Conceptual experiment of geolocation-aware IoT data dissemination model

Abstract

IoT devices are increasingly distributed around the world and have been digitizing physical things so far. However, a current IoT service is designed to be independent; it is not intended to conduct data collaboration with other IoT services. It must be important to be able to commonly use IoT data even from IoT devices managed for different services. To promote IoT data collaboration, we proposed a cross-domain data fusion platform on the basis of geographical proximity, called geocentric information platform (GCIP). Although GCIP enabled to collect and process data generated associated with geolocation of data generation, how to disseminate them should be still discussed. We can easily assume that some data summarization is preferable when data are used far away from the location of data generation. We also assume that there must be privacy and security issues in IoT data. That is why we propose a geolocation-aware IoT data dissemination model, which summarizes data due to the different distance between the location of the data source and the location of the destination. That is, as the distance becomes longer, the data is coarser. In this paper, we particularly focus on the behavior of the model and show the conceptual behavior by conducting experiments in a real environment.