## 1 Introduction  
With the continuous development of communication and network technology, multimedia steganography has been widely used in the fields of covert communication [1] and digital watermarking [2]. Recently, more and more criminals have begun to use multimedia steganography to commit high-tech crimes [3; 4; 5]. They hide secret information in public media information to make people hardly able to detect its existence with intuitive vision and hearing. Therefore, these criminals can achieve the purpose of transmitting secret information. Although the existing steganography detection methods can successfully detect covert communication entities, the physical locations of the covert communication entities are still unknown. To realize the complete forensics of the crime, post-steganalysis that investigates the physical location of the covert communication entity should be carried out [6]. Therefore, locating the covert communication entities has important practical significance and research significance for national network information security and social security and stability [7]. Previous works have discussed how to locate the covert communication entities based on IP addresses [6], but IP geolocation is usually suitable for outdoor localization and cannot be used for indoor localization. How to locate the indoor locations of the entities relying on wireless sensor nodes for covert communication is still seldom discussed. Currently, illegal elements can easily hide illegal information in the data transmitted by wireless sensors to achieve malicious attacks [8]. To obtain the wireless sensor node location to help criminal evidence, we can locate the wireless sensor node by studying its received signal strength (RSS) [9; 10]. At present, the localization method based on RSS is widely used due to its low energy consumption, low cost and low implementation complexity [11; 12; 13]. Although the localization-method-based RSS is easy to implement, the localization precision is easily affected by environmental factors, such as multipath effect, non-line of sight and shadow fading. Therefore, some scholars have improved this method [14; 15]. [PERSON] et al. [14] propose an RSS self-calibration protocol to improve localization precision. An RSS self-calibration protocol can adopt calibration parameters according to the change in environment over time. [PERSON] et al. [15] first used a deep learning method to refine the collected raw RSS data. Then, they adopted a machine learning method to extract the inherent spatial network geometric relationships from RSS data sets. These correlations are used to reduce the amplitude of RSS fluctuations and thus improve localization precision. In addition, some other localization methods can also be extended by methods based on RSS [16; 17; 18; 19; 20; 21]. [PERSON] et al. [16] and [PERSON] et al. [17] evaluate the distance between the reference point and the target node according to the obtained RSS value. They identify the overlapping region centroid of all reference points within the communication radius of the target node as the target node location. Finally, they propose the centroid localization method. [PERSON] et al. [18] and [PERSON] et al. [19] propose a localization method based on location fingerprint. This method uses RSS to construct a location fingerprint database, and finally realize localization. [PERSON] et al. [20] and [PERSON] et al. [21] propose a method based on RSS approximation, which is based on the gradient value of the RSS. This method first measures the RSS of multiple points and then compares the RSS. Finally, it will analyze the changing trend of the RSS and estimate the target node location. However, the existing RSS-based localization methods still have some problems in reference point selection and improving localization precision.