000			01838nam a22001577a 4500
082			_a005.1,NAW
100			_aNawaz, Alishba _9113005
245			_aAdaptive Trust Calculation in Fog Computing / _cAlishba Nawaz
264			_aRawalpindi _bMCS, NUST _c2023
300			_ax, 52 p
505			_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.
650			_aMSCSE / MSSE-27 _9112568
690			_aMSCSE / MSSE _9112573
700			_aSupervisor Dr. Mian Muhammad Waseem Iqbal _9113006
942			_2ddc _cTHE
999			_c594916 _d594916