Indoor localization based on WiFi has attracted a lot of research effort because of the widespread application of WiFi. Fingerprinting techniques have received much attention due to their simplicity and compatibility with existing hardware. However, existing fingerprinting localization algorithms may not resist abnormal received signal strength indication (RSSI), such as unexpected environmental changes, impaired access points (APs) or the introduction of new APs. Traditional fingerprinting algorithms do not consider the problem of new APs and impaired APs in the environment when using RSSI. In this paper, we propose a secure fingerprinting localization (SFL) method that is robust to variable environments, impaired APs and the introduction of new APs. In the offline phase, a voting mechanism and a fingerprint database update method are proposed. We use the mutual cooperation between reference anchor nodes to update the fingerprint database, which can reduce the interference caused by the user measurement data. We analyze the standard deviation of RSSI, mobilize the reference points in the database to vote on APs and then calculate the trust factors of APs based on the voting results. In the online phase, we first make a judgment about the new APs and the broken APs, then extract the secure fingerprints according to the trusted factors of APs and obtain the localization results by using the trusted fingerprints. In the experiment section, we demonstrate the proposed method and find that the proposed strategy can resist abnormal RSSI and can improve the localization accuracy effectively compared with the existing fingerprinting localization algorithms.
With the help of location-based services (LBS), it makes driving more convience for drivers. However, because the untrusted LBS server may leak the user's location information, the user's privacy is threatened. Moreover, the existing methods of location privacy protection do not take into account the impact of context on privacy protection demand. In addition, heterogeneous data sensed by vehicles also increases the complexity of application development. In order to solve the above problems, we propose a context-based location privacy protection middleware architecture, named PP-OSGi. The middleware simplifies application development by shielding the heterogeneity of vehicle sensed data and upper-layer applications. Furthermore, in order to protect the real location information of service request vehicles under different vehicle densities in a certain area, we propose a dynamically adjustable k-anonymous (DAK) algorithm and a location privacy protection (DLP) algorithm based on a dummy location, which are all encapsulated in PP-OSGi. The DAK and DLP algorithms dynamically determine the location privacy protection strength in different contexts based on the user's location privacy preference model, select an anonymous group of neighboring vehicles to construct a anonymous area, and obtain a dummy location of the service request vehicle. The experimental results show that, under the premise of protecting the location privacy of vehicles, the success rate of service requests is improved and the communication cost between vehicles is reduced.
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