# An Efficient Location Verification Scheme for Static Wireless Sensor Networks
## 2 Related Works
Moreover, it could not cover the practical environment where attackers with their known position cooperate to find out the location of honest sensors through any localization method, which consequently makes the phantom detection scheme ineffective. Later, [PERSON] et al. presented the location verification algorithm that depends on neighborhoods' observation [15]. The scheme is lightweight because it eliminates the burden of sensors by using a powerful central server to process the location verification. In addition, it does not use special hardware and sensor deployment knowledge. However, it requires the active participation of neighbors and the trusted third party to perform the location verification. Especially, the use of the central server may cause a single point of failure. The centralized approach makes it necessary that too many sensors have to be involved in the location verification process even when a small number of sensors has a problem with their location. There were more recent research works trying to eliminate the fixed anchors or to detect the unreliable anchors and sensors [16; 17; 18]. [PERSON] et al. suggested the location verification based on verifiable multilateration (VM) [11] in [16]. They used a drone to replace the fixed anchors. Their eventual algorithm focused on designing the path planning algorithm of the drone to do the task of fixed anchors instead. With the algorithm, it shows the possibility of drone-based VM, but they still depend on the fixed anchors. [PERSON] et al. provided the collaborative localization and location verification to detect the unreliable sensors [17]. They used the virtual force model to calculate the more accurate location of the sensors. Sensors exert the virtual force on each other based on their measured and estimated distances. Based on the force model, they find out the movement of anchors and normal sensors. It works in a distributed manner to eliminate the single point of failure. Even though they have an issue of anchor compromise, this depends on the anchors to initiate the whole process. [PERSON] et al. developed the distributed location verification by using the reputation system based on the mutual distance inconsistency between sensors [18]. This scheme calculates the reputation of neighbors based on the distances. The distances are estimated by measuring the received signal strength (RSS) of sensors and performing the trilateration. They can detect the movement of sensors by using the reputation result in a distributed manner. However, this still requires fixed anchors. Even though it is not designed only for WSNs, there are the other approaches of [PERSON] and [PERSON] [19; 20; 21]. They formulated the location verification problem as a kind of detection system, and the location verification is achieved by finding the optimal solution for the formulated problem. This approach helps the system designer to analyze and prove the suggested algorithm in a mathematical way. Those location verification schemes also use the fixed anchors or base stations to find the malicious sensor. To summarize, the previous research works mandate the active participation of the entire network or neighbors, the use of a dedicated verifier or a trusted third party for the location verification. The proposed scheme, which is categorized as on-spot verification, tries to reduce the communication and computation overhead caused by the active involvement of the entire network or neighbors and the complex calculation procedure. Furthermore, it decreases the sensor deployment cost by eliminating the use of a dedicated verifier or a trusted third party.