## 4 Performance Evaluation
### Results and Discussion  
Figure 3 depicts the tested results of the RNM-SC approach with LEACH++ and DSM in terms of network throughput. It is referred to as the transmission of successful data packets from medical sensors to sink nodes. The numerical analysis demonstrates its improved performance by 9% and 14%, respectively. It is because of exploiting the bidirectional routing method to determine the optimal forwarders and avoid direct transmission. The proposed approach most avoided utilizing the direct link due to the limited transmission range and adopted multi-hop communication, which remarkably achieves high throughput. Additionally, it incorporates transmission risk in active routes, which significantly contributes to network throughput. Based on the results, it is observed that other solutions incur additional costs in terms of data delivery performance and decrease the ratio of network throughput. Moreover, most of the other solutions overlook resource constraints, and as a result, that affects the node's lifetime, thereby resulting in reduced throughput performance. Figure 4 describes the tested results of the RNM-SC approach with other solutions in terms of energy consumption and reveals improved performance by 12% and 22%, respectively. The existing solutions consumed extra energy resources in determining the resilient paths and rapidly generated route request packets. Such approaches have seen overloaded communication paths with the existence of jamming nodes and suffer additional consumption of energy resources in timely data delivery. Furthermore, the transmission links are not identified either as risky or normal in the presence of heavy data traffic, and most of the network energy is misused to deal with packet retransmissions. \begin{table}
\begin{tabular}{c c} \hline \hline
**Attacks** & **How the Proposed Approach Avoids Them** \\ \hline Access to network & Unique ID private keys \\ Request of the key by a malicious node & Nonce and time \\ Confidentiality & Cipher using public keys \\ Data disclosure & Decipher with the linked private key \\ Specious packets delivery & Block the device \\ Storage overload & Distributed data chunks and diffusion \\ Data reliability & Data transfer by trusted intermediate nodes \\ Data integrity & Ciphered in iterative hashes \\ Complexity & Bidirectional heuristics \\ Authenticity & Digital signatures \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Security analysis of our proposed approach. Figure 3: Network throughput and jamming nodes. It is also seen that in routing the healthcare data from medical sensors to the cloud, due to ignoring nodes' trust, few nodes are appointed continually for the role of routing, and ultimately this causes data leakages. Such methods consume unnecessary energy on the part of sensors and affect network performance. The RNM-SC approach decreases the excessive change of control messages in constructing and maintaining data routes from medical sensors to the cloud, which greatly stabilizes the nodes' energy consumption. Figure 5 clarifies the tested results of the RNM-SC approach with an existing solution in terms of packet loss ratio. Based on the analysis, it is observed that the RNM-SC model decreases the fraction of packet loss by 30% and 43%, respectively. This improvement is due to the incorporation of nodes' trust and assessment of link risk during the computation of heuristic function. Such incorporation not only strengthened the routes but also offered a robust system for handling the malicious nodes in dropping the actual data packets. Moreover, due to the utilization of bidirectional routing heuristics, the proposed approach decreased the load distribution on the entire route and significantly increased the performance of the packet. Due to the lightweight computing resources, the RNM-SC approach consumes the least energy resources and increases the lifetime of data transmission, which decreases route breakages and packet loss rates.