, G. (1999). Swarm intelligence: from natural to artificial systems (No. 1). Oxford university press.