# A Communication-Efficient Secure Routing Protocol for IoT Networks +
## 6 Performance Evaluation
### Emulation-Based Analysis under Dynamic Topology  
In practice, the routing performance is affected by topology dynamics owing to various uncertainties, including mobility and interference. To evaluate these effects, we performed numerical simulations of routing protocols assuming distributed and dynamic wireless networks. Specifically, in order to evaluate the uncertainties in communications this simulation assumes routing on distributed sensor networks in which a routing packet cannot be decoded with an outage probability \(p_{\text{out}}\).  
This simulation assumes that \(n_{\text{relay}}\) nodes are randomly deployed in a 300 square meter area, as shown in Figure 9. The source and destination are fixed at (10 m, 150 m) and (290 m, 150 m), respectively. The nodes have communication coverage \(d_{\text{max}}\) [m], and a broadcast signal can be decoded when the communication distance is below \(d_{\text{max}}\); however, it cannot be decoded with an outage probability \(p_{\text{out}}\) even within the coverage. Node locations are assumed to be fixed during the routing process because they are able to finish within 1-2 s.  
Figure 10a shows the effects of \(p_{\text{out}}\) on the average routing time. We iterated this simulation 1000 times with different node deployments and evaluated the average performance. The \(y\)-axis indicates the sum of the communication and computation times. In addition, the computation times were measured on a Raspberry Pi, as with the emulation-based  
Figure 8: Threshold of \(N\) for \(n\) at which the proposed method is superior to the compared methods in terms of the routing overhead.  
Figure 9: Node deployment example.  
analysis in Section 6.4. Furthermore, we introduced the _route known probability_, \(p_{\text{known}}\), for the relay nodes where a relay node knows the route to the destination. With the proposed protocol, a relay node receiving a route request (RREQ) can begin to return the route reply (RREP) if it knows a route to the destination. However, SAODV, ARAN, and ISDSR+ allow nodes to perform end-to-end communication at any time.  
This figure shows that when the chance of a communication outage increases the routing time, the proposed method remains robust to outage events, specifically at a high \(p_{\text{known}}\). This is because the proposed method can avoid end-to-end routing if a relay node knows a route to the destination. Figure 9(b) illustrates the average number of communication times during routing. The proposed method is able to reduce the required number of communication times.  
The effects of communication distance \(d_{\text{max}}\) on the average routing time are shown in Figure 11. We performed simulations with \(d_{\text{max}}=50,80,\text{ and }100\,\text{(in m)}\) with various \(p_{\text{known}}\). The proposed method is advantageous when \(p_{\text{down}}\) is high. In practice, \(p_{\text{known}}\) tends to be higher as the number of communications increases. In addition, the proposed method can achieve faster routing in environments where the communication distance is short; for example, the proposed protocol can improve the average routing time more than three-fold compared with conventional methods when \(d_{\text{max}}=50\) and \(p_{\text{known}}=0.90\). This is because the compared methods require a large number of hops for end-to-end communications. In contrast, the proposed method can significantly improve the routing characteristics by reducing the number of hops.