# An Efficient Internet-Wide Scan Approach Based on Location Awareness
## 5 Experiments
### Experiments Analysis  
(1) Efficiency Evaluation: Efficiency evaluation is the central starting point of our study and is evaluated by scanning the average usage time of network segments for individual IP addresses. Each network segment is 24 network bits, and the selection of each network segment is randomised. This design is able to better simulate the uncertainty of a realistic network environment. During the experiment, to ensure the reliability of the data and the stability of the experimental results, we designed three independent experiments and selected a different IP address for scanning in each experiment. This not only helps to eliminate the chance factors in the experiments but also enhances the universality and representativeness of the experimental results. Figure 7 shows the results of the scanning time evaluation based on the location-aware model with the random-scanning method under different numbers of IP network segments. As can be seen from the figure, Figure 7a to Figure 7d represent the situation when different numbers of scanner clusters are used, respectively. In these figures, the horizontal axis represents the number of IP network segments scanned, and the vertical axis represents the average scanning time. In order to clearly distinguish between the different scanning methods, different colours are used for the bar charts, where one type of colour indicates the  
and thus improve the scanning efficiency with reasonable task allocation and optimised task scheduling. In summary, by analysing these experimental data, we can conclude that the location-aware scanning approach excels in reducing the scanning time, especially when the number of network segments is large, and can significantly improve the efficiency and reduce the unnecessary waste of time. Therefore, the application of location-aware models in large-scale network scanning tasks has significant advantages. (2) Load-balancing validation: In the above method, we introduced a load-balancing scheme to address the differences in scanning efficiency among different clusters. In order to verify the effectiveness of the method, we have recorded the scanning time of each scanner cluster and scanning node in detail, and the specific data is shown in Figure 8. The criterion for verifying load balancing is that if the difference in scanning time between different scanner clusters is large, the load is considered to be unbalanced; conversely, if the time difference is small, the load is considered to be balanced. In Figure 8a, the x-axis represents the number of target network segments being scanned, and the y-axis represents the time spent on scanning. We can observe that the difference in scanning time between each scanner cluster is small, which indicates that the load between clusters has reached a relatively balanced state through the load-balancing method. Furthermore, to verify the load balancing among the scanning nodes, we deploy three scanners in the DE cluster and two scanners in the US cluster, as shown in Figure 8b,c. We observe that there is no significant difference in the scanning time of each scanning node within the cluster, either  
Figure 7: Average scanning time of different numbers of IP network segments using location-aware model and random scanning approach: (**a**) average scan time for five scanner clusters; (**b**) average scan time for ten scanner clusters; (**c**) average scan time for twenty scanner clusters; (**d**) average scan time for thirty scanner clusters. **Note**: Horizontal axis indicates number of areas scanned, and vertical axis indicates average scanning time of scanner. in the DE cluster or the US cluster. To further demonstrate the advantages of load balancing, we show the time used by the shortest as well as the longest time-consuming scanners in the random scanning method, as detailed in Figure 8d. The variability between the shortest elapsed time and the longest elapsed time of this random scanning method can be seen in Figure 8d to be particularly significant.