Locus: An Indoor Localization, Tracking and Navigation System for Multi-story Buildings Using Heuristics Derived from Wi-Fi Signal Strength

Bhargava, Preeti; Krishnamoorthy, Shivsubramani; Nakshathri, Aditya Karkada; Mah, Matthew; Agrawala, Ashok K.

doi:10.1007/978-3-642-40238-8_18

Cited by 13 publications

(13 citation statements)

References 3 publications

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“…The floor detection algorithm based on the reduced database employs Bayesian posterior probability of each floor and is reported to achieve 75% and 86% correct detection rates in two buildings. Going one step further, Locus is a heuristics based indoor localization, tracking and navigation system for multi-story buildings that determines floor and location by using the locations of infrastructure points, and without the need for radiomaps [153]. Initial experimental results in an indoor space spanning 175.000 ft 2 , show that Locus can determine the floor with 99.97% accuracy and the location with an average location error of 7 m.…”

Section: B Floor Identification With Wlanmentioning

confidence: 99%

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

Laoudias¹,

Moreira

Kim

et al. 2018

IEEE Commun. Surv. Tutorials

369

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: B Floor Identification With Wlanmentioning

confidence: 99%

“…• Vertical Positioning: Cellular-based solutions in academic and industrial research [138]- [143] as well as commercial 3D location systems [144]- [146], WLAN-based approaches [147]- [153], and sensor-based solutions [154]- [165].…”

mentioning

confidence: 99%

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

Laoudias¹,

Moreira

Kim

et al. 2018

IEEE Commun. Surv. Tutorials

369

171

View full text Add to dashboard Cite

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“…(7) are timestamps that can be obtained easily from reading local clocks. And r.h.s has the following expression: T DoA(U, 3, 2) − T DoA (1,3,2). Terms in the first parenthesis of Eq.…”

Section: Anchors Taking Turns Sending Rtsmentioning

confidence: 99%

“…(7) can be explained as T DoA(U, 3, 2), which stands for TDoA value between AP, 3 and 2. Similarly, second term is T DoA (1,3,2). If TDoA values among anchor nodes can be either estimated through time-based method, such as parts of trilateration and hyperbolic location stated in [13], or computed from physical measurements, TDoA values of UN can be derived without difficulty.…”

Section: Anchors Taking Turns Sending Rtsmentioning

confidence: 99%

“…In the noisy Wi-Fi environment, signal strength based approach has limitations in accuracy as well as high set-up cost as it often requires a development of signal strength fingerprinting for the area which may have to be repeated at regular intervals of time [3,17]. Time-based techniques determine the distance by measuring time of flight of the packets transmitted between nodes [13].…”

Section: Introductionmentioning

confidence: 99%

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WiFi Localization Based on IEEE 802.11 RTS/CTS Mechanism

Cui¹,

Agrawala²

2015

Proceedings of the 12th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services

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Location Based Services are providing one of the fastest growing market segments today. While the most common technique for location determination is GPS, several alternative approaches have been proposed for Wi-Fi environments, based on time of flight, signal strength, etc. Time based techniques not only require accurate timestamping mechanisms, but also precise and synchronized clocks, which is quite difficult and expensive in industry. On the other hand, signal strength based methods need a lot of ground truth data. These method also require time consuming work and efforts before the system comes into use. In considerations of costs and time consumption, we present in this paper an approach for determining the location of a general Wi-Fi device combining RTS/CTS and TDoA techniques. The proposed model is deployable in various environments and contains two different methods, with clock mapping functions and asynchronized clocks. We also explain limitations of current round trip time (RTT) based RTS/CTS systems. Extensive experiments have been conducted and demonstrated how an accuracy of about one foot can be obtained and also the assumption of RTT measurements have been verified.

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