# Receiver-Initiated Handshaking MAC Based on Traffic Estimation for Underwater Sensor Networks +
## 8 Simulation and Analysis
### Performance Evaluation  
We first evaluated the energy efficiency performance of the TERI-MAC system, which is controlled by the parameter, \(E_{th}\), which represents the target energy efficiency in the Algorithm 2. Be aware of the traffic models of all neighbor senders, the receiver that initiated the handshaking communication was able to adjust the frequency of data polling to reach the required energy efficiency. The traffic estimation enabled TERI-MAC to adapt to any traffic pattern. If the traffic load was relatively low, the waiting time increased to allow more packets to be accumulated at the senders. For high traffic loads, the receiver would request the data more frequently after achieving the desired energy efficiency.  
The energy consumption of the control and data packets in our simulations was calculated based on the AquaSeNT OFDM Modems presented in Table 1. The average traffic generation rates of four senders involved in the simulation were set as \(\lambda=0.02,0.03,0.05,0.1\), respectively. Figure 10 illustrates that TERI-MAC was able to achieve the desired energy efficiency by adjusting parameter \(E_{th}\) from 0.1 to 0.9. In other words, the good consistency between the desired \(E_{th}\) and the achieved \(E\) verified the effectiveness of both the energy efficiency control and the traffic estimation procedure in TERI-MAC.  
Figure 9: Tree topology in the network simulation.  
As introduced in the problem of when to poll data in Section 6.1, the frequency of data polling is a key factor that affects energy efficiency and delays performance, and thus, Algorithm 2 is was proposed to decide when to poll data based on the threshold of the control packet overhead \(E_{th}\). Figure 11 shows the tradeoff between the energy efficiency and the average hop-by-hop communication delays of the four senders. The simulation results revealed two facts as follows: (a) The delays significantly increased when the target control packet overhead \(E_{th}\) decreased from 0.3 to 0.1 for all senders. In other words, a relatively low \(E_{th}\) resulted in a longer hop-by-hop delay. (b) For senders with different traffic loads \(\lambda\), a larger \(\lambda\) resulted in a longer average hop-by-hop delay. We found that the protocol worked with high efficiency and acceptable delay when \(E_{th}=0.2\). For delay-sensitive applications, a control packet overhead as low as 0.3 can be achieved in TERI-MAC.  
Figure 11: Tradeoff between energy efficiency and the hop-by-hop delay performance.  
Figure 10: The achieved energy efficiency with respect to different parameter settings (\(E_{th}\)) in TERI-MAC.  
```
if\(A_{Q_{i}}==A_{next}\)then % \(A_{Q_{i}}\) denotes the address of the node \(Q_{i}\)  Poll data when current handshaking ends; else while\(E_{est(i)}\geq E_{th}\) & \(max(D_{j})\leq D_{th}\)do % where \(j\) is the index of intended senders around \(Q_{i}\)  Waiting time \(++\); endwhile  Poll data if channel idle; endif
```  
**Algorithm 2** When to poll data.  
Next, we conducted simulations to verify the efficiency of Algorithm 1, which solves the how much data to poll problem. As introduced in Section 6.2, the transmission slot allocation among senders based on the traffic estimation acts as a tradeoff between channel utilization and delay performance by adjusting the delivery percentage \(P_{del}\) from 0.1 to 1. Figure 12 demonstrates that the channel utilization linearly decreases with the increase of \(P_{del}\), presenting a reduced delay in an inverse proportional way. The simulation results revealed that a \(P_{del}\) within \([0.4\ 0.5]\) can achieve an acceptable channel utilization and delay.