Context-Based Adaptive Fog Computing Trust Solution for Time-Critical Smart Healthcare Systems

Fog's inherent decentralized nature and ability to process data in transit, i.e., the ability to draw conclusions in real-time, are quite suitable for scenarios where an enormous number of decentralized devices need to communicate and 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 provides the dependability that time-critical smart healthcare systems need. Because of the critical nature of healthcare data, better security and privacy solutions for fog computing are required, with trust being of the utmost importance. Context-dependent trust solution for fogs is still an open research area, so the aim of this research is to propose a context-based adaptive trust solution for smart healthcare environments using a Bayesian approach and similarity measures. The 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 the minimization of trust bias as opposed to static weighting. Context-based similarity calculations filter out recommender nodes with malicious intent using server, social contact, and service similarity. This model is efficient and has a low-trust computation overhead because it has a linear complexity of O(n).