# An Efficient Internet-Wide Scan Approach Based on Location Awareness
## 2 Related Works  
As mentioned above, this paper focuses on the geographical location of scanning node deployment. Currently, there are multiple studies in various aspects of Internet-wide scanning aimed at improving scanning speed. Based on the research content, works related to our study can be categorised into two types:  
The first category is customised algorithm research targeting specific challenges in network probing. For example, [PERSON] et al. [10] proposed a method using environmental recognition algorithms to classify WLAN environments into different states, thereby improving scanning performance. [PERSON] et al. [11] proposed a NAT traversal system based on reverse proxies for scanning ports behind NAT. This addresses the port scanning issue for devices with private IP addresses behind NAT by adaptively adjusting scanning frequency to balance network performance and security. [PERSON] et al. [12] introduced a dynamic weighting method based on centroid calculation to build a baseline model for Internet port scanning, aiming to detect abnormal behaviors in the network by analysingchanges in scanning traffic. [PERSON] et al. [13] employed a Bayesian optimisation method to improve port scanning techniques in router port testing. The Bayesian optimisation-based port scanning technique constructs and updates a Bayesian optimisation model to select the optimal scanning strategy, thus improving the efficiency and accuracy of router port testing.  
The second category of research focuses on the influence of various factors during the scanning process on the scanning results. For example, [PERSON] et al. [14] conducted a longitudinal study over nine years using ten PlanetLab nodes distributed in different geographical locations to explore the impact of weather conditions on host downtime. They used multiple vantage points for redundancy but did not analyse the differences between vantage points. However, they recognised that hosts might not respond to all origins and correlated the probability of these drop events with various factors. In [15], it was noted that, apart from Germany, Brazil, and Australia, a large portion of affected networks were in China. This work indicated that packet loss on the path to China was abnormally high and unstable. In [16], the study investigated how the network used for Internet-wide scanning affects the results. It was found that a single scanning node's single-probe scan could detect around 96% of HTTP(S) and 84% of SSH hosts worldwide. This is more than double the loss initially estimated by [PERSON] et al. Moreover, the issue was not just due to uniform random packet loss. Host inaccessibility was caused by both temporary and long-term network problems.  
Overall, existing scanning methods have achieved good results from different perspectives. However, there is currently a lack of effective multi-node cooperative scanning methods for large-scale network scanning. In this paper, we propose a location-aware scanning method, which applies geographic location to Internet-wide scanning. It is a multi-node collaborative approach that can effectively address large-scale network scanning.