01947nam a22001697a 4500082001400000100002800014245006600042264003300108300001200141505138600153650002901539690002601568700005401594942001301648999001901661952009701680  a005.1,NAW  aNawaz, Alishba 9113005  aAdaptive Trust Calculation in Fog Computing  /cAlishba Nawaz  aRawalpindibMCS, NUST c2023  ax, 52 p  aFog is well suited for situations where a huge number of decentralized devices must communicate, provide live analysis of data, and perform storage jobs because of its inherent decentralized nature and capacity to process data in transit, i.e., ability to draw conclusions in real-time. Fog computing offers the dependability that time-sensitive smart healthcare systems require because of its ability to operate near the end user and independence from centralized architecture. Because healthcare data is so vital, there is a need for stronger security and privacy solutions for fog computing, where trust is crucial The goal of this research is to provide a context-based adaptive trust solution for the smart healthcare environment using Bayesian technique and similarity measures against bad mouthing and ballot stuffing since context dependent trust solution for fogs is still an open research topic. To assess our findings, the proposed trust model has been simulated in Contiki and Cooja. In contrast to static weighting, adaptive weights assigned to direct and indirect trust using entropy values assure the least amount of trust bias, and calculations of context similarity remove recommender nodes with malevolent intent utilizing server, coworker, and service similarity. Due to its minimal trust computation overhead and linear complexity O(n), this model is effective.  aMSCSE / MSSE-27 9112568  aMSCSE / MSSE 9112573  aSupervisor Dr. Mian Muhammad Waseem Iqbal9113006  2ddccTHE  c594916d594916  00104070aMCSbMCScTHEd2023-06-06l0o005.1,NAWpMCSTCS-546r2023-06-06w2023-06-06yTHE