Rethinking Sustainable Campus Networking: Evidence from MST-Based Cabling Optimization and Device-Level Energy Consumption Measurements

Öz

The digitalization of university campuses has intensified the need for reliable, energy-efficient, and sustainable network infrastructures. While Wi-Fi systems offer flexibility and ease of deployment, their long-term energy footprint often exceeds that of wired alternatives. Conversely, Ethernet networks, though more stable and efficient, require careful optimization to reduce installation costs and material use. This study develops a two-stage framework that combines graph-theoretic optimization with empirical device-level measurements to inform sustainable campus network design. In the first phase, the physical network layout was modeled as a Minimum Spanning Tree (MST) using the Manhattan distance metric to reflect realistic in-building cable routing. The MST was solved via Kruskal’s algorithm and validated through a mixed-integer programming model implemented in Gurobi, yielding a total cable length of 1,435.69 metres and a 63.4% reduction in total cable length—implying comparable savings in material and installation costs—compared with a naive layout. In the second phase, an experimental analysis examined the energy consumption of Wi-Fi, native Ethernet, and Ethernet-adapter connections across multiple devices. Although the paired t-tests revealed no statistically significant differences at the 95% confidence level, the results indicate that device-specific characteristics play a crucial role in determining actual energy efficiency. Overall, the findings underscore the importance of integrating optimization and empirical evaluation in sustainable network planning. The proposed framework not only supports cost-efficient infrastructure decisions but also contributes to the broader goal of reducing the carbon footprint of digital campuses.

Anahtar Kelimeler

• : Minimum spanning tree
• layout planning
• mixed integer programming
• Kruskal algorithm
• t-statistic

Destekleyen Kurum

Scientific and Technological Research Council of Türkiye (TÜBİTAK), 2209-A Research Projects Support Programme for Undergraduate Students, Project No: 1919B012319079.

Proje Numarası

1919B012319079

Etik Beyan

This research does not involve experiments on human or animal subjects and does not collect any personal or sensitive data. The study is based on technical measurements on electronic devices and simulated network layouts. Therefore, in accordance with national and institutional guidelines, formal ethics committee approval was not required.

Teşekkür

This article is based on the results of an undergraduate research project supported by the Scientific and Technological Research Council of Türkiye (TÜBİTAK) under the 2209-A Research Projects Support Programme for Undergraduate Students (Project No: 1919B012319079). The authors sincerely thank TÜBİTAK for its financial support and encouragement throughout the research process. The authors are solely responsible for all evaluations and interpretations presented in this study.

Kampüs Ağlarını Sürdürülebilirlik Perspektifinden Yeniden Düşünmek: MST Tabanlı Kablolama Optimizasyonu ve Cihaz Düzeyinde Enerji Tüketimleri

Öz

Bu çalışma, üniversite kampüslerinde dijital ağ altyapısının sürdürülebilirlik odağında nasıl tasarlanabileceğine ilişkin bütüncül bir yaklaşım önermektedir. Önerilen çerçeve, bir yandan kablolu ağın fiziksel tasarımını iyileştirirken, diğer yandan uç cihazların farklı bağlantı türleri altındaki enerji tüketimini deneysel olarak inceleyen iki aşamalı bir yapıya dayanmaktadır. İlk aşamada, bir üniversite binasının tek katına ait yerleşim planı kullanılarak düğümler arası mesafeler iç mekân koşullarını daha gerçekçi yansıtan Manhattan uzaklığı ile hesaplanmış ve Minimum Yayılan Ağaç (MST) modeli aracılığıyla toplam Ethernet kablo uzunluğu minimize edilmiştir. Kruskal algoritması ve karma tamsayılı doğrusal programlama ile çözülen model, naif yerleşim senaryosuna kıyasla kablo metrajında %63,4’lük bir azalma (3.927 metreden 1.435,69 metreye) sağlamaktadır. İkinci aşamada ise dizüstü bilgisayar ve akıllı telefondan oluşan bir cihaz seti için Wi-Fi, yerleşik Ethernet ve adaptörlü Ethernet bağlantıları altında, 4K video akışı, büyük dosya indirme, bulut yedekleme ve çevrim içi toplantı gibi yüksek yük koşullarında enerji tüketimi ölçülmüş; elde edilen veriler eşleştirilmiş örneklem t-testleri ile analiz edilmiştir. Bulgular, bağlantı türleri arasındaki tüketim farklarının küçük kaldığını ve istatistiksel olarak anlamlı düzeye ulaşmadığını göstermektedir. Genel olarak sonuçlar, kampüs ağlarında sürdürülebilirlik açısından en büyük ve öngörülebilir kazanımların altyapı düzeyindeki kablolama ve donanım kararlarından kaynaklandığını, uç cihazların bağlantı tercihlerinin ise daha sınırlı bir etkiye sahip olduğunu ortaya koymaktadır.

