基于WAVE(Wireless Access in the Vehicular Environment)协议的车联网可以通过车载终端实现车辆之间自动组网通信,解决了车辆行驶中信道切换和链路连接速率的问题.本文在分析了WAVE协议架构和相关技术的基础上,以802.11p射频模块作为...基于WAVE(Wireless Access in the Vehicular Environment)协议的车联网可以通过车载终端实现车辆之间自动组网通信,解决了车辆行驶中信道切换和链路连接速率的问题.本文在分析了WAVE协议架构和相关技术的基础上,以802.11p射频模块作为空中接口,提出一套基于该协议的硬件和软件解决方案.本方案采用三星公司的Exynos4412处理器作为主控器,USB摄像头作为视频采集模块,U-Blox7模块作为定位模块,可以实现文本、语音、视频的传输和GPS定位等功能.展开更多
Localizing a jammer in an indoor environment in wireless sensor networks becomes a significant research problem due to the ease of blocking the communication between legitimate nodes. An adversary may emit radio frequ...Localizing a jammer in an indoor environment in wireless sensor networks becomes a significant research problem due to the ease of blocking the communication between legitimate nodes. An adversary may emit radio frequency to prevent the transmission between nodes. In this paper, we propose detecting the position of the jammer indoor by using the received signal strength and Kalman filter (KF) to reduce the noise due to the multipath signal caused by obstacles in the indoor environment. We compare our work to the Linear Prediction Algorithm (LP) and Centroid Localization Algorithm (CL). We observed that the Kalman filter has better results when estimating the distance compared to other algorithms.展开更多
文摘基于WAVE(Wireless Access in the Vehicular Environment)协议的车联网可以通过车载终端实现车辆之间自动组网通信,解决了车辆行驶中信道切换和链路连接速率的问题.本文在分析了WAVE协议架构和相关技术的基础上,以802.11p射频模块作为空中接口,提出一套基于该协议的硬件和软件解决方案.本方案采用三星公司的Exynos4412处理器作为主控器,USB摄像头作为视频采集模块,U-Blox7模块作为定位模块,可以实现文本、语音、视频的传输和GPS定位等功能.
文摘Localizing a jammer in an indoor environment in wireless sensor networks becomes a significant research problem due to the ease of blocking the communication between legitimate nodes. An adversary may emit radio frequency to prevent the transmission between nodes. In this paper, we propose detecting the position of the jammer indoor by using the received signal strength and Kalman filter (KF) to reduce the noise due to the multipath signal caused by obstacles in the indoor environment. We compare our work to the Linear Prediction Algorithm (LP) and Centroid Localization Algorithm (CL). We observed that the Kalman filter has better results when estimating the distance compared to other algorithms.
