Enhanced subtraction-average-based optimizer and blockchain for security and load balancing in fog computing

Fog computing has provided answers to several issues in recent years, including the accuracy and caliber of the data gathered. Due to its inherent distributed architecture, security and load-balancing techniques present difficulties. This study proposes blockchain-based user authentication for fog computing and enhanced subtraction-average-based optimizer (ESABA)-based load balancing. Fog computing has integrated blockchain technology into its user authentication procedure. Subtraction Average Based Optimizer (SABO) and Oppositional Based Learning (OBL) functions are then combined to form the ESABA. The oppositional function in the SABO is employed to improve the initial population through their solutions. This procedure yields the best results for load balancing and user authentication. The suggested approach is put into practice in Python, and several performance metrics like waiting time, processing time, memory consumption for user verification, and authentication are assessed. The performance compared with the existing algorithm. The results demonstrate superior performance in both security and load balancing, highlighting the method’s potential for practical applications in fog computing. The proposed method takes a minimum response time of 0.5 s for twenty-five task scheduling.