Anahtar Kelimeler

• Minimum yayılan ağaç
• yerleşim planlama
• karma tam sayılı programlama
• Kruskal algoritması
• t-istatistiği

Destekleyen Kurum

Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (TÜBİTAK), 2209-A Üniversite Öğrencileri Araştırma Projeleri Destekleme Programı, Proje No: 1919B012319079.

Proje Numarası

1919B012319079

Etik Beyan

Bu araştırma insan veya hayvan denekler üzerinde deney içermemekte ve kişisel ya da hassas veri toplamamaktadır. Çalışma, elektronik cihazlar üzerinde yapılan teknik ölçümler ve simüle edilmiş ağ yerleşimleri temelinde yürütülmüştür. Bu nedenle ulusal ve kurumsal mevzuat çerçevesinde etik kurul onayı gerektirmemektedir. Araştırmanın tüm süreçlerinde ve raporlanmasında bilimsel araştırma ve yayın etiği ilkelerine uyulduğunu yazarlar beyan eder.

Teşekkür

Bu makale, Türkiye Bilimsel ve Teknolojik Araştırma Kurumu’nun (TÜBİTAK) 2209-A Üniversite Öğrencileri Araştırma Projeleri Destekleme Programı çerçevesinde desteklenen 1919B012319079 numaralı lisans araştırma projesinden türetilmiştir. Araştırma sürecine sağladığı mali katkı ve teşvik için TÜBİTAK’a içtenlikle teşekkür ederiz. Çalışmada yer alan değerlendirme ve yorumların tüm sorumluluğu yazarlara aittir.

Kaynakça

1. Abdelaal, A. M., & Ali, H. H. (2008). A graph theoretic approach for analysis and design of community wireless networks. AMCIS 2008 Proceedings, 310. Association for Information Systems.
2. Abdelmaguid, T. F. (2018). An efficient mixed integer linear programming model for the minimum spanning tree problem. Mathematics, 6(10), 183. https://doi.org/10.3390/math6100183.
3. Abdel-Wahab, H., Stoica, I., Sultan, F., & Wilson, K. (1997). A simple algorithm for computing minimum spanning trees in the internet. Information sciences, 101(1-2), 47-69. https://doi.org/10.1016/S0020-0255(97)00002-9. Agrawal, D. P. (1983). Graph theoretical analysis and design of multistage interconnection networks. IEEE Transactions on Computers, C-32(7), 637–648. https://doi.org/10.1109/TC.1983.1676295.
4. Ahmadi, H., & Martí, J. R. (2015). Minimum-loss network reconfiguration: A minimum spanning tree problem. Sustainable Energy, Grids and Networks, 1, 1-9. https://doi.org/10.1016/j.segan.2014.10.001.
5. Arogundade, O. T., Sobowale, B., & Akinwale, A. T. (2011). Prim algorithm approach to improving local access network in rural areas. International Journal of Computer Theory and Engineering, 3(3), 413-417. https://doi.org/10.7763/IJCTE.2011.V3.340.
6. Bader, D. A., & Burkhardt, P. (2022). A simple and efficient algorithm for finding minimum spanning tree replacement edges. Journal of Graph Algorithms and Applications, 26(4), 577–588. https://doi.org/10.7155/JGAA.00609.
7. Balakrishnan, A., Magnanti, T. L., & Wong, R. T. (1989). A dual-ascent procedure for large-scale uncapacitated network design. Operations Research, 37(5), 716-740. https://doi.org/10.1287/opre.37.5.716.
8. Balasubramanian, N., Balasubramanian, A., & Venkataramani, A. (2009). Energy consumption in mobile phones: A measurement study and implications for network applications. Proceedings of the 9th ACM SIGCOMM Conference on Internet Measurement (IMC '09), 280–293. ACM. https://doi.org/10.1145/1644893.1644927.

