With the development of Internet of things (IoT) technology and the increasing demand for building energy management, many buildings are adopting intelligent building management systems based on IoT devices. The intelligent building management system not only collect large amounts of real-time data from IoT devices, but also require data analysis to monitor building environment. This paper proposes a data processing framework for IoT-based intelligent building management (IBFRAME). The proposed framework aims to provide data collection, monitoring, processing, routing, and analytics services to support a variety of intelligent building applications. In this framework, an ingress router converts and forwards incoming data to application servers and data processors. To monitor building environment, data enrichers analyze thermal comfort and occupancy detection in each space of the building based on IoT sensor data, such as temperature, humidity, and CO2concentration. Data collectors of the framework gather different types of data from external data sources, such as weather databases and energy meter devices. This framework is used as the foundation for processing and analyzing IoT data in intelligent buildings, and designed to facilitate module extension. It supports the implementation and operation of diverse intelligent building application services. This paper also discusses the structure of the system that is built and operated based on the proposed framework. The system adopts a container-based time series database, which specializes in massive time series data processing, as a data management system.

Retrofit could be still expensive solution to existing building. However, with IoT(Internet of Things) environment sensors, it is possible to build a powerful intelligent building monitoring system with relatively low costs. The IoT environment sensor can analyze the IAQ(Indoor Air Quality), occupancy, and comfort level of the individual space by integrating temperature, humidity, carbon dioxide, sound, illuminance, organic compound, human body detection and IR sensor in the individual space of the building. In addition, because it supports Wifi mesh network, it can be easily installed without performing construction for retrofit in existing building, and real time monitoring can be performed. In this paper, we introduce the result of applied the IoT environment sensors to three buildings and visualization interface based on the result of analysis and real environment sensing

We developed a robust approach that can be synchronized with accuracy to a media streaming group in a network congestion situation. This approach consists of a server-side and client-side algorithm. The server-side algorithm is used to transmit the playback information using a synchronization control packet to clients. The synchronization control packet is transmitted in an event-driven manner. The clientside algorithm synchronizes the playback position by using the information received from the server. The detailed algorithm is used to accurately synchronize the playback position on the client. Finally, we measured the synchronization accuracy according to the network congestion. The performance results of this experiment can improve the completion time and the synchronization accuracy in network congestion.