## 4 Optimized Hidden Node Detection Paradigm
### Hidden Node Detection
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Size of network & 1200 \(\times\) 1200 square meters \\ Number of multimedia sensor nodes & 270 \\ Queue-capacity & 50 packets \\ free-spacepropagation & 47 meters \\ Maximum number of retransmissions allowed & 03 \\ Initial energy of node & 7 Joules \\ MAC protocol & BN- MAC [6] \\ Size of packets & 512 bytes \\ Data rate & 450 kb/second \\ Sensing range of node & 30 meters \\ Communication range & 50 meters \\ Transmitter power & 12.2 mW \\ Receiver power & 13.4 mW \\ Buffer threshold & 1024 Bytes \\ Sensing range & 30 meters \\ Number of obstacles & 10–18 for Expected and 18–27 for worst scenario \\ Simulation time & 18 min \\ Average simulation run & 10 \\ Frame rate & 40 frame/second \\ Reliability & [0.78, 0.92] \\ Average reporting rate & 4 packet/second \\ Base station location & (0,700) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Simulation parameters and its corresponding values. ### Multimedia Throughput Efficiency

To confirm the multimedia throughput-efficiency of our proposed optimized hidden node detection, we created three scenarios: the worst case, the expected case, and the ideal case. In the worst case scenario, we used 18-27 maximum obstacles depicted in Figure 5a. In the expected case, we used 10-18 obstacles as depicted in Figure 5b, and there were no obstacles in the ideal case depicted in Figure 5c. Furthermore, we compared the performance of OHND with LOSM, EBSAHN, HNP, and CMDHN. The communication time is set at 18 min in the three scenarios for all competing approaches. We observed in Figure 5a-c that when the number of obstacles increases, the throughput efficiency decreases. However, throughput efficiency of our proposed OHND is better than other competing approaches. Our results demonstrated that the approach of the OHND significantly improved the throughput efficiency of the multimedia sensor network. The reason for improvement in throughput efficiency, in our case, is the use of the obstacle-driven negative effect strategy method that handles the negative effect of the obstacles. Our approach is particularly suitable for multimedia sensor networks that suffer due to several disconnected subdivisions of the network that are experiencing obstacles among the subdivisions. Furthermore, the multiple discovery of nodes rather than single discovery of node substantially improved the performance. Figure 5: \(Cont.\)

### Successful Packet Delivery Ratio

One of the drawbacks of the hidden node in WMSNs is to drop the packets due to collisions. As a result, the quality of service is highly affected. Successful packet delivery ratio can be shown in Equation (15):

\[\mathrm{P_{s}}=\frac{\mathrm{P_{d}}\times 100}{\mathrm{P_{g}}} \tag{15}\]

where \(\mathrm{P_{s}}\): Ratio of successful packets, \(\mathrm{P_{g}}\): Number of generated packets, and \(\mathrm{P_{d}}\): delivered packets. Figure 6 demonstrates the results of our proposed OHND and its comparison with LOSM, EBSAHN, HNP, and CMDHN approaches. The performance is measured by using a various number of events. When the number of events increase, the, successful packet delivery ratio starts reducing. However, our approach is more stable when compared with other competing approaches. Our approach has 93.4% of success rate after detecting 18 events, but other approaches have 82.24%-89.2% after monitoring the same number of events. In other approaches, the lost packets cannot be transmitted. In the presence of the hidden nodes, packets are lost. As a result, there is a possibility of collision that reduces the packet delivery ratio. Figure 5: (**a**) multimedia converge in existence of 27 obstacles; (**b**) multimedia converge in existence of 18 obstacles; (**c**) multimedia coverage efficiency without the existence of obstacles. Figure 6: Successful packet delivery vs. number of monitoring events. ### Network Accuarasy

Network accuracy is highly affected due to the presence of hidden nodes.