9. Baliga, J., Ayre, R. W. A., Hinton, K., & Tucker, R. S. (2011a). Energy consumption in wired and wireless access networks. IEEE Communications Magazine, 49(6), 70–77. https://doi.org/10.1109/MCOM.2011.5783987.
10. Baliga, J., Ayre, R. W. A., Hinton, K., & Tucker, R. S. (2011b). Green cloud computing: Balancing energy in processing, storage, and transport. Proceedings of the IEEE, 99(1), 149–167. https://doi.org/10.1109/JPROC.2010.2060451.
11. Bentley, J. L., & Friedman, J. H. (1978). Fast Algorithms for Constructing Minimal Spanning Trees in Coordinate Spaces. IEEE Transactions on Computers, 27(2), 97-105. https://doi.org/10.1109/TC.1978.167504.
12. Bianzino, A. P., Chaudet, C., Rossi, D., & Rougier, J.-L. (2012). A survey of green networking research. IEEE Communications Surveys & Tutorials, 14(1), 3–20. https://doi.org/10.1109/SURV.2011.113010.00106.
13. Bolla, R., Bruschi, R., Davoli, F., & Cucchietti, F. (2011). Energy efficiency in the future internet: A survey of existing approaches and trends in energy-aware fixed network infrastructures. IEEE Communications Surveys & Tutorials, 13(2), 223–244. https://doi.org/10.1109/SURV.2011.071410.00073.
14. Boorstyn, R. R., & Frank, H. (1977). Large-scale network topological optimization. IEEE Transactions on Communications, COM-25(1), 29–47. https://doi.org/10.1109/TCOM.1977.1093708.
15. Carroll, A., & Heiser, G. (2010). An analysis of power consumption in a smartphone. Proceedings of the 2010 USENIX Annual Technical Conference (ATC '10), 21–35.
16. Chandy, K. M., & Lo, T. (1973). The capacitated minimum spanning tree. Networks, 3(2), 173-181. https://doi.org/10.1002/net.3230030204.
17. Christensen, K., Reviriego, P., Nordman, B., Bennett, M., Mostowfi, M., & Maestro, J. A. (2010). IEEE 802.3az: The road to energy efficient Ethernet. IEEE Communications Magazine, 48(11), 50–56. https://doi.org/10.1109/MCOM.2010.5621967.
18. Crainic, T. G. (2000). Service network design in freight transportation. European journal of operational research, 122(2), 272-288. https://doi.org/10.1016/S0377-2217(99)00233-7.
19. Dantzig, G. B. (1960). On the Significance of Solving Linear Programming Problems with Some Integer Variables. Econometrica, 28(1), 30–44. https://doi.org/10.2307/1905292.
20. Dawadi, B. R., Rawat, D. B., Joshi, S. R., & Keitsch, M. M. (2020). Towards energy efficiency and green network infrastructure deployment in Nepal using software defined IPv6 network paradigm. The Electronic Journal of Information Systems in Developing Countries, 86(1), e12114. https://doi.org/10.1002/isd2.12114.
21. Derrible, S., & Kennedy, C. (2011). Applications of graph theory and network science to transit network design. Transport reviews, 31(4), 495-519. https://doi.org/10.1080/01441647.2010.543709.
22. Feremans, C., Labbé, M., & Laporte, G. (2003). Generalized network design problems. European Journal of Operational Research, 148(1), 1-13. https://doi.org/10.1016/S0377-2217(02)00404-6.
23. Fu, S., Yang, D., Mei, Z., & Zheng, W. (2025). Progress in Construction Robot Path-Planning Algorithms. Applied Sciences, 15(3), 1165. https://doi.org/10.3390/app15031165.
24. Garcia-Saavedra, A., Serrano, P., Banchs, A., & Bianchi, G. (2012). Energy consumption anatomy of 802.11 devices and its implication on modeling and design. Proceedings of the 8th International Conference on Emerging Networking Experiments and Technologies (CoNEXT '12), 169–180. ACM. https://doi.org/10.1145/2413176.2413197.
25. Golden, B., Raghavan, S., & Stanojević, D. (2005). Heuristic search for the generalized minimum spanning tree problem. INFORMS Journal on Computing, 17(3), 290-304. https://doi.org/10.1287/ijoc.1040.0077.
26. Gower, J. C., & Ross, G. J. (1969). Minimum spanning trees and single linkage cluster analysis. Journal of the Royal Statistical Society: Series C (Applied Statistics), 18(1), 54-64. https://doi.org/10.2307/2346439.
27. Graham, R. L., & Hell, P. (1985). On the history of the minimum spanning tree problem. Annals of the History of Computing, 7(1), 43-57. https://doi.org/10.1109/MAHC.1985.10011.
