Geo-localized Network Data Analysis Using VIKOR: Application of Network Health Monitoring in Turkey

Öz

Middle-high level managers of operators of telecommunications have to follow many key parameters to increase the profitability along with strategy of company. Therefore, easily readable data are being provided to managers by experts. With large screens in rooms, these data are monitored momentarily and ensured to take necessary actions without creating customer dissatisfaction. Especially for operators, it is one of the key parameters to manage the network in a quality and healthy way without creating complaints. It is important to monitor network health with appointed criteria and methods. In this context, there have been weights of province-based network health by processing geolocation-based big data and using the VIKOR method. Thus, there will have been presented the network monitoring opportunity from instant real user information to managers. Although there is a lot of criteria and methods for network monitoring, it will have been one of the priority studies that will enable the analysis of geo location data with a MCDM method in this area.

Anahtar Kelimeler

• Geolocation
• VIKOR
• Telecommunication
• Data analysis
• Network monitoring

VIKOR Kullanarak Coğrafi Konumlama Ağ Verileri Analizi: Türkiye'de Ağ Sağlığı İzleme Uygulaması

Öz

Telekomünikasyon operatörlerinin orta-yüksek düzey yöneticileri, şirket stratejisiyle birlikte karlılığı artırmak için birçok önemli parametreyi takip etmek zorundadır. Bu nedenle, uzmanlar tarafından yöneticilere kolay okunabilir veriler sunulmaktadır. Bu veriler, odalardaki büyük ekranlar aracılığıyla anlık olarak izlenmekte ve müşteri memnuniyetsizliği yaratmadan gerekli önlemlerin alınmasını sağlamaktadır. Özellikle operatörler için, şikayet oluşturmadan ağını kaliteli ve sağlıklı bir şekilde yönetmek, önemli parametrelerden biridir. Ağ sağlığını belirli kriterler ve yöntemlerle izlemek önemlidir. Bu bağlamda, coğrafi konum tabanlı büyük veriyi işleyerek ve VIKOR yöntemini kullanarak il bazlı ağ sağlığı ağırlıkları oluşturulmuştur. Böylece, yöneticilere anlık gerçek kullanıcı bilgilerinden ağ izleme fırsatı sunulmuştur. Ağ izleme için birçok kriter ve yöntem bulunmasına rağmen, coğrafi konum verilerinin ÇKKV yöntemiyle analizine olanak tanıyan öncelikli çalışmalardan biri olacaktır.

Anahtar Kelimeler

• Coğrafi konumlama
• VIKOR
• Telekomünikasyon
• Veri analizi
• Ağ izleme

Kaynakça

1. Adwan, Omar, Hossam Faris, Khalid Jaradat, Osama Harfoushi, and Nazeeh Ghatasheh. 2014. “Predicting Customer Churn in Telecom Industry Using Multilayer Preceptron Neural Networks: Modeling and Analysis.” Life Science Journal 11(3):75–81. https://doi.org/https://doi.org/10.7537/marslsj110314.11.
2. Anisetti, Marco, Claudio A. Ardagna, Valerio Bellandi, Ernesto Damiani, Mario Döller, Florian Stegmaier, Tilmann Rabl, Harald Kosch, and Lionel Brunie. 2012. “Landmark-Assisted Location and Tracking in Outdoor Mobile Network.” Multimedia Tools and Applications 59(1):89–111. https://doi.org/10.1007/s11042-010-0721-x.
3. Avikal, Shwetank, Amit Kumar Singh, K. C. Nithi. Kumar, Biru Rajak, and Gaurav Kumar Badhotiya. 2021. “A Decision-Making Approach for Installation of Telecom Tower.” Materials Today: Proceedings 46(xxxx):11084–86. https://doi.org/10.1016/j.matpr.2021.02.228.
4. Bähr, Sebastian, Georg Christoph Haas, Florian Keusch, Frauke Kreuter, and Mark Trappmann. 2022. “Missing Data and Other Measurement Quality Issues in Mobile Geolocation Sensor Data.” Social Science Computer Review 40(1):212–35. https://doi.org/10.1177/0894439320944118.
5. Dzemyda, Gintautas, Jolita Bernatavičienė, and Janusz Kacprzyk. 2020. Data Science: New Issues, Challenges and Applications. Vol. 869.
6. Gonçalves, Tatiana Santos, Pedro Jerónimo, and João Carlos Correia. 2021. “Local News and Geolocation Technology in the Case of Portugal.” Publications 9(4):1–13. https://doi.org/10.3390/publications9040053.
7. Huang, Chi Yo, Pei Chu Hsu, and Gwo Hshiung Tzeng. 2012. “Evaluating Cloud Computing Based Telecommunications Service Quality Enhancement by Using a New Hybrid MCDM Model.” Smart Innovation, Systems and Technologies 15:519–36. https://doi.org/10.1007/978-3-642-29977-3_52.
8. Idris, Adnan, and Asifullah Khan. 2012. “Customer Churn Prediction for Telecommunication: Employing Various Various Features Selection Techniques and Tree-Based Ensemble Classifiers.” 2012 15th International Multitopic Conference, INMIC 2012 23–27. https://doi.org/10.1109/INMIC.2012.6511498.

