A robust and trusted framework for IoT networks

In the optimistic era of the internet, connected devices have the capability to communicate and share information with each other. The implementation of the Internet of Things (IoT) is not possible until the security-related issues of managing a huge amount of data with reduced latency have been resolved. In contrast to traditional cryptographic techniques, trust establishment schemes among sensor nodes are found to be secure, reliable, and easily manageable. Therefore, in this paper, we propose a novel hybrid trust estimation approach that calculates the trust value of devices both at the device layer (Short-Term Trust) and at the edge layer (Long-Term Trust) depending upon their resource capabilities. Short-Term Trust (STT) uses the Markov model and considers only the current trust state for the evaluation of trust value whereas Long-Term Trust (LTT) uses the voluminous historical data for trust value prediction. Further, both LTT and STT are then alternatively referred to after every periodic interval leading to the evolution of the hybrid trust model. The healthcare simulation of the proposed work when compared with the available state of arts viz. ConTrust, BTEM, and Entropy gained a 17%, 10%, and 11% increase in the level of trustworthiness respectively. In addition, on average; the simulation results provide a 7% higher detection rate and 36% lower false-positive rate when compared to BTEM and Entropy trust models presented in the literature. Besides, the proposed scheme scores only 0.69% of computational overhead; which is observed to be suitable for resource constraint IoT devices. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.