Anyplace: A Crowdsourced Indoor Information Service

Georgiou, Kyriakos; Constambeys, Timotheos; Laoudias, Christos; Petrou, Lambros; Chatzimilioudis, Georgios; Zeinalipour-Yazti, Demetrios

doi:10.1109/mdm.2015.80

Cited by 24 publications

(17 citation statements)

References 3 publications

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“…Differential, instead of absolute, RSS values have been explored for fusing crowdsourced RSS data collected with heterogeneous devices to make the resulting radiomap completely device independent [108]. The Anyplace system [109], [110] uses the concept of RSS differences for crowdsourcing the radiomap, while it guides crowdsourcers to uncovered or low fingerprint density regions through heatmaps that visualize the volume of data collected in different areas or rooms.…”

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

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355

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

Self Cite

355

171

View full text Add to dashboard Cite

show abstract

“…Several indoor positioning platforms have been proposed in recent years that have the potential to take into account IoT requirements and could be thus adopted within the IoT context. They include both large scale, enterprise range platforms supported by big Information Technology (IT) players and vendors [21,22], and smaller ones proposed by startups and/or emerging from academic research [23][24][25]. Among enterprise grade platforms, MazeMap [21] focuses on indoor maps, but offers a wide range of services, including indoor positioning, although details on how the positioning platform is designed are not available on the company website.…”

Section: Indoor Positioning Platformsmentioning

confidence: 99%

“…Platforms designed and released by startups and academia are however often characterized by an open-source approach: a notable example is AnyPlace [23] that makes source code available on GitHub under the MIT open-source license. AnyPlace currently provides the code for the positioning server and apps for Android and Windows Phone devices.…”

Section: Indoor Positioning Platformsmentioning

confidence: 99%

ThingsLocate: A ThingSpeak-Based Indoor Positioning Platform for Academic Research on Location-Aware Internet of Things

2019

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Seamless location awareness is considered a cornerstone in the successful deployment of the Internet of Things (IoT). Support for IoT devices in indoor positioning platforms and, vice versa, availability of indoor positioning functions in IoT platforms, are however still in their early stages, posing a significant challenge in the study and research of the interaction of indoor positioning and IoT. This paper proposes a new indoor positioning platform, called ThingsLocate, that fills this gap by building upon the popular and flexible ThingSpeak cloud service for IoT, leveraging its data input and data processing capabilities and, most importantly, its native support for cloud execution of Matlab code. ThingsLocate provides a flexible, user-friendly WiFi fingerprinting indoor positioning service for IoT devices, based on Received Signal Strength Indicator (RSSI) information. The key components of ThingsLocate are introduced and described: RSSI channels used by IoT devices to provide WiFi RSSI data, an Analysis app estimating the position of the device, and a Location channel to publish such estimate. A proof-of-concept implementation of ThingsLocate is then introduced, and used to show the possibilities offered by the platform in the context of graduate studies and academic research on indoor positioning for IoT. Results of an experiment enabled by ThingsLocate with limited setup and no coding effort are presented, focusing on the impact of using different devices and different positioning algorithms on positioning accuracy.

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“…Satellite positioning is ubiquitously available but has an expensive energy tag, may be negatively affected by the environment (cloudy days, forests, downtown areas), and is not available indoors. The localization research community has proposed numerous alternative solutions, including Infrared, Bluetooth [6], [7], Wireless LANs [8], [9], [10], Ambient Magnetic Field [11], Artificial Quasistatic Electromagnetic Field [12], Visual and Acoustic Analysis [13], Inertial Measurement Units (IMU) [14], Ultra-Wide-Band (UWB), and Sensor Networks, and their combinations in hybrid systems [15].…”

Section: A Indoor Localizationmentioning

confidence: 99%

“…where | • | denotes a determinant of a square matrix. Plugging (10) into (8) results in the following expression for the FIM:…”

Section: Appendixmentioning

confidence: 99%

Indoor Localization Accuracy Estimation from Fingerprint Data

Nikitin

Laoudias

Chatzimilioudis

et al. 2017

2017 18th IEEE International Conference on Mobile Data Management (MDM)

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The demand for indoor localization services has led to the development of techniques that create a Fingerprint Map (FM) of sensor signals (e.g., magnetic, Wi-Fi, bluetooth) at designated positions in an indoor space and then use FM as a reference for subsequent localization tasks. With such an approach, it is crucial to assess the quality of the FM before deployment, in a manner disregarding data origin and at any location of interest, so as to provide deployment staff with the information on the quality of localization. Even though FM-based localization algorithms usually provide accuracy estimates during system operation (e.g., visualized as uncertainty circle or ellipse around the user location), they do not provide any information about the expected accuracy before the actual deployment of the localization service. In this paper, we develop a novel framework for quality assessment on arbitrary FMs coined ACCES. Our framework comprises a generic interpolation method using Gaussian Processes (GP), upon which a navigability score at any location is derived using the Cramer-Rao Lower Bound (CRLB). Our approach does not rely on the underlying physical model of the fingerprint data. Our extensive experimental study with magnetic FMs, comparing empirical localization accuracy against derived bounds, demonstrates that the navigability score closely matches the accuracy variations users experience.

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Anyplace: A Crowdsourced Indoor Information Service

Cited by 24 publications

References 3 publications

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

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

ThingsLocate: A ThingSpeak-Based Indoor Positioning Platform for Academic Research on Location-Aware Internet of Things

Indoor Localization Accuracy Estimation from Fingerprint Data

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