Analyzing the Impact of Vehicle Node Survivability Using the Graph Invariant Methodology in Smart Cities
Received: 14 April 2025 | Revised: 19 May 2025, 2 June 2025, 10 June 2025, and 17 June 2025 | Accepted: 18 June 2025 | Online: 9 July 2025
Corresponding author: Chandu Jagan Sekhar Madala
Abstract
The existence of complex networks is prevalent across various domains, including transportation systems, underscoring the necessity of understanding their resilience for maintaining operational stability. This study examines the impact of vehicular node failures, arising from targeted attacks or random incidents, on the stability of centrality metrics across diverse graph topologies, including Star, Ring, Partial Mesh, Trimet, Dual Ring, Tree, Balanced Tree, and Hybrid Tree. A comparative analysis was conducted using centrality measures, such as degree, betweenness, closeness, eigenvector centrality, clustering coefficient, and assortativity, employing a Python-based simulation framework to model the node failures at rates of 10%, 20%, and 30%. The results indicate that the differential impacts of the node failures vary based on the centrality metric and network topology; notably, the degree centrality demonstrates greater resilience, while the betweenness centrality is particularly sensitive to disruptions. Additionally, the network size, average degree, and the nature of the node failures significantly influence these metrics' stability. The findings highlight the critical relationships between the node survivability and centrality stability, offering valuable insights for developing robust Vehicular Ad Hoc Networks (VANETs) and guiding strategic decision-making in transportation planning and infrastructure development.
Keywords:
graph topologies, network resilience, node failures, complex networks, stability analysisDownloads
References
A. Saxena and S. Iyengar, Centrality Measures in Complex Networks: A Survey. 2020. https://doi.org/10.48550/arXiv.2011.07190.
D. S. Callaway, M. E. Newman, S. H. Strogatz, and D. J. Watts, "Network robustness and fragility: percolation on random graphs," Physical Review Letters, vol. 85, no. 25, pp. 5468–5471, Dec. 2000. DOI: https://doi.org/10.1103/PhysRevLett.85.5468
W. Ellens and R. Kooij, "Graph measures and network robustness," Nov. 2013.
A. Hagberg, P. Swart, and D. Chult, Exploring Network Structure, Dynamics, and Function Using NetworkX. 2008. DOI: https://doi.org/10.25080/TCWV9851
U. Brandes, "A faster algorithm for betweenness centrality" The Journal of Mathematical Sociology, vol. 25, no. 2, pp. 163–177, Jun. 2001. DOI: https://doi.org/10.1080/0022250X.2001.9990249
A. Bavelas, "Communication Patterns in Task‐Oriented Groups," The Journal of the Acoustical Society of America, vol. 22, no. 6, pp. 725–730, Nov. 1950. DOI: https://doi.org/10.1121/1.1906679
M. Brede, "Networks—An Introduction. Mark E. J. Newman. (2010, Oxford University Press.)," Artificial Life, vol. 18, no. 2, pp. 241–242, Apr. 2012. DOI: https://doi.org/10.1162/artl_r_00062
D. J. Watts and S. H. Strogatz, "Collective dynamics of ‘small-world’ networks," Nature, vol. 393, no. 6684, pp. 440–442, Jun. 1998. DOI: https://doi.org/10.1038/30918
M. E. J. Newman, "Assortative Mixing in Networks," Physical Review Letters, vol. 89, no. 20, Oct. 2002, Art. no. 208701. DOI: https://doi.org/10.1103/PhysRevLett.89.208701
M. Raya and J.-P. Hubaux, "The security of vehicular ad hoc networks," in Proceedings of the 3rd ACM workshop on Security of ad hoc and sensor networks, Alexandria, VA, USA, Nov. 2005, pp. 11–21. DOI: https://doi.org/10.1145/1102219.1102223
H. Hartenstein and K. P. Laberteaux, "A tutorial survey on vehicular ad hoc networks," IEEE Communications Magazine, vol. 46, no. 6, pp. 164–171, Jun. 2008. DOI: https://doi.org/10.1109/MCOM.2008.4539481
S. Segarra and A. Ribeiro, "Stability and Continuity of Centrality Measures in Weighted Graphs," IEEE Transactions on Signal Processing, vol. 64, no. 3, pp. 543–555, Feb. 2016. DOI: https://doi.org/10.1109/TSP.2015.2486740
L. Cavallaro, S. Costantini, P. De Meo, A. Liotta, and G. Stilo, "Network Connectivity Under a Probabilistic Node Failure Model," IEEE Transactions on Network Science and Engineering, vol. 9, no. 4, pp. 2463–2480, Jul. 2022. DOI: https://doi.org/10.1109/TNSE.2022.3164357
