QS-Trust: An IoT ecosystem security model incorporating quality of service and social factors for trust assessment

Abstract

In the rapidly growing and increasingly complex Internet of Things (IoT) ecosystem, securing communication and data exchanges between devices is a major concern. To address this, we proposed QS-Trust, a trust-based security model considering both Quality of Service (QoS) and social parameters. QS-Trust uses a trust value to determine the trust level between devices and employs a QoS-aware trust-based algorithm to improve the security of data transmissions. Additionally, the model incorporates intelligence parameters such as computing power, memory capacity, device behavior and context information to enhance the accuracy of trust evaluation. Our simulation results demonstrated that QS-Trust effectively improved the security of the IoT ecosystem while maintaining the high level of QoS. The execution time of QS-Trust was in the range of 21 to 128 milliseconds, which is efficient for real-time IoT applications. QS-Trust offers a promising solution for securing the IoT ecosystem. The QS-Trust model effectively addresses the challenges of maintaining accurate and up-to-date trust levels in dynamic IoT environments through its decentralized approach, multi-factor evaluations, and adaptive algorithms. By continuously monitoring device performance and interactions and dynamically adjusting trust scores, QS-Trust ensures that the IoT network remains secure and reliable.