# An Accuracy-Aware Energy-Efficient Multipath Routing Algorithm for WSNs
## 6 Evaluation
### Comparison with Other Algorithms
The convergence time is evaluated by the average time required for the network to establish a set of multiple paths from the source to the destination in IQMRP, AMRBEC, and EMRAR or a node to find a route to the sink on a tree-based topology in CADC. It can be seen that as the network size increases, the convergence time of each algorithm increases. However, CADC shows linear growth with increasing adjacent node density, while IQMRP, AMRBEC, and EMRAR show exponential increases. This difference  
Figure 5: The comparison of three algorithms under different data transmission rates when \(\Delta=3\%\) and the adjacent node density is 20. (**a**) AAL performance, (**b**) PDR performance, (**c**) NRE performance. arises because the CADC algorithm only needs to find the shortest path from the source to the destination. It has a linear time complexity to the number of edges in the network. In contrast, IQMRP and EMRAR share similar time complexities--IQMRP adopts the classic ACO algorithm, while EMRAR adopts the IAAIA algorithm that improves the antibody excitation operator of the AIA algorithm and accelerates the convergence. Therefore, EMRAR boasts a shorter convergence time IQMRP. For AMRBEC, which shares the same time complexity as EMRAR, the convergence time curve closely resembles EMRAR's when the density of the adjacent nodes is larger than 10. However, at lower node densities, AMRBEC may struggle to find a multipath solution with disjointed nodes, resulting in considerably longer average converge times in the simulation. Therefore, AMRBEC is not suitable for scenarios with low node density. Figure 7 shows the comparison of average end-to-end delay under different data transmission rates. End-to-end delay is evaluated by the average time taken for data to travel from the source to the destination. It can be seen that as the data transmission rate increases, resulting in a higher offered load in the network, the average end-to-end delay of each algorithm increases. Among them, both CADC and AMRBEC algorithms exhibit poorer end-to-end delay performance. For CADC, although it establishes a tree-based topology with the shortest path, data aggregation occurs at each branch node of the tree, leading to increased end-to-end delay caused by channel sharing at branch nodes. On the other hand, AMRBEC, which must establish node-disjointed multipath, faces challenges in establishing multiple shortest paths, occasionally resorting to longer paths. Hence, at lower data transmission rates, AMRBEC fails to outperform CADC in terms of end-to-end delay. However, as the data transmission rate increases due to the node-disjointed multipath, AMRBEC performs better than CADC because of less data aggregation and queuing. IQMRP has the best performance in this comparison because it establishes a QoS-based multipath routing, with latency being one of the measurements. For EMRAR, it prefers paths with small accuracy loss to low-latency next-hop or shortest paths, resulting in an increased end-to-end delay in comparison with IQMRP. The simulation results reveal distinctive strengths and weaknesses among the evaluated algorithms. CADC is superior in convergence time but poor in all other performance metrics. IQMRP shows the best performance in average end-to-end delay, but it has the largest convergence time and is relatively compromised in other aspects. AMRBEC has minimal accuracy loss and the highest residual energy when the data transmission rate is low. However, its performance is less satisfactory in other aspects, especially when the data transmission rate and node density are low. In contrast, our proposed algorithm, EMRAR,  
Figure 6: The comparison of three algorithms on convergence time under different adjacent node densities. has the best performance in terms of PDR and residual energy while also maintaining competitiveness in other aspects.