M. S. Hossain and G. Muhammad, "Cloud-assisted Industrial Internet of Things (IIoT) – Enabled framework for health monitoring," Computer Networks, vol. 101, pp. 192–202, Jun. 2016. DOI: https://doi.org/10.1016/j.comnet.2016.01.009
M. Pirani, A. Mitra, and S. Sundaram, "Graph-theoretic approaches for analyzing the resilience of distributed control systems: A tutorial and survey," Automatica, vol. 157, Nov. 2023, Art. no. 111264. DOI: https://doi.org/10.1016/j.automatica.2023.111264
Z. Wan, Y. Mahajan, B. Kang, T. Moore, and J.-H. Cho, A Survey on Centrality Metrics and Their Implications in Network Resilience. 2020. https://doi.org/10.48550/arXiv.2011.14575. DOI: https://doi.org/10.1109/ACCESS.2021.3094196
A. Zanella, N. Bui, A. Castellani, L. Vangelista, and M. Zorzi, "Internet of Things for Smart Cities," IEEE Internet of Things Journal, vol. 1, no. 1, pp. 22–32, Oct. 2014. DOI: https://doi.org/10.1109/JIOT.2014.2306328
M. A. Matin, M. M. Islam, M. A. Matin, and M. M. Islam, "Overview of Wireless Sensor Network," in Wireless Sensor Networks - Technology and Protocols, M. A. Matin, Ed. IntechOpen, 2012. DOI: https://doi.org/10.5772/49376
J. Stankovic, "Research Directions for the Internet of Things," IEEE Internet of Things Journal, vol. 1, pp. 3–9, Feb. 2014. DOI: https://doi.org/10.1109/JIOT.2014.2312291
S. Olariu, M. C. Weigle, S. Olariu, and M. C. Weigle, Vehicular Networks: From Theory to Practice, 1st ed. Chapman and Hall/CRC, 2009. DOI: https://doi.org/10.1201/9781420085891
K. Akkaya and M. Younis, "A survey on routing protocols for wireless sensor networks," Ad Hoc Networks, vol. 3, no. 3, pp. 325–349, May 2005. DOI: https://doi.org/10.1016/j.adhoc.2003.09.010
G. Ghoshal and A.-L. Barabási, "Ranking stability and super-stable nodes in complex networks," Nature communications, vol. 2, no. 1, 2011, Art. no. 394. DOI: https://doi.org/10.1038/ncomms1396
E. Costenbader and T. W. Valente, "The stability of centrality measures when networks are sampled," Social Networks, vol. 25, no. 4, pp. 283–307, Oct. 2003. DOI: https://doi.org/10.1016/S0378-8733(03)00012-1
S. S. Amiripalli and V. Bobba, "An Optimal Graph based zigbee mesh for smart homes," 2020.
S. S. Amiripalli, V. Rampay, and M. S. N. V. Jitendra, "A graph analytics on telecom backbone networks using networkX," AIP Conference Proceedings, vol. 2796, no. 1, Jul. 2023, Art. no. 150003. DOI: https://doi.org/10.1063/5.0148904
S. S. Amiripalli and V. Bobba, "Trimet graph optimization (TGO) based methodology for scalability and survivability in wireless networks," International Journal of Advanced Trends in Computer Science and Engineering, vol. 8, no. 6, pp. 3454–3460, 2019. DOI: https://doi.org/10.30534/ijatcse/2019/121862019
L. D. Valdez et al., "Erratum to: Cascading failures in complex networks," Journal of Complex Networks, vol. 8, no. 3, Jun. 2020, Art. no. cnaa022. DOI: https://doi.org/10.1093/comnet/cnaa022
S. S. Amiripalli, V. Bobba, S. M. Thampi, and E.-S. M. El-Alfy, "Impact of trimet graph optimization topology on scalable networks," Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology, vol. 36, no. 3, pp. 2431–2442, Jan. 2019. DOI: https://doi.org/10.3233/JIFS-169954
N. Bendahman and D. Lotfi, "Unveiling Influence in Networks: A Novel Centrality Metric and Comparative Analysis through Graph-Based Models," Entropy (Basel, Switzerland), vol. 26, no. 6, Dec. 2024, Art. no. 486. DOI: https://doi.org/10.3390/e26060486
O. Ugurlu, "Comparative analysis of centrality measures for identifying critical nodes in complex networks," Journal of Computational Science, vol. 62, Jul. 2022, Art. no. 101738. DOI: https://doi.org/10.1016/j.jocs.2022.101738
F. Dablander and M. Hinne, "Node centrality measures are a poor substitute for causal inference," Scientific Reports, vol. 9, no. 1, Dec. 2019, Art. no. 6846. DOI: https://doi.org/10.1038/s41598-019-43033-9
K. Raju Rajana and S. S. Amiripalli, "A Novel Lucas-based Clustering Optimization for Enhancing Survivability in Smart Home Design," Engineering, Technology & Applied Science Research, vol. 15, no. 1, pp. 19903–19909, Feb. 2025. DOI: https://doi.org/10.48084/etasr.9232
Downloads
How to Cite
License
Copyright (c) 2025 Chandu Jagan Sekhar Madala, Naveen Kumar Kuppili
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
