## 3 Proposed Approach  
In this section, we present a description of the proposed RNM-SC approach with its operating components and system model. It offers a fault-tolerant solution with an improved delivery ratio for patients' data over the insecure wireless medium with low power consumption. Moreover, as healthcare data are very critical in terms of data accuracy and transmitted on inconsistent channels, the proposed work therefore also presents privacy and authentication to deal with network resilience under different contingencies. Although several solutions have been proposed in the recent past to overcome network vulnerability, they are based on heavy-weight cryptographic computation. Such solutions imposed additional power consumption and uneven load distribution on IoT-based networks. In this regard, the proposed approach presents a lightweight authentication process between IoT sensors and decreasing computing power using the ZigBee wireless standards. The medical sensors collect patients' information and transmit it using the ZigBee devices for wireless communication. Along with mutual authentication before data transmission, the RNM-SC approach also overcomes security deficiencies. Furthermore, it acquires the secret generation and sensitive data from cloud systems to network applications and increases network resiliency. Figure 1 illustrates the research flow of the developed components in the proposed approach. In this work, the system model comprises the following assumptions. 1. All IoT-based sensors communicate with their local coordinator. 2. The local coordinator transmits the network data towards a static base station. 3. Each IoT-based sensor has restricted transmission power and residual energy. 4. A malicious attacker may alter, drop, and re-route the monitored data over the wireless medium. The RNM-SC approach comprises two main components, and its procedural diagram is illustrated in Figure 2. First, a local nodes table is generated based on predefined distance measurement. In case no neighbors are found in the proximity of the node, then the sources node increases its communication threshold up to a certain limit. Afterward, the proposed approach utilizes the secure and authentic cryptosystem for the generation of private/public keys. Once nodes have their security keys, then they initiate the procedure of bidirectional data transfer. When any node receives an RREQ packet, they compute the heuristic value and share it with the source nodes. Upon receiving the heuristic value, the most optimal nodes are selected for data transfer while balancing load distribution  
Figure 1: The research components of the proposed network-reliable approach with security. and energy consumption. The node that belongs to the optimal route is responsible for validating the incoming data packets and performing the process of diffusion to the nodes that are within its local table. These selected nodes forward the incoming packets to their neighbors until the packets are received to the sink node. In the end, verification and validation of the users are performed on network applications, so they can interact with cloud services and obtain the network data. In the first component, each node maintains entries and constructs a node table. The node table comprises distance to the neighbor node, distance to sink node, residual energy, and unique identity \(ID\). For accomplishment, each node floods the \"Hello\" packet in its transmission radius. If no response is received, then the node increases its transmission range by an amount of \(\sigma\) distance. If the same node falls in the multiple transmission ranges, then the least distance is considered. After forming the node tables, they are arranged in its undirected graph \(G(N,\,\epsilon)\), which comprises neighbors and edges. Moreover, the constructed graph is organized into different levels. The nodes with the same distance value from the root node are considered in a single level \(L_{i}\).