## 4 Optimized Hidden Node Detection Paradigm
### Hidden Node Detection
This procedure not only finds the visible viewpoint, but it also helps detect the non-overlapped viewpoints. Similarly, the overlapped areas are detected by applying the node boundaries. Table 3 shows the details of the notations used and their respective explanations. \begin{table}
\begin{tabular}{c c} \hline \hline
**Notations** & **Description** \\ \hline \(\theta\ell_{e}\) & Average location error \\ \(l_{e1}\) & First estimated location \\ \(l_{e2}\) & Second estimated location \\ \(l_{e1}\) & First actual location \\ \(l_{d2}\) & Second actual location \\ \(T_{sn}\) & Total number of multimedia sensor nodes \\ \(\Psi\) & Obstacles \\ \(\theta\ell_{t}\) & Average location detection time \\ \(l\) & Total locations of all multimedia sensor nodes \\ \(\mathbb{R}\) & Boundary range \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Notations and their description. Figure 4: Obstacles within the node boundary and viewpoint; the yellow area represents the viewpoints of first sensor, the blue represents the viewpoints of the second sensor, the red represents the viewpoints of the third sensor and the dark blue represents the overlapped viewpoints area of the sensors. ## 5 Simulation Setup and Experimental Results

To validate the effectiveness of our proposed optimized hidden node detection paradigm for wireless multimedia sensor networks, we performed the simulation by using network simulator-NS2. The network is constructed to cover 1200 \(\times\) 1200 square meters. The 270 multimedia enabled nodes are randomly distributed with homogenous capabilities. The initial energy of each node is set at 7 joules. The simulation aims to identify the coverage and network efficiency in the presence of hidden nodes. Furthermore, the performance of OHND is compared with other known mechanisms handling the issue of hidden nodes: Line Of Sight Method (LOSM) [22], Efficient Beam Scanning Algorithm for Hidden Node (EBSAHN) [23], Hidden Node Problem (HNP) [24], and Clustering-Based Mechanism for Detecting the Hidden Nodes (CMDHN) [25]. We designed three scenarios that include both hidden nodes and without hidden nodes. In the first scenario, each multimedia sensor node hears 20 out of 30 of the other multimedia sensor nodes. This gives a 66.666% probability of detecting the hidden nodes without wasting additional energy. In the second scenario, 10 out of 30 multimedia sensor nodes can hear other nodes that give a 33.33% probability to detect the hidden nodes. In the third scenario, all end nodes that contribute in the network can listen to each other. The distance between each multimedia sensor node is set at 40 meters. These three scenarios demonstrate expected, worst, and ideal scenarios, respectively. All nodes are completely constructed using the angle \(\theta=70^{\circ}\) and the 30 meter sensing range that is set for each multimedia sensor node. The communication capability of each sensor node is set at 50 meters. We set 10-18 obstacles in the first scenario. In the second scenario, 18-27 obstacles were set. The third scenario had no obstacle. In scenarios 1 and 2, the viewpoint of the multimedia sensor nodes was affected. The remaining simulation parameters are given in Table 4.

Based on simulation, we obtained these results:

* Multimedia Throughput Efficiency
* Successful Packet Delivery Ratio
* Network Accuracy
* Energy Consumption with Hidden Nodes