28. Greenberg, H. J. (1998). Greedy algorithms for minimum spanning tree. University of Colorado at Denver.
29. Gunaratne, C., Christensen, K., & Nordman, B. (2005). Managing energy consumption costs in desktop PCs and LAN switches with proxying, split TCP connections, and scaling of link speed. International journal of network management, 15(5), 297-310. https://doi.org/10.1002/nem.565.
30. Gunaratne, C., Christensen, K., Nordman, B., & Suen, S. (2008). Reducing the energy consumption of Ethernet with adaptive link rate (ALR). IEEE Transactions on Computers, 57(4), 448-461. https://doi.org/10.1109/TC.2007.70836.
31. Guo, H., Wang, F., James, G., Zhang, L., & Luo, J. (2018). Graph theory based topology design and energy routing control of the energy internet. IET Generation, Transmission & Distribution, 12(20), 4507-4514. https://doi.org/10.1049/iet-gtd.2018.6238.
32. Gupta, M., & Singh, S. (2003, August). Greening of the Internet. In Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications (pp. 19-26). https://doi.org/10.1145/863955.863959.
33. Hosseini, A. (2024). Uncertainty modeling and stability assessment of minimum spanning trees in network design. Mathematics, 12(23), 3812. https://doi.org/10.3390/math12233812.
34. Jayawant, P., & Glavin, K. (2009). Minimum spanning trees. Involve, a Journal of Mathematics, 2(4), 439-450. https://doi.org /10.2140/involve.2009.2.439. https://doi.org/10.2140/involve.2009.2.439.
35. Jones, C. E., Sivalingam, K. M., Agrawal, P., & Chen, J. C. (2001). A survey of energy efficient network protocols for wireless networks. Wireless Networks, 7(4), 343-358. https://doi.org/10.1023/A:1016627727877.
36. Kalic, G., Bojic, I., & Kusek, M. (2012). Energy consumption in Android phones when using wireless communication technologies. Proceedings of the 35th International Convention MIPRO, 754–759.
37. Kershenbaum, A., & Boorstyn, R. R. (1983). Centralized teleprocessing network design. Networks, 13(2), 279-293. https://doi.org/10.1002/net.3230130211.
38. Kershenbaum, A., & Van Slyke, R. (1972, August). Computing minimum spanning trees efficiently. In Proceedings of the ACM annual conference-Volume 1 (pp. 518-527). https://doi.org/10.1145/800193.569966.
39. Kim, M., Yoon, S. D., & Ahn, H. K. (2021). Shortest rectilinear path queries to rectangles in a rectangular domain. Computational Geometry, 99, 101796. https://doi.org/10.1016/j.comgeo.2021.101796.
40. Koutitas, G., & Demestichas, P. (2010). A review of energy efficiency in telecommunication networks. Telfor journal, 2(1), 2-7.
41. Kruskal, J. B. (1956). On the shortest spanning subtree of a graph and the traveling salesman problem. Proceedings of the American Mathematical society, 7(1), 48-50. https://doi.org/10.1090/S0002-9939-1956-0078686-7.
42. Lorincz, J., Capone, A., & Wu, J. (2019). Greener, energy-efficient and sustainable networks: State-of-the-art and new trends. Sensors, 19(22), 4864. https://doi.org/10.3390/s19224864.
43. Mahadevan, P., Sharma, P., Banerjee, S., & Ranganathan, P. (2009). A power benchmarking framework for network devices. Proceedings of the 8th International IFIP-TC 6 Networking Conference (NETWORKING '09), LNCS 5550, 795–808. Springer. https://doi.org/10.1007/978-3-642-01399-7_62.
44. Mejri, I., Layeb, S. B., & Zeghal, F. (2023). A survey on network design problems: main variants and resolution approaches. European Journal of Industrial Engineering, 17(2), 253-309. https://doi.org/10.1504/EJIE.2023.129443.
45. Minoux, M. (1989). Networks synthesis and optimum network design problems: Models, solution methods and applications. Networks, 19(3), 313-360. https://doi.org/10.1002/net.3230190305.
46. Mitchell, J. S. B. (2017). Shortest paths and networks. In J. E. Goodman, J. O’Rourke, & C. D. Tóth (Eds.), Handbook of Discrete and Computational Geometry (3rd ed., pp. 811–848). Chapman & Hall/CRC. https://doi.org/10.1201/9781315119601.