9. Ivanochko, O., M. Gregus, M. Szalek, J. Rolinski, and B. Stolinski. 2021. “City Tourism Services with Mobile Geolocation Sharing.” Procedia Computer Science 191:49–56. https://doi.org/10.1016/j.procs.2021.07.010.
10. Jones, T. O., and W. E. Sasser. 1995. “Why Satisfied Customers Defect.” Harvard Business Review 73(6):88.
11. Lin, Chia Li, Ying Hsiu Shih, Gwo Hshiung Tzeng, and Hsiao Cheng Yu. 2016. “A Service Selection Model for Digital Music Service Platforms Using a Hybrid MCDM Approach.” Applied Soft Computing Journal 48:385–403. https://doi.org/10.1016/j.asoc.2016.05.035.
12. Marquez-Barja, Johann, Carlos T. Calafate, Juan Carlos Cano, and Pietro Manzoni. 2012. “A Geolocation-Based Vertical Handover Decision Algorithm for Vehicular Networks.” Proceedings - Conference on Local Computer Networks, LCN 360–67. https://doi.org/10.1109/LCN.2012.6423648.
13. Önüt, Semih, Selin Soner Kara, and Elif Işik. 2009. “Long Term Supplier Selection Using a Combined Fuzzy MCDM Approach: A Case Study for a Telecommunication Company.” Expert Systems with Applications 36(2 PART 2):3887–95. https://doi.org/10.1016/j.eswa.2008.02.045.
14. Opricovic, Serafim, and Gwo Hshiung Tzeng. 2004. “Compromise Solution by MCDM Methods: A Comparative Analysis of VIKOR and TOPSIS.” European Journal of Operational Research 156(2):445–55. https://doi.org/10.1016/S0377-2217(03)00020-1.
15. Opricovic, Serafim, and Gwo Hshiung Tzeng. 2007. “Extended VIKOR Method in Comparison with Outranking Methods.” European Journal of Operational Research 178(2):514–29. https://doi.org/10.1016/j.ejor.2006.01.020.
16. Peixoto, Pedro S., Diego Marcondes, Cláudia Peixoto, and Sérgio M. Oliva. 2020. “Modeling Future Spread of Infections via Mobile Geolocation Data and Population Dynamics. An Application to COVID-19 in Brazil.” PLoS ONE 15(7 July):1–23. https://doi.org/10.1371/journal.pone.0235732.
17. Pidchenko, Sergiy, Oksana Kucheruk, Oleh Pyvovar, Viktor Stetsiuk, and Viktor Mishan. 2023. “A Multi-Criteria Approach To Decision-Making in Telecommunication Network Components Selection.” Radioelectronic and Computer Systems (1–105):155–65. https://doi.org/10.32620/reks.2023.1.13.
18. Priandani, Nurizal D., Herman Tolle, Anggi G. Hapsani, and Lutfi Fanani. 2017. “Malang Historical Tourism Guide Mobile Application Based on Geolocation.” ACM International Conference Proceeding Series 98–101. https://doi.org/10.1145/3056662.3056695.
19. Roxin, A., J. Gaber, and M. Wack. 2007. “Survey of Wireless Geolocation Techniques.” IEEE Globecom Workshops 1–9. https://doi.org/HTTPS://DOİ.ORG/ 10.1109/GLOCOMW.2007.4437809.
20. Ullah, Irfan, Basit Raza, Ahmad Kamran Malik, Muhammad Imran, Saif Ul Islam, and Sung Won Kim. 2019. “A Churn Prediction Model Using Random Forest: Analysis of Machine Learning Techniques for Churn Prediction and Factor Identification in Telecom Sector.” IEEE Access 7(c):60134–49. https://doi.org/10.1109/ACCESS.2019.2914999.
21. Wei, Chih Ping, and I. Tang Chiu. 2002. “Turning Telecommunications Call Details to Churn Prediction: A Data Mining Approach.” Expert Systems with Applications 23(2):103–12. https://doi.org/10.1016/S0957-4174(02)00030-1.