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This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Visual sensor networks have emerged as an important. Consisting of a large number of low- power camera nodes. The camera nodes provide information. Using multiple cameras in the. However, the large amount.
In this. paper, we provide an overview of the current state- of- the- art in. Our goal is to provide a better understanding. Introduction Camera- based networks have been used for security monitoring and surveillance for a very long time. In these networks, surveillance cameras act as independent peers that continuously send video streams to a central processing server, where the video is analyzed by a human operator. These limitations in combination with the general understanding that constrained devices will take up a prominent role in the future Internet, raised the need for.![]() With the advances in image sensor technology, low- power image sensors have appeared in a number of products, such as cell phones, toys, computers, and robots. Furthermore, recent developments in sensor networking and distributed processing have encouraged the use of image sensors in these networks, which has resulted in a new ubiquitous paradigm—visual sensor networks. Visual sensor networks (VSNs) consist of tiny visual sensor nodes called camera nodes, which integrate the image sensor, embedded processor, and wireless transceiver. Following the trends in low- power processing, wireless networking, and distributed sensing, visual sensor networks have developed as a new technology with a number of potential applications, ranging from security to monitoring to telepresence. In a visual sensor network a large number of camera nodes form a distributed system, where the camera nodes are able to process image data locally and to extract relevant information, to collaborate with other cameras on the application- specific task, and to provide the system's user with information- rich descriptions of captured events. With current trends moving toward development of distributed processing systems and with an increasing number of devices with built- in image sensors, a question of how these devices can be used together appears . There are several specific questions that have intrigued the research community. How can the knowledge gained from wireless sensor networks be used in the development of visual sensor networks? What kind of data processing algorithms can be supported by these networks? What is the best way to manage a large number of cameras in an efficient and scalable manner? What are the most efficient camera node architectures? Inspired by the tremendous potential of visual sensor networks as well as by the current progress in this research field, we provide in this paper an overview of the current research directions, challenges, and potential applications for visual sensor networks. Several survey papers on multimedia sensor networks and visual processing can be found in the current literature. Here, we go one step further, by discussing these and other aspects of visual sensor networks in more detail and taking a multidisciplinary look at these topics. An extensive survey of wireless multimedia sensor networks is provided in . Here, we discuss similar problems, but considering visual sensor networks as distributed systems of embedded devices, highly constrained in terms of available energy, bandwidth resources and with limiting processing capabilities. Thus, we are focusing on the low power and low complexity aspects of visual sensor networks. Considering that many aspects of visual sensor networks, such as those related to the design of the networking protocol stack or data encoding techniques in the application layer have already been thoroughly discussed in . Thus, this paper complements these other survey papers and can be a valuable source of information regarding the state- of- the- art in several research directions that are vital to the success of visual sensor networks. Characteristics of Visual Sensor Networks One of the main differences between visual sensor networks and other types of sensor networks lies in the nature of how the image sensors perceive information from the environment. Most sensors provide measurements as 1. D data signals. However, image sensors are composed of a large number of photosensitive cells. One measurement of the image sensor provides a 2. D set of data points, which we see as an image. The additional dimensionality of the data set results in richer information content as well as in a higher complexity of data processing and analysis. In addition, a camera's sensing model is inherently different from the sensing model of any other type of sensor. Typically, a sensor collects data from its vicinity, as determined by its sensing range. Cameras, on the other hand, are characterized by a directional sensing model—cameras capture images of distant objects/scenes from a certain direction. The 2. D sensing range of traditional sensor nodes is, in the case of cameras, replaced by a 3. D viewing volume (called field of view, or Fo. V). Visual sensor networks are in many ways unique and more challenging compared to other types of wireless sensor networks. These unique characteristics of visual sensor networks are described next. Resource Requirements. The lifetime of battery- operated camera nodes is limited by their energy consumption, which is proportional to the energy required for sensing, processing, and transmitting the data. Given the large amount of data generated by the camera nodes, both processing and transmitting image data are quite costly in terms of energy, much more so than for other types of sensor networks. Furthermore, visual sensor networks require large bandwidth for transmitting image data. Thus both energy and bandwidth are even more constrained than in other types of wireless sensor networks. Local Processing. Local (on- board) processing of the image data reduces the total amount of data that needs to be communicated through the network. Local processing can involve simple image processing algorithms (such as background substraction for motion/object detection, and edge detection) as well as more complex image/vision processing algorithms (such as feature extraction, object classification, scene reasoning). Thus, depending on the application, the camera nodes may provide different levels of intelligence, as determined by the complexity of the processing algorithms they use . For example, low- level processing algorithms (such as frame differencing for motion detection or edge detection algorithms) can provide a camera node with the basic information about the environment, and help it decide whether it is necessary to transmit the captured image or whether it should continue processing the image at a higher level. More complex vision algorithms (such as object feature extraction, object classification, etc.) enable cameras to reason about the captured phenomena, such as to provide basic classification of the captured object. Furthermore, the cameras can collaborate by exchanging the detected object features, enabling further processing to collectively reason about the object's appearance or behavior. At this point the visual sensor network becomes a user- independent, intelligent system of distributed cameras that provides only relevant information about the monitored phenomena. Therefore, the increased complexity of vision processing algorithms results in highly intelligent camera systems that are oftentimes called smart camera networks . One specific image processing algorithm cannot achieve the same performance for different types of images—for example, an algorithm for face extraction significantly differs from algorithm for vehicle detection. However, oftentimes it is impossible to keep all the necessary image processing algorithms in the constrained memory of a camera node. One solution to this problem is to use mobile agents—a specific piece of software dispatched by the sink node to the region of interest . Mobile agents collect and aggregate the data using a specific image algorithm and send the processed data back to the sink. Furthermore, the mobile agents can migrate between the nodes in order to perform the specific task, thereby performing distributed information processing . In this way, the amount of data sent by the node, as well as the number of data flows in the network, can be significantly reduced. Real- Time Performance. Most applications of visual sensor networks require real- time data from the camera nodes, which imposes strict boundaries on maximum allowable delays of data from the sources (cameras) to the user (sink). The real- time performance of a visual sensor network is affected by the time required for image data processing and for the transmission of the processed data throughout the network. Constrained by limited energy resources and by the processing speed of embedded processors, most camera nodes have processors that support only lightweight processing algorithms. On the network side, the real- time performance of a visual sensor network is constrained by the wireless channel limitations (available bandwidth, modulation, data rate), employed wireless standard, and by the current network condition. For example, upon detection of an event, the camera nodes can suddenly inject large amounts of data in the network, which can cause data congestion and increase data delays. Different error protection schemes can affect the real- time transmission of image data through the network as well. Commonly used error protection schemes, such as automated- repeat- request (ARQ) and forward- error- correction (FEC) have been investigated in order to increase the reliability of wireless data transmissions . However, due to the tight delay constraints, methods such as ARQ are not suitable to be used in visual sensor networks. Install Windows Vista from Bootable USB Flash Memory Drive. You can even install Windows Vista with USB 2. All you need is a high speed 4. GB flash memory only to create a bootable Windows Vista on flash memory drive. It sound a bit rare to hear people install Windows Vista on Flash Memory ya. Actually, The concept is simple by using flash memory drive to act as the Windows Vista installation DVD disk only. But installing Windows Vista from a high speed USB flash drive perhaps is the easiest & fastest way to complete a Windows Vista install which nowadays every computer or laptop have a USB drive. 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