## 1 Introduction
### Related Works and Issues  
Since the exposure of the source node location in WSN inevitably threatens the security of the monitored target, the source node location privacy protection becomes an urgent issue to be solved. However, since the computational capability, storage capacity and energy resources of sensor nodes are limited, the balance between security and network performance becomes an inevitable requirement. The existing researches on source node location privacy protection are mainly based on cyclic entrapment [11], dummy data sources [6; 7; 12; 13; 14; 15; 16] and phantom routing [6; 7; 16; 17; 18; 19; 20; 21; 22]. [PERSON] et al. [11] introduced the cyclic entrapment concept as a special case of dummy data source routing. In cyclic entrapment, multiple nodes act as dummy data sources, and interconnect to form a loop. The main aim of cyclic entrapment is to confuse adversary with these loops during a hop-by-hop-trace attack, thereby preventing the attacker from going back to the real source node. However, such a strategy needs to activate one or more loops to restrict the attacker, and the nodes in the loop which act as the dummy data source need to generate dummy data periodically, which causes a large amount of abnormal communication overhead, results in energy hole [19] and damages the network performance seriously. In addition, when the Panda-Hunter model was first proposed in Reference [6], it has been assumed that the hunter has the ability to cache the location information, and he can record the position of the nearest \(N\) nodes, so as to avoid falling into the loop, which invalidates the protection capability of the scheme. Although dummy data source routing can protect source location privacy to a certain extent, it needs to generate a lot of dummy data periodically, which not only causes a lot of waste of energy consumption, but also increases packet collision probability and reduces packet transmission efficiency. Moreover, the true path of packet transmission is included in all paths, and the source node location privacy protection capability is probabilistic. [PERSON] et al. first proposed the phantom routing scheme (PRS) in Reference [6] to protect the location privacy of source nodes using the Panda-Hunter model. They use the phantom node to entice the adversaries away from the actual source node. If the attacker cannot track the PN, it is impossible for him to trace the location of the true source node [2]. PRS involves two phases: The random walk phase and the subsequent flooding phase. In the random walk phase, the packet from the real source is randomly transmitted \(h\) hops to determine a PN. In the flooding phase, the PN transmits the data packets to the SN by the way of flooding. The main aim of this scheme is to ensure that the attacker can only trace back to the PN and cannot trace back to the real source node, thus ensuring the privacy of the source node location. However, the shortest path in the flooding phase is included in the flooding paths, and the first packet received by the attacker is the shortest path packet. The attacker can capture the SoN by tracing back along the shortest path. [PERSON] et al. proposed a phantom single-path routing scheme (PSRS) in Reference [7], which is similar to the PRS scheme. In this scheme, the first phase is a directed random walk, and in the second phase, a single path routing algorithm is used to transmit data packets to the SN. This paper also provides a technique for classifying neighbors of nodes. In Reference [18], [PERSON] et al. pointed out that the PNs in the existing source location privacy protection schemes are concentrated near the SoN, and once the attacker traces back to the PN, he can further trace to the SoN easily. In response to this problem, they proposed an enhanced source location privacy preservation protocol using source-based restricted flooding (EPUSBRF).