Sualtı Kablosuz Algılayıcı Ağlarda Aloha tabanlı Maliyet Etkin Ortam Erişim Protokolü

Sualtı algılayıcı ağlar; okyanusta veri toplama, kirlilik izleme, okyanus örneklemesi gibi çok çeşitliuygulamalarla hızla gelişen bir araştırma alanıdır. En çok araştırılan alanlardan biri, birçok uygulamanıntemelini oluşturan sualtı algılayıcı ağların kapsama alanıdır. Kapsama alanı genellikle bir ağın algılayıcıtarafından ne kadar etkin izlendiği ile ilgilidir. Su kirliliği başta olmak üzere, okyanus veya denizbölgesinde ortaya çıkan başlıca problemler vardır. Sualtı kirliliği genel olarak asitleşmeye, plastikkalıntılara ve toksinlere sebep olmaktadır. Günümüzde bu kirliliğin belirlenmesi, insan gözetimli izlemesüreci ile gerçekleştirilmektedir. Bu sebeple, kirliliğin oluşumunu tanımlamak için otomatik ve akıllıizleme sistemine ihtiyaç duyulmaktadır. Önerdiğimiz benzetim modeli, su altındaki kirliliğin oluşumunutanımlayan ve alarm veren akıllı algılayıcı tabanlı izleme sistemini tanımlamaktadır. Benzetim modelinitasarladığımız sistemin, maliyet açısından verimli olması için ortam erişim protokolü olarak Alohaseçilmiştir. Sistemin verimliliği benzetim modeli ile test edilerek mevcut insan gözetimi içeren izlemesürecinden daha kararlı, düşük maliyetli ve yönetilebilir olduğu gösterilmiştir. Algılayıcı ağ yükü 0 ile 6arasında değiştiğinde, en yüksek başarım oranı 0,36 olarak ağ yükü 1 olduğunda elde edilmektedir.Gecikme değeri 0,14ms ile 0,16ms arasına yakın değerlerde değişirken, en düşük gecikme 0,15ms olarakbenzetim süresinin ortalarında elde edilmektedir.

Aloha based Cost Effective Medium Access Protocol in Underwater Wireless Sensor Networks

Underwater sensor networks; it is a rapidly developing area of research with a wide range of applications such as data collection in ocean, pollution monitoring and ocean sampling. One of the most researched areas is the coverage of underwater sensor networks, which are the basis of many applications. The coverage is usually related to how effectively a network is monitored by the sensor. There are major problems in the ocean or marine region, especially in water pollution. Underwater pollution generally causes acidification, plastic residues and toxins. Today, the determination of this pollution is carried out through a human surveillance monitoring process. Therefore, there is a need for an automatic and intelligent monitoring system to identify the formation of pollution. The proposed simulation model defines the intelligent sensor-based monitoring system that identifies and alarms the formation of underwater pollution. Aloha was chosen as the medium access protocol for the costeffective system in which we designed the simulation model. The efficiency of the system has been shown to be more stable, cost-effective and manageable than the monitoring process involving the existing human surveillance by testing with the simulation model. When the sensor network load increases from 0 to 6, the highest performance ratio is obtained as 0.36 when the network load is 1. The delay value changes between 0.14 ms and 0.16 ms, while the lowest delay is acquired as 0.15 ms in the middle of simulation duration.

