Wireless visual sensor network (VSN) can be said to be a special class of wireless sensor network (WSN) with smart-cameras. Due to its visual sensing capability, it has become an effective tool for applications such a...Wireless visual sensor network (VSN) can be said to be a special class of wireless sensor network (WSN) with smart-cameras. Due to its visual sensing capability, it has become an effective tool for applications such as large area surveillance, environmental monitoring and objects tracking. Different from a conventional WSN, VSN typically includes relatively expensive camera sensors, enhanced flash memory and a powerful CPU. While energy consumption is dominated primarily by data transmission and reception, VSN consumes extra power onimage sensing, processing and storing operations. The well-known energy-hole problem of WSNs has a drastic impact on the lifetime of VSN, because of the additional energy consumption of a VSN. Most prior research on VSN energy issues are primarily focusedon a single device or a given specific scenario. In this paper, we propose a novel optimal two-tier deployment strategy for a large scale VSN. Our two-tier VSN architecture includes tier-1 sensing network with visual sensor nodes (VNs) and tier-2 network having only relay nodes (RNs). While sensing network mainly performs image data collection, relay network only for wards image data packets to the central sink node. We use uniform random distribution of VNs to minimize the cost of VSN and RNs are deployed following two dimensional Gaussian distribution so as to avoid energy-hole problem. Algorithms are also introduced that optimizes deployment parameters and are shown to enhance the lifetime of the VSN in a cost effective manner.展开更多
广泛密布的视频传感器可持续记录降雨信息,基于视频传感器估算高时空分辨率的雨量数据,已经成为当前最具有前景的雨量估计途径之一。然而,由于传感器设备、视频场景等的复杂多变,极易导致各个视频传感器反演的降雨数据质量参差不齐,需...广泛密布的视频传感器可持续记录降雨信息,基于视频传感器估算高时空分辨率的雨量数据,已经成为当前最具有前景的雨量估计途径之一。然而,由于传感器设备、视频场景等的复杂多变,极易导致各个视频传感器反演的降雨数据质量参差不齐,需要对其处理,保证反演数据质量。受地理学第一定律启发,以视频传感网中节点间的时空信息为约束,提出一种视频节点协同的雨量反演精度控制模型(Precision Control Model,PCM)。PCM模型通过视频节点间降雨信息互验证的方式,从降雨事件的时空一致性、态势一致性和相关性等特征出发,构建雨量反演的多粒度滤波方法,以期实现降雨事件的高精度表达。实验结果表明,在多种降雨场景中,PCM模型均可有效的提高了雨量反演的准确性与稳定性。降雨强度(Rainfall Intensity,RI)相对误差的均值在中、小雨场景降低约14.85%,大雨场景降低约19.90%;RI相对误差的标准差在中、小雨场景降低约40.87%,大雨场景降低约40.96%,可为高质量降雨数据的生产提供支持。展开更多
文摘Wireless visual sensor network (VSN) can be said to be a special class of wireless sensor network (WSN) with smart-cameras. Due to its visual sensing capability, it has become an effective tool for applications such as large area surveillance, environmental monitoring and objects tracking. Different from a conventional WSN, VSN typically includes relatively expensive camera sensors, enhanced flash memory and a powerful CPU. While energy consumption is dominated primarily by data transmission and reception, VSN consumes extra power onimage sensing, processing and storing operations. The well-known energy-hole problem of WSNs has a drastic impact on the lifetime of VSN, because of the additional energy consumption of a VSN. Most prior research on VSN energy issues are primarily focusedon a single device or a given specific scenario. In this paper, we propose a novel optimal two-tier deployment strategy for a large scale VSN. Our two-tier VSN architecture includes tier-1 sensing network with visual sensor nodes (VNs) and tier-2 network having only relay nodes (RNs). While sensing network mainly performs image data collection, relay network only for wards image data packets to the central sink node. We use uniform random distribution of VNs to minimize the cost of VSN and RNs are deployed following two dimensional Gaussian distribution so as to avoid energy-hole problem. Algorithms are also introduced that optimizes deployment parameters and are shown to enhance the lifetime of the VSN in a cost effective manner.
文摘广泛密布的视频传感器可持续记录降雨信息,基于视频传感器估算高时空分辨率的雨量数据,已经成为当前最具有前景的雨量估计途径之一。然而,由于传感器设备、视频场景等的复杂多变,极易导致各个视频传感器反演的降雨数据质量参差不齐,需要对其处理,保证反演数据质量。受地理学第一定律启发,以视频传感网中节点间的时空信息为约束,提出一种视频节点协同的雨量反演精度控制模型(Precision Control Model,PCM)。PCM模型通过视频节点间降雨信息互验证的方式,从降雨事件的时空一致性、态势一致性和相关性等特征出发,构建雨量反演的多粒度滤波方法,以期实现降雨事件的高精度表达。实验结果表明,在多种降雨场景中,PCM模型均可有效的提高了雨量反演的准确性与稳定性。降雨强度(Rainfall Intensity,RI)相对误差的均值在中、小雨场景降低约14.85%,大雨场景降低约19.90%;RI相对误差的标准差在中、小雨场景降低约40.87%,大雨场景降低约40.96%,可为高质量降雨数据的生产提供支持。
