Growing an organic indoor location system

Park, Jungeun; Charrow, Ben; Curtis, Dorothy; Battat, Jonathan; Minkov, Einat; Hicks, Jamey; Teller, Seth; Ledlie, Jonathan

doi:10.1145/1814433.1814461

Cited by 280 publications

(182 citation statements)

References 32 publications

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“…In the face of these challenges, authors in [103] use Voronoi regions for reasoning about gaps in coverage (i.e., areas with low density of fingerprints) and a clustering method for identifying potentially erroneous user data. They demonstrate rapid coverage while maintaining positioning accuracy comparable to that achieved with a professionally collected radiomap.…”

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

“…There are other works dedicated to mobile phone based indoor localization/tracking, e.g. Virtual Compass [5] and OIL [17]. Most of the exiting indoor tracking methods need a pre-knowledge or at least three anchors, and are infeasible to provide the realtime multi-user formation.…”

Section: Related Workmentioning

confidence: 99%

“…One category of existing methods are based on fingerprints, e.g., [4], [6], [17], [23], [28], which achieve room-level (meterlevel) accuracy. Those methods, however, are typically labor intensive and environment restrictive during fingerprint collection stage.…”

Section: Introductionmentioning

confidence: 99%

Montage: Combine frames with movement continuity for realtime multi-user tracking

Zhang

Liu

Jiang

et al. 2014

IEEE INFOCOM 2014 - IEEE Conference on Computer Communications

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Abstract-In this work we design and develop Montage for real-time multi-user formation tracking and localization by offthe-shelf smartphones. Montage achieves submeter-level tracking accuracy by integrating temporal and spatial constraints from user movement vector estimation and distance measuring. In Montage we designed a suite of novel techniques to surmount a variety of challenges in real-time tracking, without infrastructure and fingerprints, and without any a priori user-specific (e.g., stride-length and phone-placement) or site-specific (e.g., digitalized map) knowledge. We implemented, deployed and evaluated Montage in both outdoor and indoor environment. Our experimental results (847 traces from 15 users) show that the stridelength estimated by Montage over all users has error within 9cm, and the moving-direction estimated by Montage is within 20 o . For real-time tracking, Montage provides meter-second-level formation tracking accuracy with off-the-shelf mobile phones.

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“…In the second (operating) stage, the system identifies the stored Wi-Fi fingerprint most similar to current measurements, and returns the associated room. The first stage of our room localization technique is similar to that of Park et al (Park et al 2010). All users contribute their Wi-Fi RSS and room information to create a shared database of room fingerprints.…”

Section: Room Localizationmentioning

confidence: 99%

User‐Centric Indoor Air‐Quality Monitoring on Mobile Devices

Jiang

Piedrahita

et al. 2013

AI Magazine

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Since people spend a majority of their time indoors, indoor air quality (IAQ) can have a significant impact on human health, safety, productivity, and comfort. Due to the diversity and dynamics of people's indoor activities, it is important to monitor IAQ for each individual. Most existing air quality sensing systems are stationary or focus on outdoor air quality. In contrast, we propose MAQS, a user-centric mobile sensing system for IAQ monitoring. MAQS users carry portable, indoor location tracking and IAQ sensing devices that provide personalized IAQ information in real time. To improve accuracy and energy efficiency, MAQS incorporates three novel techniques: (1) an accurate temporal n-gram augmented Bayesian room localization method that requires few Wi-Fi fingerprints; (2) an air exchange rate based IAQ sensing method, which measures general IAQ using only CO$_2$ sensors; and (3) a zone-based proximity detection method for collaborative sensing, which saves energy and enables data sharing among users. MAQS has been deployed and evaluated via a real-world user study. This evaluation demonstrates that MAQS supports accurate personalized IAQ monitoring and quantitative analysis with high energy efficiency. We also found that study participants frequently experienced poor IAQ.

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Growing an organic indoor location system

Cited by 280 publications

References 32 publications

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

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

Montage: Combine frames with movement continuity for realtime multi-user tracking

User‐Centric Indoor Air‐Quality Monitoring on Mobile Devices

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