Accurate WiFi Based Localization for Smartphones Using Peer Assistance

Liu, Hongbo; Yang, Jie; Sidhom, Simon; Wang, Yan; Chen, Yingying; Ye, Fan

doi:10.1109/tmc.2013.140

Cited by 135 publications

(50 citation statements)

References 30 publications

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“…It is worth noting that Wi-Fi and Bluetooth technologies can also be used as environmental metrics to study mobility patterns 15 and locale characteristics. 16 Figure 6 shows one of the ways that network traces allow us to understand the movements and interactions a person makes. These figures represent the evolution, over the course of 1 day, of interactions between Wi-Fi access points (APs) and between Bluetooth devices, using a unique numerical identifier generated from their BSSID (Y axis).…”

Section: Environmental Sensorsmentioning

confidence: 99%

Characterizing user mobility using mobile sensing systems

Faye

Bronzi

Tahirou

et al. 2017

International Journal of Distributed Sensor Networks

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Recent technological advances and the ever-greater developments in sensing and computing continue to provide new ways of understanding our daily mobility. Smart devices such as smartphones or smartwatches can, for instance, provide an enhanced user experience based on different sets of built-in sensors that follow every user action and identify its environment. Monitoring solutions such as these, which are becoming more and more common, allows us to assess human behavior and movement at different levels. In this article, extended from previous work, we focus on the concept of human mobility and explore how we can exploit a dataset collected opportunistically from multiple participants. In particular, we study how the different sensor groups present in most commercial smart devices can be used to deliver mobility information and patterns. In addition to traditional motion sensors that are obviously important in this field, we are also exploring data from physiological and environmental sensors, including new ways of displaying, understanding, and analyzing data. Furthermore, we detail the need to use methods that respect the privacy of users and investigate the possibilities offered by network traces, including Wi-Fi and Bluetooth communication technologies. We finally offer a mobility assistant that can represent different user characteristics anonymously, based on a combination of Wi-Fi, activity data, and graph theory.

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Section: Environmental Sensorsmentioning

confidence: 99%

Characterizing user mobility using mobile sensing systems

Faye

Bronzi

Tahirou

et al. 2017

International Journal of Distributed Sensor Networks

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show abstract

“…H Liu et al 35 propose a peer-assisted localization approach to eliminate the errors. This method needs other peer devices to participate and is not realistic to implement.…”

Section: Fingerprint-based Approachmentioning

confidence: 99%

Fingerprint-based Wi-Fi indoor localization using map and inertial sensors

Wang

Wei

Liu

et al. 2017

International Journal of Distributed Sensor Networks

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It is a common understanding that the localization accuracy can be improved by indoor maps and inertial sensors. However, there is a lack of concrete and generic solutions that combine these two features together and practically demonstrate its validity. This article aims to provide such a solution based on the mainstream fingerprint-based indoor localization approach. First, we introduce the theorem called reference points placement, which gives a theoretical guide to place reference points. Second, we design a Wi-Fi signal propagation-based cluster algorithm to reduce the amount of computation. The paper gives a parameter called reliability to overcome the skewing of inertial sensors. Then we also present Kalman filter and Markov chain to predict the system status. The system is able to provide high-accuracy real-time tracking by integrating indoor map and inertial sensors with Wi-Fi signal strength. Finally, the proposed work is evaluated and compared with the previous Wi-Fi indoor localization systems. In addition, the effect of inertial sensors' reliability is also discussed. Results are drawn from a campus office building which is about 80 m 3 140 m with 57 access points.

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“…The currently available indoor location systems, such as Wi-Fi [2,3], Radiofrequency Identification (RFID) [4][5][6], Ultra-WideBand (UWB) [7][8][9][10][11], Bluetooth [12][13][14], and Ultrasound [15,16], have achieved great progress in terms of transferring data within a short range, but when it comes to the indoor environment, these systems have various problems, including high cost, high power consumption, low accuracy, and low precision. The advent of Bluetooth Low Energy (BLE) provided opportunities to overcome the above problems.…”

Section: Introductionmentioning

confidence: 99%

A RSSI/PDR-Based Probabilistic Position Selection Algorithm with NLOS Identification for Indoor Localisation

Han

Xing

Deng

et al. 2018

IJGI

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Abstract:In recent years, location-based services have been receiving increasing attention because of their great development prospects. Researchers from all over the world have proposed many solutions for indoor positioning over the past several years. However, owing to the dynamic and complex nature of indoor environments, accurately and efficiently localising targets in indoor environments remains a challenging problem. In this paper, we propose a novel indoor positioning algorithm based on the received signal strength indication and pedestrian dead reckoning. In order to enhance the accuracy and reliability of our proposed probabilistic position selection algorithm in mixed line-of-sight (LOS) and non-line-of-sight (NLOS) environments, a low-complexity identification approach is proposed to identify the change in the channel situation between NLOS and LOS. Numerical experiment results indicate that our proposed algorithm has a higher accuracy and is less impacted by NLOS errors than other conventional methods in mixed LOS and NLOS indoor environments.

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Accurate WiFi Based Localization for Smartphones Using Peer Assistance

Cited by 135 publications

References 30 publications

Characterizing user mobility using mobile sensing systems

Characterizing user mobility using mobile sensing systems

Fingerprint-based Wi-Fi indoor localization using map and inertial sensors

A RSSI/PDR-Based Probabilistic Position Selection Algorithm with NLOS Identification for Indoor Localisation

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