## 5 Visual Data Processing in WVSNs
In [43], they study the interplay of these three factors (image transmission, image processing and MAC retransmission) is studied for image aggregation in WVSNs. It is concluded that image aggregation should be encouraged to decrease the total power consumption in highly redundant portions of the captured images and in a large transmission radius. When there are more than seven aggregated data source nodes, the MAC retransmission losses will dominate the other two factors so that image aggregation should not be encouraged. Novel visual data processing schemes require good quality visual data in WVSNs. Methods to determine and measure the quality of the captured visual data are important and challenging. The quality of sensor-driven multimedia information in terms of certainty, accuracy/confidence and timeliness are defined in [44]. A model is proposed to characterize these performance metrics. Then, these three performance metrics are computed dynamically to determine the quality of information based on current sensor observation, agreement/disagreement among the participating sensors, current context information and prior confidence of the sensors in performing various detection tasks. This mechanism could be used in a real-time application (e.g., security surveillance) where the ground truth is not available to verify the observation. To prolong the lifetime of WVSNs, besides energy efficient data processing schemes, energy efficient visual data transmission schemes should also be devised. In [45], they jointly consider energy efficient data processing and visual data transmission schemes. A mathematical model to capture the interplay between source rates, encoding power, routing, video quality and network lifetime is developed. The distributed algorithm is proposed to maximize the network lifetime by jointly optimizing the source rates, the encoding powers, and the routing scheme. Transmission errors are considered in [45] and error remedy techniques in both large delay applications (e.g., environmental data collection) and small delay applications (e.g., real-time traffic monitoring) are studied to quantified the impacts on the maximum network lifetime. Because of the limited hardware capability of the camera sensors in WVSNs, collaborative and cooperative visual data processing among the camera sensors is necessary to meet the application requirements for visual data quality, delay time, packet loss and power consumption. As recalled in Section 4, the captured images between the neighboring sensors could be redundant or supportive. Intelligent cooperative visual data processing schemes should ignore the redundant images and only process the supportive images. However, this is not an easy task since it involves not only the relative angle and locations of the camera sensors, but also needs to meet the requirements of the application. Existing researches shed some light on the collaborative and cooperative visual data processing techniques, but there is still a big room for improvements. In Table 4, we summarize the existing works on visual data processing in WVSNs. Most of the existing works borrow the idea from traditional multiprocessing techniques. Even though image/video processing has been studied for decades this traditional multimedia processing could not be directly applied in WVSNs because of the limited resource constraints on processing capability, battery power and wireless bandwidth. In meeting the challenges of limited resources, new novel collaborative visual data processing schemes among the sensor nodes should be developed.