In the last few years, we have witnessed the cache localization as one of the most challenging problems for device-to-device (D2D) communication in the IoT environment. It has been a major performance bottleneck due to cache localization problem in D2D communication as there are advancements in cellular technology, especially in 5G base stations (BSs) deployment around the globe. It is due to the fact that with an increase in the enormous amount of the number of users and devices, there has been an increase in the demands of service availability within a fraction of seconds by the end users. It results in an increase in burden on the existing network infrastructure with respect to Quality of Service (QoS) and Quality of Experience (QoE) provisions to the end users and service providers. However, caching the most popular content on the user equipments (UE's) can resolve the aforementioned problems. Motivated from these facts, in this article, we propose a model to address the problem of the cache localization decision making. In the proposed scheme, first, we collected the data set traces to predict the cache locations. Then, we predicted the locations where the user can cache the most accessed content using machine learning classification models. The classification models used in the proposed solution are decision tree and random forest. The metrics used for evaluation of the results obtained are access delay and energy consumption of the UEs. On comparing the proposal with the other existing state-of-the-art models, we observed that the random forest model yields higher accuracy as compared to other existing models. Also, we have observed that the access delay is maximum at the user's end when contents are shared with the gateway.
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