47. Mohamad, H., Zalnidzham, W. I. F. W., Salim, N. A., Shahbudin, S., & Yasin, Z. M. (2019). Power system restoration in distribution network using minimum spanning tree—Kruskal’s algorithm. Indonesian Journal of Electrical Engineering and Computer Science, 16(1), 1-8. https://doi.org/10.11591/ijeecs.v16.i1.pp1-8.
48. Nedevschi, S., Popa, L., Iannaccone, G., Ratnasamy, S., & Wetherall, D. (2008). Reducing network energy consumption via sleeping and rate-adaptation. Proceedings of the 5th USENIX Symposium on Networked Systems Design and Implementation (NSDI '08), 323–336.
49. Niewiadomska-Szynkiewicz, E., Sikora, A., Arabas, P., Kamola, M., Mincer, M., & Kołodziej, J. (2014). Dynamic power management in energy aware computer networks and data intensive computing systems. Future Generation Computer Systems, 37, 284-296. https://doi.org/10.1016/j.future.2013.10.002.
50. Osipov, V., Sanders, P., & Singler, J. (2009, January). The filter-kruskal minimum spanning tree algorithm. In 2009 Proceedings of the Eleventh Workshop on Algorithm Engineering and Experiments (ALENEX) (pp. 52-61). Society for Industrial and Applied Mathematics. https://doi.org/10.1137/1.9781611972894.5.
51. Pavon, W., Torres, M., & Inga, E. (2024). Integrating minimum spanning tree and MILP in urban planning: a novel algorithmic perspective. Buildings, 14(1), 213. https://doi.org/10.3390/buildings14010213.
52. Perrucci, G. P., Fitzek, F. H. P., & Widmer, J. (2011). Survey on energy consumption entities on the smartphone platform. Proceedings of the IEEE 73rd Vehicular Technology Conference (VTC Spring), 1–6. https://doi.org/10.1109/VETECS.2011.5956528.
53. Pop, P. C. (2020). The generalized minimum spanning tree problem: An overview of formulations, solution procedures and latest advances. European Journal of Operational Research, 283(1), 1-15. https://doi.org/10.1016/j.ejor.2019.05.017.
54. Pop, P. C., Kern, W., & Still, G. (2006). A new relaxation method for the generalized minimum spanning tree problem. European Journal of Operational Research, 170(3), 900-908. https://doi.org/10.1016/j.ejor.2004.07.058.
55. Prim, R. C. (1957). Shortest connection networks and some generalizations. The Bell System Technical Journal, 36(6), 1389-1401. https://doi.org/10.1002/j.1538-7305.1957.tb01515.x.
56. Reviriego, P., Christensen, K., Rabanillo, J., & Maestro, J. A. (2011). An initial evaluation of energy efficient Ethernet. IEEE Communications Letters, 15(5), 578–580. https://doi.org/10.1109/LCOMM.2011.040111.102259.
57. Reyes-Polanco, P., & Contreras-Bolton, C. (2025). A novel three-stage matheuristic for the capacitated minimum spanning tree problem with time windows. Knowledge-Based Systems, 310, 112971. https://doi.org/10.1016/j.knosys.2025.112971.
58. Rice, A., & Hay, S. (2010). Measuring mobile phone energy consumption for 802.11 wireless networking. Pervasive and Mobile Computing, 6(6), 593-606. https://doi.org/10.1016/j.pmcj.2010.07.005.
59. Sarkar, A., Batta, R., & Nagi, R. (2009). Finding rectilinear least cost paths in the presence of convex polygonal congested regions. Computers & operations research, 36(3), 737-754. https://doi.org/10.1016/j.cor.2007.10.023.
60. Wang, E., Huo, W., Xu, S., Qu, P., & Na, L. (2025). Autonomous navigation of indoor wheeled robots based on improved Gmapping and improved Bidirectional A*. Discov. Robot. 1, 7. https://doi.org/10.1007/s44430-025-00007-z.
61. Wang, L., & Manner, J. (2010, December). Energy consumption analysis of WLAN, 2G and 3G interfaces. In 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing (pp. 300-307). IEEE. https://doi.org/10.1109/GreenCom-CPSCom.2010.81.
62. Wieberneit, N. (2008). Service network design for freight transportation: a review. OR spectrum, 30(1), 77-112. https://doi.org/10.1007/s00291-007-0079-2.
63. Xu, Y., Ke, G. Y., Li, R., & Zhang, L. (2026). Target-based distributionally robust minimum spanning tree problem. European Journal of Operational Research, 332(1), 191-208. https://doi.org/10.1016/j.ejor.2026.02.024.
64. Yaman, H., Karaşan, O. E., & Pınar, M. Ç. (2001). The robust spanning tree problem with interval data. Operations research letters, 29(1), 31-40. https://doi.org/10.1016/S0167-6377(01)00078-5.