# Intelligent Video Surveillance Platform for Wireless Multimedia Sensor Networks
## 5 Experimental Results  
We have deployed our platform at Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences for development and testing purposes. The algorithms are implemented in C. We must emphasize that every experiment reported in this paper is based on actual implementation running on a real test-bed. Experimental setup consists of 7 raspberry pi sensor nodes. All multimedia data can be transferred from a source node to sink node through multi-hop communication. In each raspberry pi, we stored a stream of pre-recorded video that was recorded in cubic environment. Figure 7 shows the network topology analyzed in this paper. All the nodes are statically deployed in the deployment area. Node 7 is connected to the PC computer, in which the collected data of each node are visualized. Since the distance between nodes is short, we assign each node an IP address. We establish the network using IP address. For example, we write the destination address of node 1 as IP address of node 2, the destination address of node 2 as IP address of node 3, and so on. In the experiments, the following metrics are compared: buffer occupancy, I-frame packet loss, and P-frame packet loss. The parameters used in our experiments are shown in Table 1. The following three schemes are implemented:  
1. IVSP: It is the scheme proposed in this paper. 2. ECODA [33]: Enhanced congestion detection and avoidance for multiple class of traffic in sensor networks
3. No congestion control: This is the baseline and no congestion control scheme is used. In this scheme, packets are dropped by congested sensors, and no further action is taken. We call this scheme as NoIVSP. Figure 8 presents the status of the buffer occupancy at different data rates for the NoIVSP. Figure 8a shows that just node 7 gets congested and remaining nodes do not experience any congestion at data \begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline Number of video sensor nodes & 7 \\ Number of sink nodes & 1 \\ Frames per second (fps) & 15 \\ Number of retransmissions for I frame packets & 3 \\ Number of retransmissions for P frame packets & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters used in our experiments. Figure 7: Routing topology used in IVSP. rate of 0.1 Mbps. Increment in the data rate at up to 0.5 Mbps involves two other nodes in congestion, which can be seen in Figure 8b. However, nodes 1 to 4 do not experience any congestion so far. Similarly, Figure 8c shows that node 4 gets congested by further increase in the data rate. Results in Figure 8d indicate that at the data rate of 2 Mbps, congestion happens at every node except node 1; it does not suffer any congestion, because data is only generated at node 1 and it does not relay any traffic. Therefore, probability of congestion is very minimal at node 1. Figure 9a,b represents the progress made with IVSP. In Figure 9a,b, the \(y\)-axis is buffer occupancy, and the \(x\)-axis is time in minutes. We can observe that IVSP efficiently adopts the buffer occupancy of sensor nodes according to network condition. Therefore, IVSP achieves superior congestion-free rate. The buffer occupancy over-shoots first and then falls down. The reason is that the network was not congestedwhen sensor nodes started sending the packets at the beginning. Figure 8 shows that in the case of NoIVSP, the buffer begins to overflow immediately, because it has no congestion control mechanism. Whereas, whenever IVSP determines the network is congested, it applies the rate decrease step we have described, computes a new rate allocation, and sends the new rate to its child node. Figure 9a,b also represents the variation of \(\beta\) at different values. When \(\beta\) is small, we have less incoming data, which shows that congestion probability is reduced. Therefore, buffer occupancy shuffles between normal state, slow state, and urgent state. Whereas, when \(\beta\) is large, incoming data becomes very high, which shows that there is more probability of congestion.