Dynamic Trust Computational Model with Secure Data Transmission in IoT and WSN

As the integration of the Internet of Things (IoT) and Wireless Sensor Networks (WSN) continues to grow and its security and trust becomes a major issue. Due to transmission of sensitive between various networks, it is necessary to provide higher security such networks as well as nodes. Direct trust and indirect trust calculation models which are successful in a comparatively smaller network, but cannot deliver the requirements of adaptability of trust level in each instant for this smart environment such as IoT and complex WSN. Therefore, the aim of this paper is to design and develop a dynamic trust computational model to enhance data security in IoT and WSN. The framework describes the calculation of trust values assigned to different nodes depending on the values of data credibility, node’s behavior, the frequency of communicating with other nodes. It also calculated the reliability coefficients to determine the current overall trust values for the nodes. The model incorporates machine learning algorithms to monitor and identify abnormal node activities and learn from the network’s changes and identify possible security threats. In extensive experimental analysis we built IoT and WSN environments, the work showed a marked improvement over state-of-art approaches and decreased the loss of data and reduces the compromised node activity. Compared to the traditional static trust models, this proposed model ensured a more effective secure data routing with up to 99.40% accuracy in identifying the malicious nodes. In contrast to static and dynamic procedures for calculating trust, this model proposes an innovative, context-based system for estimating trust with reference to genuine-time node action. The use of the proposed framework rather than static models produces better security outcomes by enhancing work with 30% due to real-time metrics dependent on the context and adaptive threat identification.