Last advances in low power sensors has led to the development of wireless visual sensor networks. These networks comparing to traditional wireless sensor networks provides valuable visual information, therefor are suitable for surveillance and control applications. One of the important challenges in wireless visual sensor networks is energy consumption. Therefore, in this paper we address the problem of energy optimization in wireless visual sensor networks. The entropy of the captured images in each camera node sensor as a quality criteria is used. For realization of this goal, we introduced a new formula for expressing the entropy of captured image in each camera node. This model is a function of the distance between camera node and target and the angel between the main view line of camera and the target. Then, we introduced a new formula which expresses the relation between the image entropy and the number of bits required for displaying image pixel value. In next step, we proposed node selection algorithm based on entropy. For proposing our algorithm, first optimization of energy consumption is formulated, then the problem is solved as a convex problem. We used from CVX library in MATLAB and Log-Barrier Method for solving our problem and shown the simulation result of them. Finally, we compare our proposed algorithm with MDT benchmark algorithm, CVX and Log-Barrier algorithms. Also we compare the sensitivity to error for both algorithms.

中文翻译：

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基于新熵模型的无线视觉传感器网络能量优化

低功率传感器的最新进展导致了无线视觉传感器网络的发展。与传统的无线传感器网络相比，这些网络提供了有价值的视觉信息，因此适用于监视和控制应用。无线视觉传感器网络的重要挑战之一是能耗。因此，在本文中，我们解决了无线视觉传感器网络中的能量优化问题。使用每个相机节点传感器中捕获图像的熵作为质量标准。为了实现这个目标，我们引入了一个新的公式来表达每个相机节点中捕获图像的熵。该模型是摄像机节点与目标之间的距离以及摄像机主视线与目标之间的角度的函数。然后，我们引入了一个新公式，该公式表示图像熵与显示图像像素值所需的位数之间的关系。下一步，我们提出了基于熵的节点选择算法。为了提出我们的算法，首先制定了能源消耗的优化，然后将问题作为凸问题解决。我们使用 MATLAB 中的 CVX 库和 Log-Barrier Method 来解决我们的问题，并展示了它们的仿真结果。最后，我们将我们提出的算法与 MDT 基准算法、CVX 和对数屏障算法进行了比较。我们还比较了两种算法对错误的敏感性。首先制定能源消耗的优化，然后将问题作为凸问题求解。我们使用 MATLAB 中的 CVX 库和 Log-Barrier Method 来解决我们的问题，并展示了它们的仿真结果。最后，我们将我们提出的算法与 MDT 基准算法、CVX 和对数屏障算法进行了比较。我们还比较了两种算法对错误的敏感性。首先制定能源消耗的优化，然后将问题作为凸问题求解。我们使用 MATLAB 中的 CVX 库和 Log-Barrier Method 来解决我们的问题，并展示了它们的仿真结果。最后，我们将我们提出的算法与 MDT 基准算法、CVX 和对数屏障算法进行了比较。我们还比较了两种算法对错误的敏感性。