# An Efficient Location Verification Scheme for Static Wireless Sensor Networks
## 2 Related Works  
There have been many secure localization schemes [2; 3; 4; 5; 6; 7; 8] to give honest sensors the capability of securely positioning themselves even in the presence of attackers. Even though the secure localization protects the location estimation process from several attacks, the location estimation can still go wrong in some situations, e.g., compromised sensors report a false location intentionally. Therefore, many researchers have worked on the location verification in WSNs [10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21]. Location verification schemes can be categorized as in-region and on-spot verification [15]. In-region verification usually measures the round-trip time of challenge-response packets to calculate the distance between two sensors and uses the distance to determine whether a sensor is located inside some region. This can be done with the distance-bounding [22], which requires extra hardware to measure the timing with nanosecond precision. The echo protocol proposed by [PERSON] et al. is an example of addressing the location verification problem based on the distance-bounding [10]. It used radio wave and ultrasound to determine whether a sensor is inside a specific region or not. On the other hand, on-spot verification usually verifies whether the claimed location of the sensors is correct [11; 12; 13; 14; 15; 16; 17; 18]. This type of verification mainly uses pre-knowledge of the sensor deployment, the hop-distance relationship, graph theory or neighborhoods' observation to verify the claimed location of sensors. The following research works are the examples of on-spot verification. [PERSON] et al. proposed the location anomaly detection (LAD) scheme as the second-line of defense for the localization [12]. In their paper, they formulated the localization anomaly problem as an anomaly intrusion detection problem and proposed the detection method. The method is based on the consistency between estimated location and the deployment knowledge of sensors. The scheme needs to collect packets from all sensors for location verification to generate several matrices reflecting the observations on other sensors. It is effective in detecting attacks against the localization, but requires prior sensor deployment knowledge and involvement of many sensors for the location verification. [PERSON] et al. proposed the on-spot verification scheme called Probabilistic Location Verification (PLV). The idea is to utilize the hop-distance between sensors [13]. The hop-distance is a statistical relationship between the hop count and the Euclidean distance the broadcast packet traverses. In this scheme, a small number of verifiers performs the probability calculation and sends it to the central node. The central node finally calculates the plausibility of the reported location of a sensor to detect the malicious sensors. With the help of probability and statistics, the scheme does not need special hardware, but it still has room to be attacked due to the use of dedicated verifiers and the trusted third party. Phantom node detection was proposed by [PERSON] et al. [14]. In phantom node detection, each sensor generates the local map, which is constructed based on the distances among its neighbors to perform the location verification process. Since the process only uses relative distances among neighbors and does not reveal the location of sensors, attackers cannot make their fake location consistent with honest sensors in the local map. In other words, the scheme works based on the assumption that all of the location information of sensors is hidden. The advantage of the scheme is that it can filter out most of the attackers even when they overwhelm honest sensors, and it works in a distributed way. However, it requires all neighbors' estimation and the broadcast of their mutual distances with much communication between them.