Sualtı Kablosuz Algılayıcı Ağlarda Aloha Tabanlı Maliyet Etkin Ortam Erişim Protokolü

Year 2019, Volume: 19 Issue: 1, 114 - 120, 28.05.2019

Muhammed Enes Bayrakdar

https://doi.org/10.35414/akufemubid.478095

Cited By: 1

Abstract

Sualtı algılayıcı ağlar;
okyanusta veri toplama, kirlilik izleme, okyanus örneklemesi gibi çok çeşitli
uygulamalarla hızla gelişen bir araştırma alanıdır. En çok araştırılan
alanlardan biri, birçok uygulamanın temelini oluşturan sualtı algılayıcı ağların
kapsama alanıdır. Kapsama alanı genellikle bir ağın algılayıcı tarafından ne
kadar etkin izlendiği ile ilgilidir. Su kirliliği başta olmak üzere, okyanus
veya deniz bölgesinde ortaya çıkan başlıca problemler vardır. Sualtı kirliliği
genel olarak asitleşmeye, plastik kalıntılara ve toksinlere sebep olmaktadır. Günümüzde
bu kirliliğin belirlenmesi, insan gözetimli izleme süreci ile gerçekleştirilmektedir.
Bu sebeple, kirliliğin oluşumunu tanımlamak için otomatik ve akıllı izleme
sistemine ihtiyaç duyulmaktadır. Önerdiğimiz benzetim modeli, su altındaki
kirliliğin oluşumunu tanımlayan ve alarm veren akıllı algılayıcı tabanlı izleme
sistemini tanımlamaktadır. Benzetim modelini tasarladığımız sistemin, maliyet
açısından verimli olması için ortam erişim protokolü olarak Aloha seçilmiştir. Sistemin
verimliliği benzetim modeli ile test edilerek mevcut insan gözetimi içeren
izleme sürecinden daha kararlı, düşük maliyetli ve yönetilebilir olduğu
gösterilmiştir. Algılayıcı ağ yükü 0 ile 6 arasında 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
olarak benzetim süresinin ortalarında elde edilmektedir.

Keywords

Sualtı, Algılayıcı ağlar, Aloha, Ortam erişim

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

Abstract

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
cost-effective 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.

Keywords

Underwater, Sensor networks, Aloha, Medium Acces

References

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