PSSN: a novel cache placement method based on adapted Shannon entropy and simple additive weighting method in named data networking

This paper introduces a new strategy called PSSN to address the Cache Placement challenge in NDN. The paper first presents a novel clustering method based on the SAW decision-making approach and dynamically adapted Shannon weighting. Criteria such as the number of neighbors, hop count, router capacity, and CPU power are simultaneously considered for clustering and determining cluster heads. A key innovation in the proposed clustering is storing a copy of each content in each cluster to reduce duplicate content, increase content diversity, and consequently improve hit rate while reducing latency. Subsequently, for content placement, popularity of content, remaining router capacity, and hop count are analyzed concurrently using the SAW method adapted with the proposed approach. This ensures that popular content is placed closer to requesters. Throughout all stages of the method, the dynamic change in the status of content requests from users leads to a dynamic adjustment of the criteria weights. Simulation results using NDNsim demonstrate improvements in key parameters, with average enhancements of 17.8% and 9% for Hit rate and Delivery Time, respectively, as well as a 30.75% improvement in Load Balancing compared to recent methods.