Graph-based modeling and optimization of secure routing mechanisms in wireless and sensor network architectures
Sanjivani Hemant Kulkarnisanjivani.kulkarni@mitwpu.edu.inDepartment of Computer Science and EngineeringMIT World Peace UniversityPune, Maharashtra, 411038, IndiaView full profile → , *Sulakshana Sagar MalwadeCorresponding authorsulakshana.malwade@mitwpu.edu.inDepartment of Computer Science and EngineeringMIT World Peace UniversityPune, Maharashtra, 411038, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 01 Mar 2026
- Published Online:
- 31 Oct 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-2507
- Pages:
- 3837–3847
Abstract
In wireless or sensor networks, due to limited resources, dynamic network topology, unreliable communication links, and malicious intrusion, it is still difficult to find secure and energy efficient routing schemes. In this study, a graph based modelling and multiobjective optimization model for secure routing in wireless sensor networks is proposed. The sensor nodes and communication links are modelled as a weighted graph which includes link reliability, node trust, link energy consumption, transmission delay and security risks. A composite routing cost function dynamically assesses possible paths, and discards nodes that are malicious, unreliable or have little energy. The proposed framework provides a route selection mechanism that can be adapted to changing scenarios on network and security. Experimental examination indicates that the packet delivery, attack avoidance, energy efficiency, communication delay and network robustness are superior to the conventional shortest-path, energy-aware, and trust-based routing methods. Dynamic trust evaluation adds more to the anti-blackhole and anti-selective-forwarding protection. The system design proposed offers a scalable and resource-efficient solution to ensure reliable, adaptable, and secure communications for future wireless sensor and IoT systems.
Keywords
Subject Classifications
References
[1] Q. Cappart, D. Chételat, E. B. Khalil, A. Lodi, C. Morris, and P. Veličković, “Combinatorial Optimization and Reasoning with Graph Neural Networks,” J. Mach. Learn. Res., vol. 24, pp. 1–61 (2023).
[2] Z. A. Dagdeviren, “Weighted Connected Vertex Cover Based Energy-Efficient Link Monitoring for Wireless Sensor Networks Towards Secure Internet of Things,” IEEE Access, vol. 9, pp. 10107–10119 (2021).
[3] M. Majid, S. Habib, A. R. Javed, M. Rizwan, G. Srivastava, T. R. Gadekallu, and C. Lin, “Applications of Wireless Sensor Networks and Internet of Things Frameworks in the Industry Revolution 4.0: A Systematic Literature Review,” Sensors, vol. 22, Art. no. 2087 (2022).
[4] X. Xie, Z. Dou, and Y. Zhang, “Reinforcement Learning-Based Intelligent Decision-Making for Communication Parameters,” KSII Trans. Internet Inf. Syst., vol. 16, pp. 2942–2960 (2022).
[5] Z. Al Aghbari, A. M. Khedr, W. Osamy, I. Arif, and D. P. Agrawal, “Routing in Wireless Sensor Networks Using Optimization Techniques: A Survey,” Wireless Pers. Commun., vol. 111, pp. 2407–2434 (2020).
[6] S. Sharma, P. Rathi, R. K. Popli, S. Tanwar, and P. Vats, “Algebraic Graph Models for Secure Distributed Networks and Cryptographic Systems,” J. Discrete Math. Sci. Cryptogr., vol. 28, no. 8, pp. 3071–3080 (2025), doi: 10.47974/JDMSC-2468.
[7] H. Abdulrab, F. A. Hussin, A. Abd Aziz, A. Awang, I. Ismail, and P. A. M. Devan, “Reliable Fault-Tolerant-Based Multipath Routing Model for Industrial Wireless Control Systems,” Appl. Sci., vol. 12, Art. no. 544 (2022).
[8] M. Abbasi, A. Shahraki, and A. Taherkordi, “Deep Learning for Network Traffic Monitoring and Analysis (NTMA): A Survey,” Comput. Commun., vol. 170, pp. 19–41 (2021).
[9] A. K. Sharma, N. Purohit, S. Joshi, I. U. Lakkewar, and P. Khobragade, “Securing IoT Environments: Deep Learning-Based In trusion Detection,” in Deep Learning for Intrusion Detection, F. S. Masoodi and A. Bamhdi, Eds. Wiley, ch. 12, pp. 251–266 (2026), doi: 10.1002/9781394285198.ch12.
[10] M. Kol, E. E. Uslu, Z. A. Dagdeviren, and O. Dagdeviren, “Design and Analysis of Minimum Weighted Connected Capacitated Vertex Cover Algorithms for Link Monitoring in IoT-Enabled WSNs,” Sensors, vol. 26, Art. no. 2752 (2026).
[11] L. Sixu, W. Muqing, and Z. Min, “Particle Swarm Optimization and Artificial Bee Colony Algorithm for Clustering and Mobile-Based Software-Defined Wireless Sensor Networks,” Wireless Netw., vol. 28, pp. 1671–1688 (2022).
[12] O. I. Khalaf, C. A. Romero, S. Hassan, and M. T. Iqbal, “Mitigating Hotspot Issues in Heterogeneous Wireless Sensor Networks,” J. Sensors, vol. 2022, Art. no. 7909472 (2022).
[13] O. E. Oluyisola, F. Sgarbossa, and J. O. Strandhagen, “Smart Production Planning and Control: Concept, Use Cases, and Sustainability Implications,” Sustainability, vol. 12, Art. no. 3791 (2020).
