## 4 Visual Data Capture in WVSNs
The proposed algorithm is based on a Directed Acyclic Graph (DAG) that jointly schedules communication and computation tasks of an application with minimum energy consumption subject to delay constraints. However, in this work, the basic assumption of the task scheduling algorithm is that these tasks are independent. Such an independence assumption is valid in scalar data in WSNs, but not in visual data in WVSNs. In WVSNs, the energy consumption and visual data processing time for the tasks in the WVSN depend on the correlation among the camera sensor nodes. In Table 3, we summarize the existing works on visual data capture in WVSNs. In most of the existing research works, the camera sensor node is semi-static (i.e., cameras can only pan and tilt). However, in the near future, new Mobile, Pan, Tilt and Zoom (MPTZ) camera sensors that can move and adjust the camera on-the-fly will be available. This opens a new visual data capturing era in WVSN networks. This MPTZ camera sensor enables fewer sensors to be randomly deployed at the \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Research** & **Sensors** & **Collaboration** & **Gps** & **Distributed** & **Notes** \\
**Works** & **Hierarchy** & **Collaboration** & **Needed** & **Processing** & **Notes** \\ \hline [PERSON] _et al._ [26] & Single tier & Among camera sensors & No & Yes & \begin{tabular}{c} Visual correlation \\ entropy framework \\ 3D directional sensing \\ model \\ \end{tabular} \\ [PERSON] _et al._ [27] & Multi-tier & \begin{tabular}{c} Among camera \\ sensors \\ \end{tabular} & Yes & Yes & \begin{tabular}{c} 3D directional sensing \\ model \\ \end{tabular} \\ [PERSON] _et al._ [28] & Multi-tier & \begin{tabular}{c} Scalar and \\ camera sensors \\ \end{tabular} & No & No & \begin{tabular}{c} Supervised learning \\ needed \\ Distance-based lookup \\ table \\ \end{tabular} \\ [PERSON] _et al._ [29] & Single tier & No & Yes & No & \begin{tabular}{c} Distance-based lookup \\ table \\ Counting the activated \\ scalar sensors \\ \end{tabular} \\ [PERSON] _et al._ [30] & Two tier & \begin{tabular}{c} Scalar and \\ camera sensors \\ \end{tabular} & No & Yes & \begin{tabular}{c} Counting the activated \\ scalar sensors \\ \end{tabular} \\ [PERSON] _et al._ [31, 32, 33] & Single tier & \begin{tabular}{c} Among camera \\ sensors \\ \end{tabular} & No & Yes & \begin{tabular}{c} Estimate camera’s \\ position, orientation \\ and focal length \\ \end{tabular} \\ [PERSON] _et al._ [35] & Single tier & \begin{tabular}{c} Among camera \\ sensors \\ \end{tabular} & Yes & Yes & \begin{tabular}{c} Vision graph \\ construction \\ \end{tabular} \\ [PERSON] _et al._ [37] & Single tier & No & No & No & \
\begin{tabular}{c} Real-time camera \\ control \\ \end{tabular} \\ [PERSON] _et al._ [38] & Single tier & No & No & No &
\begin{tabular}{c} Task mapping and \\ scheduling mechanism \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Research works on visual data capture in WVSNs. initial stage and then the camera sensor can be moved and adjusted to meet the application needs. This brings up new research opportunities on visual data capturing by using MPTZ camera sensors to meet the application requirements in an energy efficient way.