PDF

___

Akhter, A., Uddin, M. A., Abir, M. A. I. and Islam M. M., 2016. Noise aware level based routing protocol for underwater sensor networks. International Workshop on Computational Intelligence, Dhaka, 169-174.
Basagni, S., Bölöni, L., Gjanci, P., Petrioli, C., Phillips, C. A. and Turgut, D., 2014. Maximizing the value of sensed information in underwater wireless sensor networks via an autonomous underwater vehicle. IEEE INFOCOM - IEEE Conference on Computer Communications, Toronto, ON, 988-996.
Byeon, M., Kim, B., Jeon, J. and Park, S., 2008. Design and implementation of high-speed communication modem using ultrasonic sensors for underwater sensor networks. OCEANS Conference, Quebec City, QC, 1-4.
Chao, Y., 2016. Autonomous underwater vehicles and sensors powered by ocean thermal energy. OCEANS Conference, Shanghai, 1-4.
Erdem, H. E. and Gungor, V. C., 2017. Lifetime analysis of energy harvesting underwater wireless sensor nodes. 25th Signal Processing and Communications Applications Conference, Antalya, 1-4.
Erol, M. and Oktug, S., 2008. A localization and routing framework for mobile underwater sensor networks. IEEE INFOCOM Workshops, Phoenix, AZ, 1-3.
Fauziya, F., Agrawal, M. and Lall, B., 2016. Channel capacity of a Vector Sensor based underwater communications system. OCEANS MTS / IEEE Conference, Monterey, CA, 1-5.
Goyal, N., Dave, M. and Verma, A. K., 2016. Congestion control and load balancing for cluster based underwater wireless sensor networks. Fourth International Conference on Parallel, Distributed and Grid Computing, Waknaghat, 462-467.
Guangzhong, L. and Zhibin, L., 2010. Depth-Based Multihop Routing protocol for Underwater Sensor Network. The 2nd International Conference on Industrial Mechatronics and Automation, Wuhan, 268-270.
Katti, A. and Lobiyal, D. K., 2016. Sensor node deployment and coverage prediction for underwater sensor networks. 3rd International Conference on Computing for Sustainable Global Development, New Delhi, 3018-3022.
Lazar, I., Ghilezan, A. and Hnatiuc, M., 2016. Development of underwater sensor unit for studying marine life. IEEE 22nd International Symposium for Design and Technology in Electronic Packaging, Oradea, 82-85.
Lee, T. J., Han, M. S., Han, J. W., Shin, S. J., Kim, K. M., Chun, S. Y. and Son, K., 2011. A Study on the Low Power Communication for Underwater Sensor Network. IFIP 9th International Conference on Embedded and Ubiquitous Computing, Melbourne, VIC, 420-423.
Li, S., Wang, W. and Zhang, J., 2012. Efficient deployment surface area for underwater wireless sensor networks. IEEE 2nd International Conference on Cloud Computing and Intelligence Systems, Hangzhou, 1083-1086.
Park, S., 2009. An efficient transmission scheme for underwater sensor networks. OCEANS - EUROPE Conference, Bremen, 1-3.
Park, S., Jo, B. and Han, D., 2009. An efficient mac protocol for data collection in underwater wireless sensor networks. MILCOM - IEEE Military Communications Conference, Boston, MA, 1-5.
Rachman, R., Laksana, E. P., Putra, D. S. and Sari, R. F., 2012. Energy Consumption at the Node in Underwater Wireless Sensor Network (UWSNs). Sixth UKSim/AMSS European Symposium on Computer Modeling and Simulation, Valetta, 418-423.
Rezaei, H. F., Kruger, A. and Just, C., 2012. An energy harvesting scheme for underwater sensor applications. IEEE International Conference on Electro/Information Technology, Indianapolis, IN, 1-4.
Wahid, A. and Kim, D., 2012. Connectivity-based routing protocol for underwater wireless sensor networks. International Conference on ICT Convergence, Jeju Island, 589-590.
Yang, Y., Xiaomin, Z., Bo, P. and Yujing, F., 2009. Design of sensor nodes in underwater sensor networks. 4th IEEE Conference on Industrial Electronics and Applications, Xi'an, 3978-3982.
Yashwanth, N. and Sujatha, B. R., 2016. Wireless sensor node localization in underwater environment. International Conference on Electrical, Electronics, Communication, Computer and Optimization Techniques, Mysuru, 339-344.
Yu, C. H., Lee, K. H., Moon H. P., Choi, J. W. and Seo, Y. B., 2009. Sensor localization algorithms in underwater wireless sensor networks. ICCAS-SICE Conference, Fukuoka, 1760-1764.
Zhan, A., Chen, G. and Wang, W., 2009. Utilizing Automatic Underwater Vehicles to Prolong the Lifetime of Underwater Sensor Networks. Proceedings of 18th International Conference on Computer Communications and Networks, San Francisco, CA, 1-6.
Riverbed Modeler, https://www.riverbed.com/gb/, (31.10.2018).
Matlab Software, https://ww2.mathworks.cn/en/, (31.10.2018).