# An Equilibrium Strategy-Based Routing Optimization Algorithm for Wireless Sensor Networks
## 4 Simulation Results and Analysis
### Comparison with Other Algorithms
#### 4.2.2 Traffic Balance and Energy Balance  
Usually, the node load affects network performance in two ways. On the one hand, the node load can reflect the amount of traffic that is aggregated to the node at some point. The larger the load, the more data need to be transferred. On the other hand, the number of the loads also determines the residual energy of node to a certain extent, that is, the larger the load, the less energy the node has due to the greater transmission energy consumption. In addition, the more balanced the energy consumption between nodes, the higher the node survival rate and the longer the network lifetime. Therefore, on behalf of measuring the balance of the network, we consider both traffic balance and energy balance in this section.  
(1) Traffic Balance  
Figure 9 shows the different packet loss rates of different algorithms at different network sizes. It can be seen that compared with the other two algorithms, the packet loss rate of ESRA algorithm is always stable at a lower value as the network size changes. Obviously, this is because FAF-EBRM and FLEOR do not take into account the queue size of the nodes. A large amount of data are concentrated in some \"hot spots\" at the same time, causing nodes in this area to exceed their load capacity due to excessive data reception, thus a large amount of data are discarded. On the contrary, our algorithm adopts the edge-cutting strategy to balance the node load. In the minimum routing graph, the load is updated in real time, and the candidate parent nodes with smaller load are dynamically selected as the forwarding node, which alleviates the congestion degree, so the network traffic is relatively balanced.  
(2) Energy Balance  
For the three different algorithms, the comparison of the energy variance EV and the node survival rate NSR are given in Figures 10 and 11, respectively. Among them, the EV curve of the FAF-EBRM algorithm has the highest volatility, and the energy balance is worse than the other two algorithms. The energy balance of FLEOR is improved compared with FAF-EBRM, this is because FLEOR considers the energy balance of the forward neighbor, and prefers the forward neighbor with larger residual energy as next hop. However, the network lifetime of FLEOR is shorter than FAF-EBRM, and the first node death appears in around 320 rounds. In this paper, the ESRA algorithm can sense the node loads in some way, which can optimize the routing of the next data round and rebalance the energy distribution. Therefore, the EV curve has the smallest fluctuation and the node survival rate is maintained at a highest level at all times. Simultaneously, according to the rounds of the first dead node, its network lifetime is about 80% and 150% longer than FAF-EBRM and FLEOR respectively, which means that the network can maintain a longer effective working time. In addition, when all nodes in FAF-EBRM have died, ESRA has not yet appeared the death of first node, showing a good network performance.  
Figure 9: The PLR comparisons with varying network size.