Verifying trust over IoT-ad hoc network-based applications under uncertainty

Abstract

The rapid integration of the Internet of Things (IoT) with ad hoc networks offers significant advantages for revolutionizing smart environments. However, ensuring trust and reliability within these interconnected systems remains a significant challenge. To address this challenge, this paper introduces an innovative three-valued (3v) trust model customized specifically for IoT-ad hoc systems. The model employs three-valued logic to evaluate trust in social commitments within ambiguous scenarios, where commitments serve as the foundation of the business logic. Our aim is to enhance the effectiveness and economic feasibility of IoT in smart environments. To advance this initiative, we introduce the 3v-TCTLC, a distinctive modeling language that extends the conventional two-valued logic by introducing a third value to accommodate uncertainty. This novel approach facilitates the assessment of trust in commitments within uncertain IoT-ad hoc environments. Additionally, we improve the functionality of the “MACMAS-interactor” tool, incorporating new features to support our 3v-TCTLC logic. Through two case studies in the domains of smart health monitoring and smart home systems, we validate our model against specific requirements in uncertain contexts. These case studies highlight the robustness and practicality of our proposed tools and methodologies. Compared to prevalent trust management strategies employed in IoT and ad-hoc networks, our methodology stands out distinctly. While many current solutions propose trust-centered protocols, with some even harnessing advanced technologies, they frequently overlook the crucial element of model checking. Our approach not only incorporates this critical component but also ensures the integrity of the system. Furthermore, even though the field of multi-valued model checking has seen advancements like χCTL, our research contributes significantly by integrating trust and commitment verification specifically designed for IoT-ad hoc environments. Our empirical assessments in the domains of smart health and home systems confirm that our tool and strategies demonstrate superior performance in terms of time and space usage and better adaptability and scalability in uncertain scenarios, representing a noteworthy advancement over existing techniques and tools.