Deep Multisensor Dashboard for Composition Layer of Web of Things in the Smart City

Abstract

The application of sensor networks in control and management of smart cities are increasing rapidly. The Internet of Things (IoT) platforms are becoming a major infrastructure component of the smart cities. The requirement for development of Web of Things (WoT) architectures and patterns have significant importance for optimal management and decision support in the smart city. Due to the fact that the sensor networks in even a small city produce a very large amount of data, manual interpretation of this data is not feasible. In recent years machine learning algorithms such as deep learning give the computer systems the ability to interpret and annotate this big data stream and to produce patterns which can help decision makers. This high-level interpretation is often presented in form of dashboards in the composition layers of the WoT. In this paper a deep learning based multisensor dashboard for decision support in the smart city is presented. The proposed dashboard is capable of interpretation and decision level fusion on sensor network data. Simulation results show that the proposed model is a suitable solution for making decisions in the IoT framework for the smart city.