## 6 Visual Data Transmission in WVSNs  
Visual data transmission in WVSNs is very expensive due to its requirements of large amounts of bandwidth and battery power. Hence, as compared to the routing issues in WSNs, we have more stringent battery and bandwidth constraints in WVSNs. In [46] a routing scheme for the image sensors to route the captured image data to the base station is proposed. The routing decision is based on the three parameters (relative position to the base station, queue length and residual power) of next hop neighboring relay sensors. They study the tradeoff between these three parameters to optimize the system throughput and prolonging the network lifetime. Reference [47] proposes delay constrained routing in WVSNs. The basic idea is to route the highly relevant visual data through faster paths with lower end-to-end delay. The discussion on reactive opportunistic routing scheme to balance between power consumption and delay in [48] is a similar idea. The basic idea is to choose the next relay node based on the location information and packet relevance information. High relevance packets choose the shorter path to the sink node and low relevance packets use the leftover paths that have enough battery power. These three works assume that the relevance of the captured visual data is fixed or given. Even though this assumption could simplify the problem complexity, it is not applicable to real-time WVSN applications since the relevance of visual data changes from time to time based on the camera's FoV and the application needs. In other words, the routing decisions should also consider the camera's FoV and the application needs. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Research** & **Processing** & **Visual Quality** & **Power** & **Distributed** & **Notes** \\
**Works** & **Technique** & **Aware** & **Aware** & **Processing** & \\ \hline [PERSON] _et al_. [40] & Video aggregation & No & No & Yes & Bandwidth efficient \\ [PERSON] _et al_. [42] & Compression & No & Yes & Yes & \begin{tabular}{c} Correlation processing and \\ coding via clustering \\ \end{tabular} \\ [PERSON] _et al_. [44] & Visual data model & Yes & No & Yes & \begin{tabular}{c} Data quality determined by \\ sensors collaboratively \\ \end{tabular} \\ [PERSON] _et al_. [41] & Image aggregation & No & No & No & \begin{tabular}{c} S-JPEG to reduce the \\ redundant block size \\ \end{tabular} \\ [PERSON] _et al_. [45] & Optimizing source & & & & \begin{tabular}{c} Processing and routing \\ schemes to prolong network \\ lifetime \\ \end{tabular} \\ [PERSON] _et al_. [39] & Multimedia fusing & No & Yes & Yes & \
\begin{tabular}{c} Fusing the different formats \\ of multimedia \\ \end{tabular} \\ [PERSON] _et al_. [43] & Image aggregation & No & Yes & Yes &
\begin{tabular}{c} Energy efficient aggregation \\ schemes \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Research works on data processing in WVSNs. Some of the works consider the data capture and transmission simultaneously. In [49], they propose a distributed visual data capture and routing algorithm for WVSNs subject to energy constraints. The basic idea of the algorithm is to allocate the energy in data capture (sampling) and routing based on exchanging the collected visual data and residual power information with neighboring sensor nodes. They compare two routing schemes (fixed routing and flexible routing) under the same power budget constraint. They found that as compared to the fixed routing scheme, flexible routing could deliver twice the amount of visual data back to the base station but it comes at about 100 times the communication and computation cost. They conclude that a flexible routing energy allocation scheme is applicable only in small networks. In [50], they study the inter-node differential coding and routing issues simultaneously to reduce the captured overlapped FoV data transmitted on the WVSN.