IoTCache: Toward Data-Driven Network Caching for Internet of Things

Abstract

This article proposes IoTCache, a popularity-based caching solution for Internet of Things (IoT). We first build a large popularity dataset that reflects peoples interest and request pattern for IoT data. By analyzing the popularity features of the dataset, we propose the popularity evolving model (PEM) for answering the fundamental problem that what is the popularity pattern of IoT data? Then, we design a data-driven popularity prediction method, which consists of two parts: 1) deep neural network (DNN)-based PEM for generating predicted popularity and 2) statistic-based PEM for dealing with the cold boot problem. Furthermore, we present a popularity-based evicting and prefetching algorithm to address what to cache and when to cache problems. We evaluate IoTCache on two IoT platforms which are on the basis of content delivery network (CDN) and information-centric network (ICN), respectively. The experimental results show that IoTCache can significantly increase the cache hit ratio, and decrease the IoT edge traffic and data latency.