A prototype node for wireless vision sensor network applications development

This paper presents a prototype vision-enabled sensor node based on a commercial vision system of reduced size and power consumption. The wireless infrastructure for the deployment of a distributed smart camera network based on these nodes is provided by commercial motes. The smart camera, based on a low-power bio-inspired processing scheme, enables in-node image processing and vision tools. This permits to elaborate a lighter representation of the scene, keeping the relevant information in terms of detected elements, features and events, alleviating the data transmission through the network. Therefore by passing only the relevant information to the neighboring sensor nodes, distributed and collaborative vision is possible with the limited data rates available in commercial wireless sensor networks. Communication between the different components of the system is supported by the available UARTs and GPIOs. Several examples of in-node image processing and feature detection has been tested in the prototype, and information at different abstraction levels has been broadcasted to the network.Junta de Andalucía 2006-TIC-2352Ministerio de Ciencia e Innovación TEC2009-1181

Towards Efficient Wireless Video Sensor Networks: A Survey of Existing Node Architectures and Proposal for A Flexi-WVSNP Design

Wireless sensor networks (WSNs) capable of capturing video at distributed video sensor nodes and transmitting the video via multiple wireless hops to sink nodes have received significan

Energy-efficient Feedback Tracking on Embedded Smart Cameras by Hardware-level Optimization

Embedded systems have limited processing power, memory and energy. When camera sensors are added to an embedded system, the problem of limited resources becomes even more pronounced. In this paper, we introduce two methodologies to increase the energy-efficiency and battery-life of an embedded smart camera by hardware-level operations when performing object detection and tracking. The CITRIC platform is employed as our embedded smart camera. First, down-sampling is performed at hardware level on the micro-controller of the image sensor rather than performing software-level down-sampling at the main microprocessor of the camera board. In addition, instead of performing object detection and tracking on whole image, we first estimate the location of the target in the next frame, form a search region around it, then crop the next frame by using the HREF and VSYNC signals at the micro-controller of the image sensor, and perform detection and tracking only in the cropped search region. Thus, the amount of data that is moved from the image sensor to the main memory at each frame is optimized. Also, we can adaptively change the size of the cropped window during tracking depending on the object size. Reducing the amount of transferred data, better use of the memory resources, and delegating image down-sampling and cropping tasks to the micro-controller on the image sensor, result in significant decrease in energy consumption and increase in battery-life. Experimental results show that hardware-level down-sampling and cropping, and performing detection and tracking in cropped regions provide 41.24% decrease in energy consumption, and 107.2% increase in battery-life. Compared to performing software-level down-sampling and processing whole frames, proposed methodology provides an additional 8 hours of continuous processing on 4 AA batteries, increasing the lifetime of the camera to 15.5 hours

A Survey of multimedia streaming in wireless sensor networks: progress, issues and design challenges

Advancements in Complementary Metal Oxide Semiconductor (CMOS) technology have enabled Wireless Sensor Networks (WSN) to gather, process and transport multimedia (MM) data as well and not just limited to handling ordinary scalar data anymore. This new generation of WSN type is called Wireless Multimedia Sensor Networks (WMSNs). Better and yet relatively cheaper sensors that are able to sense both scalar data and multimedia data with more advanced functionalities such as being able to handle rather intense computations easily have sprung up. In this paper, the applications, architectures, challenges and issues faced in the design of WMSNs are explored. Security and privacy issues, over all requirements, proposed and implemented solutions so far, some of the successful achievements and other related works in the field are also highlighted. Open research areas are pointed out and a few solution suggestions to the still persistent problems are made, which, to the best of my knowledge, so far have not been explored yet

ENERGY-EFFICIENT LIGHTWEIGHT ALGORITHMS FOR EMBEDDED SMART CAMERAS: DESIGN, IMPLEMENTATION AND PERFORMANCE ANALYSIS

An embedded smart camera is a stand-alone unit that not only captures images, but also includes a processor, memory and communication interface. Battery-powered, embedded smart cameras introduce many additional challenges since they have very limited resources, such as energy, processing power and memory. When camera sensors are added to an embedded system, the problem of limited resources becomes even more pronounced. Hence, computer vision algorithms running on these camera boards should be light-weight and efficient. This thesis is about designing and developing computer vision algorithms, which are aware and successfully overcome the limitations of embedded platforms (in terms of power consumption and memory usage). Particularly, we are interested in object detection and tracking methodologies and the impact of them on the performance and battery life of the CITRIC camera (embedded smart camera employed in this research). This thesis aims to prolong the life time of the Embedded Smart platform, without affecting the reliability of the system during surveillance tasks. Therefore, the reader is walked through the whole designing process, from the development and simulation, followed by the implementation and optimization, to the testing and performance analysis. The work presented in this thesis carries out not only software optimization, but also hardware-level operations during the stages of object detection and tracking. The performance of the algorithms introduced in this thesis are comparable to state-of-the-art object detection and tracking methods, such as Mixture of Gaussians, Eigen segmentation, color and coordinate tracking. Unlike the traditional methods, the newly-designed algorithms present notable reduction of the memory requirements, as well as the reduction of memory accesses per pixel. To accomplish the proposed goals, this work attempts to interconnect different levels of the embedded system architecture to make the platform more efficient in terms of energy and resource savings. Thus, the algorithms proposed are optimized at the API, middleware, and hardware levels to access the pixel information of the CMOS sensor directly. Only the required pixels are acquired in order to reduce the unnecessary communications overhead. Experimental results show that when exploiting the architecture capabilities of an embedded platform, 41.24% decrease in energy consumption, and 107.2% increase in battery-life can be accomplished. Compared to traditional object detection and tracking methods, the proposed work provides an additional 8 hours of continuous processing on 4 AA batteries, increasing the lifetime of the camera to 15.5 hours