## 5 Performance Evaluation of EMOR
### Simulation Environment  
In order to validate the effectiveness of EMOR in NAN, we evaluate the performance of EMOR in MATLAB 2018 simulator [56] and the parameters used in this simulation are defined in Table 3 as: \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Network Size & \(500\times 500\) m\({}^{2}\) \\ Number of the Mobile Sink & 1 \\ Number of Sensors & 200 \\ Mobility Pattern & Randomly \\ Duration for a Data Collection Period & 600 s \\ Communication Range for Sensor Nodes & 20 m \\ (Data + Overhead) Packet Size & 1024 bits \\ Probe Message Size & 120 bits \\ Transmit Power & 15 mW \\ Receive Power & 13 mW \\ Medium Access Control (MAC) Layer & IEEE 802.11 \\ Max Buffer size & 512 K-bytes \\ Target Reliability in Probabilistic and Opportunistic Flooding & 0.6 \\ Algorithm (POFA) & 2.5 J \\ Initial Energy of Nodes & 1024 bits \\ Buffer Threshold & 1024 bits \\ \(E_{dac}\) & \(20\times 10^{-7}\) J/bit \\ \(\varepsilon_{amp}\) & \(10\times 10^{-9}\) J/bit/m\({}^{2}\) \\ Weights (\(\alpha\), \(\beta\), \(\gamma\) and \(\sigma\)) respectively & 0.4, 0.3, 0.1, 0.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Simulation parameters. Figure 6: Multi-disjoint path selection and routing strategy for real-time and non-real-time traffic. We conducted a set of simulation experiments for EMOR protocol and performed a comparative analysis of EMOR with several other protocols used in sensor networks. Our simulation environment includes a network field of 500 m \(\times\) 500 m in which 200 sensors were deployed randomly. We have assumed that all sensor nodes are independent and identical with a communication range of 20 m. We have fixed the duration of the data collection period in our experiments to check the impact of several other parameters on EMOR's performance and to compare that performance with various algorithms used in sensor networks. The classical data transmission algorithms like Extremely Opportunistic Routing (ExOR) [19] improves the reliability of data forwarding by exploiting the broadcasting nature of wireless medium in such a way that allows multiple neighboring nodes to overhear the ongoing transmission and participate in data forwarding, whereas probabilistic and opportunistic flooding algorithm (POFA) [57] achieves the target reliability on each hop of data forwarding by adopting the controlled transmissions but at the cost of higher energy consumption and increased number of hopes. [PERSON] et al. in [58] presented reliable proliferation routing with low duty cycle (RPRDC) by integration of concepts like randomized dispersity, forwarding, and reliable path finder to improve the packet delivery ratio. [PERSON] et al. in [22] presented a data collection model based on opportunistic node connection (DCBONC) in wireless sensor networks. DCBONC calculates the optimal path from the mobile sink to every sensor node in the graph by formulation of a spanning tree. DCBONC also utilizes a routing protocol which adapts to different sensor nodes statutes to improve the reliability of data transmission. DCBONC depicts improvement in performance in parameters like packet delivery ratio (PDR), energy consumption, and network lifetime. Foregoing in view, most of the classical algorithms used in sensor networks, consider unreliable wireless links due to lack of link quality parameter. Most of the algorithms do not include multipath links for data forwarding except ExOR and POFA. In addition to it, no sensor network algorithm developed so far has contributed towards SGs NAN while focusing on real-time and non-real-time traffic requirements and efficiently managing the energy resources.