An Overview of Wireless Indoor Positioning Systems: Techniques, Security, and Countermeasures

Chebli, Mouna S.; Mohammad, Heba; Amer, Khalifa Al

doi:10.1007/978-3-030-34914-1_22

Cited by 3 publications

(2 citation statements)

References 27 publications

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“…WiFi-based positioning technology is mainly divided into two categories: triangle positioning method and fingerprint positioning method [ 21 , 22 ]. Triangulation is also the basic operating principle of GPS [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

WiFi Indoor Location Based on Area Segmentation

Wang

Gao

Dai

et al. 2022

Sensors

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Indoor positioning is the basic requirement of future positioning services, and high-precision, low-cost indoor positioning algorithms are the key technology to achieve this goal. Different from outdoor maps, indoor data has the characteristic of uneven distribution and close correlation. In areas with low data density, in order to achieve a high-precision positioning effect, the positioning time will be correspondingly longer, but this is not necessary. The instability of WiFi leads to the introduction of noise when collecting data, which reduces the overall performance of the positioning system, so denoising is very necessary. For the above problems, a positioning system using the DBSCAN algorithm to segment regions and realize regionalized positioning is proposed. DBSCAN algorithm not only divides the dataset into core points and edge points, but also divides part of the data into noise points to achieve the effect of denoising. In the core part, the dimensionality of the data is reduced by using stacking auto-encoders (SAE), and the localization task is accomplished by using a deep neural network (DNN) with an adaptive learning rate. At the edge points, the random forest (RF) algorithm is used to complete the localization task. Finally, the proposed architecture is verified on the UJIIndoorLoc dataset. The experimental results show that our positioning accuracy does not exceed 1.5 m with a probability of less than 87.2% at the edge point, and the time is only 32 ms; the positioning accuracy does not exceed 1.5 m with a probability of less than 98.8% at the core point. Compared with indoor positioning algorithms such as multi-layer perceptron and K Nearest Neighbors (KNN), good results have been achieved.

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Section: Introductionmentioning

confidence: 99%

WiFi Indoor Location Based on Area Segmentation

Wang

Gao

Dai

et al. 2022

Sensors

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“…However, high inter-connectivity to unknown devices and the lack of strong communication protocols in terms of security and privacy represent a weakness. Malicious actors could perform attacks such as spoofing or eavesdropping in unsecured collaborative systems (Chebli et al, 2019). These issues encouraged the development of more privacy-friendly solutions.…”

Section: Introductionmentioning

confidence: 99%

Anonymous Attribute-based Credentials in Collaborative Indoor Positioning Systems

Casanova-Marqués

Pascacio

Hajný

et al. 2021

Proceedings of the 18th International Conference on Security and Cryptography

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Collaborative Indoor Positioning Systems (CIPSs) have recently received considerable attention, mainly because they address some existing limitations of traditional Indoor Positioning Systems (IPSs). In CIPSs, Bluetooth Low Energy (BLE) can be used to exchange positioning data and provide information (the Received Signal Strength Indicator (RSSI)) to establish the relative distance between the actors. The collaborative models exploit the position of actors and the relative position among them to allow positioning to external actors or improve the accuracy of the existing actors. However, the traditional protocols (e.g., iBeacon) are not yet ready for providing sufficient privacy protection. This paper deals with privacy-enhancing technologies and their application in CIPS. In particular, we focus on cryptographic schemes which allow the verification of users without their identification, so-called Anonymous Attribute-based Credential (ABC) schemes. As the main contribution, we present a cryptographic scheme that allows security and privacy-friendly sharing of location information sent through BLE advertising packets. In order to demonstrate the practicality of our scheme, we also present the results from our implementation and benchmarks on different devices.

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