A Novel Content Popularity Prediction Algorithm Based on Auto Regressive Model in Information-Centric IoT

Abstract

As information-centric networking (ICN) cache can effectively reduce the requests from customers to producers and improve the efficiency of content acquisition, there are many studies propose to improve system performance of the Internet of Things (IoT) by using the concept of the ICN. In the context of information-centric IoT, the addressing location based on content names and routing transport mechanisms, which presents a high demand for the statistics and prediction of the content popularity. To improve the accuracy of the content popularity prediction, in this paper, we demonstrate a particular analysis of the content popularity and propose a content popularity prediction algorithm based on auto-regressive (AR) model. The algorithm derives regression parameters based on least-squares estimates and predicts future trends of the content popularity through combining various known values in a certain period. The evaluation results show that the proposed algorithm can accurately predict the content popularity of the next time period in information-centric IoT. As a result, the algorithm can increase the cache hit rate in routers, and reduce the network traffic and service access delay effectively to improve the experience of users in various scenarios such as real-time streaming media services.