Graph-theoretic trust models for secure IoT and medical sensor networks
Ravindra S. Patilravipatilich@gmail.comDepartment of Emergency MedicineKrishna Institute of Medical SciencesKrishna Vishwa Vidyapeeth (Deemed to be University)Karad, Maharashtra, 415539, IndiaView full profile → , Deepika Sharmadeepika.sharma@niu.edu.inDepartment of Computer SciencesNoida International UniversityGreater Noida, Uttar Pradesh, 201312, IndiaView full profile → , Rahul A. Padgilwarrahul.padgilwar1@vit.eduDepartment of Engineering Science and HumanitiesVishwakarma Institute of TechnologyPune, Maharashtra, 411037, IndiaView full profile → , *Ramachandro MajjiCorresponding authorramachandro.m@vardhaman.orgDepartment of Computer Science and Engineering (Artificial Intelligence & Machine Learning)Vardhaman College of EngineeringShamshabad, Hyderabad, Telangana, 501218, IndiaView full profile → , L. Yamunalyamuna@gmail.comDepartment of Computer Science and Engineering (Artificial Intelligence & Machine Learning)Pragati Engineering CollegeKakinada, Andhra Pradesh, 533437, IndiaView full profile → , N. Muninathanmuninathan@maher.ac.inCentral Research LaboratoryMeenakshi Medical College Hospital & Research InstituteMeenakshi Academy of Higher Education & ResearchKanchipuram, Tamil Nadu, 631552, 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-2496
- Pages:
- 3707–3718
Abstract
To secure IoT and medical sensor networks, mechanisms that are able to detect malicious and compromised devices or unreliable devices are needed. In this study a graph-theoretic trust model is suggested, where the sensors are the nodes and the communication relationship between the sensors is represented by a weighted edge. The trust is estimated directly from packet delivery, data consistency, responsiveness and authentication behavior, and indirectly is based on a collection of the recommendations of neighboring nodes. A composite score adaptively weights both direct, indirect and historical evidence and contextual factors. The operations performed on the nodes (adjacency-matrix operations) and the analysis of paths (path-based analysis) both enable scalable propagation of trust and isolation of suspicious nodes. The simulation results show better detection of malicious nodes, reliability of packets and the robustness of the network when compared to the traditional reputation methods. The framework offers an easy-to-understand and lightweight basis for secure distributed healthcare monitoring applications.
Keywords
Subject Classifications
References
[1] M. J. C. S. Reis, “Trust-aware federated graph learning for secure and energy-efficient IoT ecosystems,” Computers, vol. 15, no. 2, Art. no. 121 (2026), doi: 10.3390/computers15020121.
[2] L. Wang, K. Petrova, and M. L. Yang, “Trust models in wireless sensor networks for defending against denial-of-service attacks: A literature review,” Appl. Sci., vol. 15, no. 6, Art. no. 3075 (2025), doi: 10.3390/app15063075.
[3] Y. Alghofaili and M. A. Rassam, “A trust management model for IoT devices and services based on the multi-criteria decision-making approach and deep long short-term memory technique,” Sensors, vol. 22, no. 2, Art. no. 634 (2022), doi: 10.3390/s22020634.
[4] A. Rodríguez, A. López-Chau, L. Dávila-Nicanor, V. Landassuri-Moreno, and S. Lazcano-Salas, “Lightweight protection mechanisms for IoT networks based on trust modelling,” IoT, vol. 7, no. 1, Art. no. 18 (2026), doi: 10.3390/iot7010018.
[5] K. Miličević, L. Omrčen, M. Kohler, and I. Lukić, “Trust model concept for IoT blockchain applications as part of the digital transformation of metrology,” Sensors, vol. 22, no. 13, Art. no. 4708 (2022), doi: 10.3390/s22134708.
[6] M. J. C. S. Reis, “Trust-aware and energy-efficient federated learning for secure sensor networks at the edge,” Sensors, vol. 26, no. 8, Art. no. 2307 (2026), doi: 10.3390/s26082307.
[7] S. M. Muzammal, R. K. Murugesan, N. Z. Jhanjhi, M. Humayun, A. O. Ibrahim, and A. Abdelmaboud, “A trust-based model for secure routing against RPL attacks in Internet of Things,” Sensors, vol. 22, no. 18, Art. no. 7052 (2022), doi: 10.3390/s22187052.
[8] A. Saleh, P. Joshi, R. S. Rathore, and S. S. Sengar, “Trust-aware routing mechanism through an edge node for IoT-enabled sensor networks,” Sensors, vol. 22, no. 20, Art. no. 7820 (2022), doi: 10.3390/s22207820.
[9] M. Ileana, P. Petrov, and V. Milev, “Secure integration of sensor networks and distributed web systems for electronic health records and custom CRM,” Sensors, vol. 25, no. 16, Art. no. 5102 (2025), doi: 10.3390/s25165102.
[10] T. A. Wani, P. P. Bedekar, S. Raut, T. Porwal, M. P. Rao, and B. N. M. Rao, “Analysis of edge colorings and c′(G) in discrete structures using graph theoretic approaches,” J. Discrete Math. Sci. Cryptogr., vol. 29, no. 2-A, pp. 437–444 (2026), doi: 10.47974/JDMSC-2475.
[11] T. Ngo, J. Yin, Y.-F. Ge, and H. Wang, “Optimizing IoT intrusion detection—A graph neural network approach with attribute-based graph construction,” Information, vol. 16, no. 6, Art. no. 499 (2025), doi: 10.3390/info16060499.
[12] M. A. Khan, R. N. B. Rais, O. Khalid, and S. Ahmad, “Trust-based optimized reporting for detection and prevention of black hole attacks in low-power and lossy green IoT networks,” Sensors, vol. 24, no. 6, Art. no. 1775 (2024), doi: 10.3390/s24061775.
[13] V. Aivaliotis, K. Tsantikidou, and N. Sklavos, “IoT-based multi-sensor healthcare architectures and a lightweight-based privacy scheme,” Sensors, vol. 22, no. 11, Art. no. 4269 (2022), doi: 10.3390/s22114269.
[14] M. S. Abdalzaher, K. Seddik, M. Elsabrouty, O. Muta, H. Furukawa, and A. Abdel-Rahman, “Game theory meets wireless sensor networks security requirements and threats mitigation: A survey,” Sensors, vol. 16, no. 7, Art. no. 1003 (2016), doi: 10.3390/s16071003.
