Enhance Information Derivation by In-Network Semantic Mashup for IoT Applications
Lijun Dong, Richard Z. Li · 2018
In Internet of Things (IoT) applications, the users usually are not interested in the raw data that are produced by the physical devices. Through semantic mashup, the new information can be derived, which can be meaningful to the users. In this paper, we identify that the in-network capabilities of Internet routers can overcome the inefficiency in the data request and semantic mashup that is initiated either by the user directly or through an IoT middleware (e.g. oneM2M platform). We propose the high level architecture of the in-network semantic mashup function of an Internet router. The algorithm of processing semantic mashup integrated data request message is proposed as well as the data processing procedure. The simulation results show the significant performance improvements in the transported data overhead and the user experienced latency.