Fuzzy logic trust-based fog node selection

Abstract

Fog node selection is a crucial element in the development of a fog computing system. It forms the foundation for other techniques such as resource allocation, task delegation, load balancing, and service placement. Fog consumers have the task of choosing the most suitable and reliable fog node(s) from the available options, based on specific criteria. The study presents the Fog Node Selection Engine (FNSE) as an intelligent and reliable fog node selection framework to select appropriate and reliable fog nodes in a trustworthy manner. The FNSE predicts the trust value of fog nodes to help the fog consumer select a reliable fog node based on its trust value. We propose three AI-driven models within the FNSE framework: FNSE based on fuzzy logic (FL), FNSE based on logistic regression (LR), and FNSE based on a deep neural network (DNN). We implement these three models separately using MATLAB for FL and Python for LR and DNN. The performance of the proposed models is compared based on the performance metrics of accuracy, precision, recall, F1 score and execution time. The experiment results show that the FL-based FNSE approach achieves the best performance with the highest accuracy, precision, recall, and F1 score values. The FL-based FNSE approach also consumes less time and can make predictions quickly. The FNSE framework based on FL improves the overall performance of the selection process of fog nodes.