In paper , a secure localization mechanism is proposed so that phantom nodes with fake locations can be detected. The basic assumpiton in this paper is that each sensor can conduct bidirectional communication. A useful conclusion in this paper is that we can hide the nodes’ location information to avoid faked nodes giving consistent distances. This means that all faked nodes can be detected by looking at the inconsistent ranging claims. However, the method proposed in the paper is based on the bidirectional commmunication between the neighbour nodes. While in our application, the beacons do not have communication among each other so that the distance between any two nodes cannot be estimated.
 is a classical paper in the so called “attack-resistant location estimation” field. But like many papers in that time, it is assumed that communication between each node pair is available so that distance can be estimated, which is not the case in our iBeacon case.
In Paper , indoor localization can be achieved without site survey phase. The key is to exploit the user motions from the mobile phone to connect the independent radio signatures. The existing indoor localization methods are categorized as two ways: (1) Fingerprinting-based and (2) Model-based. (Actually, machine learning is a emerging method in recent years). However, in this method, the accurate floor plan has to be known beforehand.
In 2012 MobiSys paper , unsupervised indoor localization can be achieved from the observations that some positions with special identifiable signatures such as an elavator or a corridor-corner can serve as natural landmarks in the building. Although the performance of the method is demonstrated with Wi-Fi based localization, same idea can be used in BLE based localization if we can get other data from the device such as accelerometer and compass. However, this technology also needs to know the accurate floor plan of the building. How to detect and remove the wrong landmark is not discussed in the paper.
In 2017 PerCom papaer , human locomotion and map exploitation are utilized to compensate for the accumulated error when using inertial sensors for infrastratructure-free indoor localization. However, this method also relies on the accuracy of the POI information.
 Wu, C., Yang, Z., Liu, Y., & Xi, W. (2013). WILL: Wireless indoor localization without site survey. IEEE Transactions on Parallel and Distributed Systems, 24(4), 839-848.
 Wang, H., Sen, S., Elgohary, A., Farid, M., Youssef, M., & Choudhury, R. R. (2012, June). No need to war-drive: unsupervised indoor localization. In Proceedings of the 10th international conference on Mobile systems, applications, and services (pp. 197-210). ACM.
 Lin, X., Chang, X. W., & Liu, X. (2017, March). LocMe: Human locomotion and map exploitation based indoor localization. In Pervasive Computing and Communications (PerCom), 2017 IEEE International Conference on (pp. 131-140). IEEE.
 Hwang, J., He, T., & Kim, Y. (2007, May). Detecting phantom nodes in wireless sensor networks. In INFOCOM 2007. 26th IEEE International Conference on Computer Communications. IEEE (pp. 2391-2395). IEEE.
 Liu, D., Ning, P., & Du, W. K. (2005, April). Attack-resistant location estimation in sensor networks. In Proceedings of the 4th international symposium on Info