Efficient Route Analysis and Content Delivery in IoT-Fog Networks with ETLO

Efficient content delivery in fog computing networks is crucial for reducing the internet access points and long-distance cloud network traffic. Optimizing route analysis for forwarding content using cache is a difficult task because fog networks are dynamic and the need to minimize computation and source overhead. In this study, we discourse the route research problem in fog computing networks with an optimised technique called Enhanced Teaching Learning Optimisation (ETLO). The ETLO algorithm is made to reflect system conditions as accurately as possible and achieve optimal coverage while minimizing the number of iterations and resource overhead. Through extensive simulations and comparisons with existing Teaching Learning Based Optimization (TLBO) and Simulated Annealing (SA) algorithms, we demonstrate that ETLO outperforms TLBO in terms of memory overhead and network size needed to distribute material to far-off places. Moreover, ETLO exhibits better execution time, memory overhead, and scalability with respect to network size compared to SA. The analysis reveals that the moderation rate plays a critical part in determining the amount of memory and execution time, with an inverse proportional relationship. The average memory usage of ETLO is found to be 0.186% less than TLBO, while TLBO performs slightly better in execution time. Thereby, the proposed ETLO algorithm significantly enhances the efficiency of content delivery in fog computing networks, leading to an optimized and resource-efficient routing process. The findings underscore the potential of ETLO in improving network performance and content caching strategies, thereby contributing to the advancement of fog computing technologies.