Biblio

With the rapid development of the Internet of vehicles, there is a huge amount of multimedia data becoming a hidden trouble in the Internet of Things. Therefore, it is necessary to process and store them in real time as a way of big data curation. In this paper, a method of real-time processing and storage based on CDN in vehicle monitoring system is proposed. The MPEG-DASH standard is used to process the multimedia data by dividing them into MPD files and media segments. A real-time monitoring system of vehicle on the basis of the method introduced is designed and implemented.

Some IoT data are time-sensitive and cannot be processed in clouds, which are too far away from IoT devices. Fog computing, located as close as possible to data sources at the edge of IoT systems, deals with this problem. Some IoT data are sensitive and require privacy controls. The proposed Policy Enforcement Fog Module (PEFM), running within a single fog, operates close to data sources connected to their fog, and enforces privacy policies for all sensitive IoT data generated by these data sources. PEFM distinguishes two kinds of fog data processing. First, fog nodes process data for local IoT applications, running within the local fog. All real-time data processing must be local to satisfy real-time constraints. Second, fog nodes disseminate data to nodes beyond the local fog (including remote fogs and clouds) for remote (and non-real-time) IoT applications. PEFM has two components for these two kinds of fog data processing. First, Local Policy Enforcement Module (LPEM), performs direct privacy policy enforcement for sensitive data accessed by local IoT applications. Second, Remote Policy Enforcement Module (RPEM), sets up a mechanism for indirectly enforcing privacy policies for sensitive data sent to remote IoT applications. RPEM is based on creating and disseminating Active Data Bundles-software constructs bundling inseparably sensitive data, their privacy policies, and an execution engine able to enforce privacy policies. To prove effectiveness and efficiency of the solution, we developed a proof-of-concept scenario for a smart home IoT application. We investigate privacy threats for sensitive IoT data and show a framework for using PEFM to overcome these threats.

A high definition(HD) wide dynamic video surveillance system is designed and implemented based on Field Programmable Gate Array(FPGA). This system is composed of three subsystems, which are video capture, video wide dynamic processing and video display subsystem. The images in the video are captured directly through the camera that is configured in a pattern have long exposure in odd frames and short exposure in even frames. The video data stream is buffered in DDR2 SDRAM to obtain two adjacent frames. Later, the image data fusion is completed by fusing the long exposure image with the short exposure image (pixel by pixel). The video image display subsystem can display the image through a HDMI interface. The system is designed on the platform of Lattice ECP3-70EA FPGA, and camera is the Panasonic MN34229 sensor. The experimental result shows that this system can expand dynamic range of the HD video with 30 frames per second and a resolution equal to 1920*1080 pixels by real-time wide dynamic range (WDR) video processing, and has a high practical value.