Autonomous smartphone-based WiFi positioning system by using access points localization and crowdsourcing

Zhuang, Yuan; Syed, Zainab; Georgy, Jacques; El‐Sheimy, Naser

doi:10.1016/j.pmcj.2015.02.001

Cited by 100 publications

(50 citation statements)

References 24 publications

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“…Recently, autonomous crowdsourcing systems have been presented that rely on Trusted Portable Navigator 3 to build and update the databases for trilateration (i.e., AP locations and propagation parameters) and fingerprint matching WiFi positioning systems [101], [102]. These systems eliminate various limitations of current crowdsourcing systems such as the requirement for floor plan map or GPS, suitability to specific environments, and implementation of simple sensorbased navigation solutions.…”

Section: Radiomap Construction Through Crowdsourcingmentioning

confidence: 99%

A Survey of Enabling Technologies for Network Localization, Tracking, and Navigation

Laoudias¹,

Moreira

Kim

et al. 2018

IEEE Commun. Surv. Tutorials

354

171

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Abstract-Location information for events, assets, and individuals, mostly focusing on two dimensions so far, has triggered a multitude of applications across different verticals, such as consumer, networking, industrial, health care, public safety, and emergency response use cases. To fully exploit the potential of location awareness and enable new advanced location-based services, localization algorithms need to be combined with complementary technologies including accurate height estimation, i.e., three dimensional location, reliable user mobility classification, and efficient indoor mapping solutions. This survey provides a comprehensive review of such enabling technologies. In particular, we present cellular localization systems including recent results on 5G localization, and solutions based on Wireless Local Area Networks (WLAN), highlighting those that are capable of computing 3D location in multi-floor indoor environments. We overview range-free localization schemes, which have been traditionally explored in Wireless Sensor Networks (WSN) and are nowadays gaining attention for several envisioned Internet of Things (IoT) applications. We also present user mobility estimation techniques, particularly those applicable in cellular networks, that can improve localization and tracking accuracy. Regarding the mapping of physical space inside buildings for aiding tracking and navigation applications, we study recent advances and focus on smartphone-based indoor Simultaneous Localization and Mapping (SLAM) approaches.The survey concludes with service availability and system scalability considerations, as well as security and privacy concerns in location architectures, discusses the technology roadmap, and identifies future research directions.

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Section: Radiomap Construction Through Crowdsourcingmentioning

confidence: 99%

A Survey of Enabling Technologies for Network Localization, Tracking, and Navigation

Laoudias¹,

Moreira

Kim

et al. 2018

IEEE Commun. Surv. Tutorials

354

171

View full text Add to dashboard Cite

show abstract

“…With the development of Wi-Fi positioning technology, many Wi-Fi positioning systems have been proposed-from early systems such as RADAR [2] and Horus [3], to systems using advanced technology such as WILL [23] and Lifts [24]. These systems, including the various techniques and methods researchers have proposed, all view accuracy as the primary measuring rule of indoor positioning technology.…”

Section: Wi-fi Positioning Metricsmentioning

confidence: 99%

Indoor Fingerprint Positioning Based on Wi-Fi: An Overview

Xia

Liu

Yuan

et al. 2017

IJGI

195

125

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Abstract:The widely applied location-based services require a high standard for positioning technology. Currently, outdoor positioning has been a great success; however, indoor positioning technologies are in the early stages of development. Therefore, this paper provides an overview of indoor fingerprint positioning based on Wi-Fi. First, some indoor positioning technologies, especially the Wi-Fi fingerprint indoor positioning technology, are introduced and discussed. Second, some evaluation metrics and influence factors of indoor fingerprint positioning technologies based on Wi-Fi are introduced. Third, methods and algorithms of fingerprint indoor positioning technologies are analyzed, classified, and discussed. Fourth, some widely used assistive positioning technologies are described. Finally, conclusions are drawn and future possible research interests are discussed. It is hoped that this research will serve as a stepping stone for those interested in advancing indoor positioning.

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“…On the other hand, the demand for indoor navigation is quickly increasing in various applications including: health care monitoring, logistics, Location Based Services (LBS), emergency services, tourism, and people management [6]. Several different techniques have been developed for indoor navigation, such as WiFi [7,8], Bluetooth [9], near field communication (NFC) [10,11], radio-frequency identification (RFID) [12], and micro-electro-mechanical systems (MEMS) sensors [13,14]. To date, self-contained navigation systems based on MEMS sensors can be applied to different indoor applications, including mobile robot navigation [15] and pedestrian navigation [16].…”

Section: Introductionmentioning

confidence: 99%

“…Iron materials and electronic devices indoors can easily disturb magnetometers. On the other hand, most public buildings such as universities, colleges, airports, shopping malls, and office buildings already have well-established WiFi infrastructure [7], making WiFi the main aiding resource for INS and PDR indoors.…”

Section: Introductionmentioning

confidence: 99%

PDR/INS/WiFi Integration Based on Handheld Devices for Indoor Pedestrian Navigation

et al. 2015

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Abstract:Providing an accurate and practical navigation solution anywhere with portable devices, such as smartphones, is still a challenge, especially in environments where global navigation satellite systems (GNSS) signals are not available or are degraded. This paper proposes a new algorithm that integrates inertial navigation system (INS) and pedestrian dead reckoning (PDR) to combine the advantages of both mechanizations for micro-electro-mechanical systems (MEMS) sensors in pedestrian navigation applications. In this PDR/INS integration algorithm, a pseudo-velocity-vector, which is composed of the PDR-derived forward speed and zero lateral and vertical speeds from non-holonomic constraints (NHC), works as an update for the INS to limit the velocity errors. To further limit the drift of MEMS inertial sensors, trilateration-based WiFi positions with small variances are also selected as updates for the PDR/INS integrated system. The experiments illustrate that positioning error is decreased by 60%-75% by using the proposed PDR/INS integrated MEMS solution when compared with PDR. The positioning error is further decreased by 15%-55% if the proposed PDR/INS/WiFi integrated solution is implemented. The average accuracy of the proposed PDR/INS/WiFi integration algorithm achieves 4.5 m in indoor environments.

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Autonomous smartphone-based WiFi positioning system by using access points localization and crowdsourcing

Cited by 100 publications

References 24 publications

A Survey of Enabling Technologies for Network Localization, Tracking, and Navigation

A Survey of Enabling Technologies for Network Localization, Tracking, and Navigation

Indoor Fingerprint Positioning Based on Wi-Fi: An Overview

PDR/INS/WiFi Integration Based on Handheld Devices for Indoor Pedestrian Navigation

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