# An Energy Balance Clustering Routing Protocol for Intra-Body Wireless Nanosensor Networks
## 3 Clustering Routing Framework
### Clustering and Cluster Head Updating
Therefore, by comparing the EBCR with the

\[S_{k}=w(m)\frac{E_{res\_k}}{E_{max}}+\left[1-w(m)\times\frac{d_{\sum}\ -d(v_{k},h(v_{k}))}{d_{\sum}}\right] \tag{14}\]3 EBCR routing process. SFR protocol, the performance of the network is simulated as the network load increases (the data packet generation interval becomes smaller). The simulation scene is a circular two-dimensional plane, and the nano-nodes are randomly distributed in the area. First, investigate the energy changes of the network as the simulation time advances. Then the performance variation of the iWNSNs is investigated when the distance between the nanonode and the control node changes. ### Simulation Scenario

In this paper, when modeling a wireless nanosensor network, it is assumed that nanosensor nodes are placed in the hands to construct iWNSNs. The system architecture is shown in \[S_{k}=w(m)\frac{E_{res\_k}}{E_{max}}+\left[1-w(m)\times\frac{d_{\sum}\ -d(v_{k},h(v_{k}))}{d_{\sum}}\right] \tag{14}\]4

The nanointerface device is placed on the back of the hand, and the nano-nodes are implanted into the skin to monitor certain skin indicators. The biological composition of the back of the hand can be modeled as a layered structure, the upper layer consisting of skin, divided into dermis and epidermis. It is demonstrated in [25] that these two human tissues have very similar electrical properties, so in this paper, both are treated as a single tissue. The middle layer represents subcutaneous fat, which is thinner on the back of the hand. This thinness makes the back of the hand ideal for building a body area network because the distance between the outside and the vein is quite short. As described in [26], nanodevices must be encapsulated in biocompatible capsules or artificial cells for implantation into the human body. ### Evaluation Index

In the simulation experiment, the network lifetime, average residual energy, data packet transmission success rate, control overhead and effective throughput are selected to analyze the routing protocol. The specific definitions are as follows. \[S_{k}=w(m)\frac{E_{res\_k}}{E_{max}}+\left[1-w(m)\times\frac{d_{\sum}\ -d(v_{k},h(v_{k}))}{d_{\sum}}\right] \tag{14}\]5 Model of nano-nodes in the skin. #### 4.2.1 Network lifetime

There are many different definitions of network lifetime. This paper simulates the change of the number of dead nodes in the network with the simulation time, and focuses on the time of the first dead node in the network and the time of the last dead node. Death time (DT) is defined as:

\[S_{k}=w(m)\frac{E_{res\_k}}{E_{max}}+\left[1-w(m)\times\frac{d_{\sum}\ -d(v_{k},h(v_{k}))}{d_{\sum}}\right] \tag{14}\]6

where \(DT\) represents the death time of the node in the iWNSNs. \(E_{k}(t)\) indicates the energy value of the node \(v_{k}\) at the time \(t\). \(\eta\) is the threshold value of the remaining energy. When the node energy level is below the threshold, the node can be considered dead. #### 4.2.2 Average Residual Energy

The average residual energy refers to the average remaining energy of the nodes in the network, which is calculated as follows:

\[S_{k}=w(m)\frac{E_{res\_k}}{E_{max}}+\left[1-w(m)\times\frac{d_{\sum}\ -d(v_{k},h(v_{k}))}{d_{\sum}}\right] \tag{14}\]7

where \(E_{res,k}\) represents the remaining energy of the node \(v_{k}\) at the current moment, \(n\) represents the number of nodes in the network and \(E_{max}\) represents the maximum value of the energy of the node, which takes the initial energy of the nano-node in the simulation. #### 4.2.3 Data Packet Transmission Success Rate

The data packet transmission success rate can reflect the working efficiency of the routing protocol, which represents the ratio of the total number of successfully received data packets of the destination node to the total number of data packets in the network.