Fingerprinting Based Indoor Localization Considering the Dynamic Nature of Wi-Fi Signals

Current localization techniques in the outdoors cannot work well in indoors. The Wi-Fi fingerprinting technique is an emerging localization technique for indoor environments. However, in this technique, the dynamic nature of WiFi signals affects the accuracy of the measurements. In this paper, we use the affinity propagation clustering method to decrease the computation complexity in location estimation. Then, we use the least variance of Received Signal Strength (RSS) measured among Access Points (APs) in each cluster. Also, we assign lower weights to alter APs for each point in a cluster, to represent the level of similarity to Test Point (TP) by considering the dynamic nature of signals in indoor environments. A method for updating the radio map and improving the results is then proposed to decrease the cost of constructing the radio map. Simulation results show that the proposed method has 22.5% improvement in average in localization results, considering one altering AP in the layout, compared to the case when only RSS subset sampling is considered for localization because of altering APs

Indoor Geo-Indistinguishability:Adopting Differential Privacy for Indoor Location Data Protection

Due to the extensive applicability of Location-Based Services (LBSs) and the Global Navigation Satellite System (GNSS) failure in indoor environments, indoor positioning systems have been widely implemented. Location fingerprinting, in particular, collects the Received Signal Strength (RSS) from users&amp;#x0027; devices, allowing Location Service Providers (LSPs) to precisely identify their locations. Therefore, LSPs and potential attackers have implicit access to this sensitive data, violating users&amp;#x0027; privacy. This issue has been addressed in outdoor environments by introducing Geo-indistinguishability (GeoInd), an alternative representation of Differential Privacy (DP). In indoor environments, however, the user lacks their coordinates, posing a new difficulty. This paper presents a novel framework for implementing GeoInd for indoor environments. The proposed framework introduces two distance calculation and RSS generation methods based solely on RSS values. Moreover, involving other participants or trusted third parties is not necessary to protect privacy, regardless of the attackers&amp;#x0027; prior knowledge. The proposed framework is evaluated in a simulated environment and two experimental settings. The results validate the proposed framework's efficiency, effectiveness, and applicability in indoor environments under the GeoInd setting.</p

Design and performance of the fingerprinting technique for indoor location

Due to the general failure of Global Positioning System (GPS) for indoor positioning, non-satellite-based technologies are important forindoor localization. Using wireless networks based on the Received Signal Strength (RSS) location fingerprinting technique is the mostpopular positioning method used for indoor environments.This research proposes a new positioning technique based on fingerprinting that utilises one of the most available signals ofopportunity (SoOP), which is frequency modulated (FM) broadcast radio signals. Then the fusion of FM and Wi-Fi is investigated. Theresult outperformed the previous methods in terms of accuracy and a more robust and reliable positioning system is presented.Moreover, an analytical framework for estimating the accuracy performance of fingerprinting indoor positioning systems is suggested.Using this model, the most common signal distances such as Euclidean, Manhattan and Chebychev are fully analyses and comparedtogether both mathematically and by simulation so that we can identify which provides least positioning error. Crame-Rao lower bound(CRB) is widely used for assessing localization performance limits but the recent measurement revealed that CRB does not alwaysrepresent an actual lower bound for indoor positioning. We utilise and modify two more advanced lower bounds and propose anoptimization trend in system configuration such that the attained root mean square error in the position estimator gets closer to theminimal attainable variance in the fingerprinting position estimator. Finally, a new method for error estimation in indoor localizationsystems is designed and novel precision measurements factors for fingerprinting method is developed. Thus the quality of service ofthe positioning system is improved and the integrity of the system is guaranteed.In this research, the problem of evaluation and enhancement the accuracy of fingerprinting positioning systems utilizing terrestrial FMsignals is addressed and analytical frameworks and appropriate solid tools for designing more precise indoor positioning systems aredeveloped. In summary, the FM-based positioning analysis, analytical position error estimation tools, statistical analysis on theaccuracy of the indoor positioning systems, and the design criteria tools in this thesis are novel and provide interesting insights intothe positioning system performance. These tools are used to optimise the system performance under given performance objectives andconstraints