Trust Management in the Internet of Things

The rapid adoption of the Internet of Things (IoT) has enabled a wide range of applications while introducing significant security and privacy challenges. Due to the dynamic, heterogeneous, and resource-constrained nature of IoT devices, traditional security solutions are often unsuitable. Trust Management Schemes (TMSs) have emerged as viable alternatives; however, many existing approaches rely on device-level implementation, leading to scalability issues, increased overhead, and certification concerns. Most existing schemes utilize simple threshold-based mechanisms for attack detection, which are ineffective in detecting sophisticated or adaptive threats. Moreover, they lack mechanisms to analyze established trust to assess device-specific security and performance risks. To address these limitations, this thesis proposes a novel trust management framework deployed at the IoT access layer (e.g., gateways) rather than on IoT devices. Trust is established through device–gateway interactions, eliminating the need for device modification while reducing overhead and improving scalability. The framework evaluates trust using communication, security, and advanced attributes. Then, a machine-learning-based trust analyzer is deployed to detect malicious behavior and assess devices’ security posture and reliability based on their established trust. Building on the TMS, a Trust-Aware Resource Allocation (TARA) algorithm is proposed to integrate trust and security metrics into task allocation. TARA ensures that only capable and trustworthy devices are selected, improving efficiency, user satisfaction, and network resilience. The key contributions are: (1) a robust interaction-based TMS that requires no modification of the IoT device, (2) a trust analyzer that detects malicious devices and evaluates security and reliability risks, and (3) the TARA algorithm for security-aware and trust-driven task allocation. Extensive simulations and hardware validation demonstrate that the proposed TMS significantly improves trust accuracy, attack detection, and system robustness, while TARA achieves better user satisfaction and system efficiency.

Name thesis:degree_name

Doctor of Philosophy (Ph.D.)

Level thesis:degree_level

Doctoral

Discipline thesis:degree_discipline

Engineering, Electrical and Computer

Grantor dc:publisher

Carleton University

Year dc:date.issued

2025

Author dc:creator

• Farhat, Ali

Statement dc:rights

• Copyright © 2025 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, distribution to students, research and scholarship. Theses may only be shared by linking to the Carleton University Institutional Repository and no part may be copied without proper attribution to the author; no part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.

Language dc:language.iso

en

DOI

10.22215/etd/2025-16943

OAI identifier oai:identifier

oai:carleton.scholaris.ca:20.500.14718/45100