Abstract:
Fog’s inherent decentralized nature and ability to process data in transit i.e., ability to draw conclusions at real-time is quite suitable for scenarios where an enormous number of decentralized devices need to communicate, provide live analysis of data and storage tasks. Fog computing’s ability to work close to the end user and non-reliance on centralized architecture provide the dependability that time critical smart healthcare systems need. Owing to the critical nature of healthcare data there is a need for better security and privacy solutions for fog computing of which trust is of utmost importance. Context dependent trust solution for fogs is still an open research area so aim of this research is to propose a context-based adaptive trust solution for smart-healthcare environment using Bayesian approach and similarity measures. Proposed trust model has been simulated in Contiki, Cooja and a Java based application has been developed to analyze our results. Adaptive weights assigned to direct and indirect trust using entropy values ensure minimization of trust bias as opposed to static weighting and context similarity calculations filter out recommender nodes with malicious intent using server, social contact and service similarity. This model has a low trust computation overhead and is efficient as it lies in liner complexity O(n).
