Indoor Positioning and Fingerprinting: The R Package ipft

Sansano-Sansano, Emilio; Montoliu, Raúl; Belmonte, Óscar; Torres-Sospedra, Joaquín

doi:10.32614/rj-2019-010

Cited by 7 publications

(4 citation statements)

References 27 publications

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“…This method estimates the location of the test fingerprint by averaging the known locations of the K-nearest fingerprints in the radio map. KNN (k = 3) classification was performed using the ‘ipfKnn’ and ‘ipfEstimate’ functions from the ipft package in R (Sansano et al, 2019). As we had no prior expectation of the ideal choice of k and exploratory analysis suggested that different values did not impact the localisation accuracy, we used the default value (3) from the ‘ipfKnn’ function and the Euclidean distance between fingerprints was calculated to determine similarity.…”

Section: Methodsmentioning

confidence: 99%

Fingerprint localisation for fine-scale wildlife tracking using automated radio telemetry

Tyson,

Fragueira,

Sansano-Sansano

et al. 2024

Preprint

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1: Automated radio telemetry systems are among the most widely applicable method for tracking wildlife at fine spatiotemporal scales. These systems are increasingly applying large networks of simple receivers to detect radio signal strength (RSS) from radio tagged individuals. Analytical methods to derive position estimates using RSS localisation, however, are relatively underdeveloped in the field of wildlife tracking. Here, we apply approaches from indoor positioning systems to develop a new method, radio fingerprinting, for localizing radio tagged animals in structurally complex, outdoor environments. This method characterizes the RSS patterns at known locations to generate a radio map of the study area, which is then used to predict the location of new RSS patterns. 2: We conducted field tests to evaluate the localisation accuracy of radio fingerprinting relative to multilateration, a commonly used method for RSS localisation. To do so, we established an experimental receiver network covering multiple habitat types and compared the localisation accuracy of radio fingerprinting to multilateration. Additionally, we evaluated how a variety of features characteristic of typical tracking datasets affected the accuracy of both methods. 3: While both methods had a similar median error (~30 m), the fingerprint localisation method offered several advantages over multilateration. Multilateration localisation estimates were highly affected by missed detections from the nearest receiver to the test point, which occurred in 30% of cases. In these cases, the median error was 97 m, over a 3-fold increase. Distance to the nearest receiver also biased multilateration estimates with error increasing as the distance increased. Additionally, errors from multilateration estimates were higher in more densely vegetated areas. In contrast, fingerprinting position estimates were largely robust to each of these scenarios. 4: Automated radio telemetry enables the fine-scale, continuous tracking of range-resident animals. We present radio fingerprinting as a localisation method in outdoor environments where standard localisation methods may be inadequate. This approach can be applied to any automated radio telemetry hardware and to any study system where a radio map can be generated.

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Section: Methodsmentioning

confidence: 99%

Fingerprint localisation for fine-scale wildlife tracking using automated radio telemetry

Tyson,

Fragueira,

Sansano-Sansano

et al. 2024

Preprint

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“…In the fingerprintbased method, a specific location P(x, y) is assigned to a distinct fingerprint. Any information helping to distinguish a location can be used as a fingerprint; e.g., if every location has a unique temperature, this temperature may be considered as a fingerprint (Sansano, E. et al [43]). Normally, RSSI values are usually used as fingerprints in engineering.…”

Section: Related Workmentioning

confidence: 99%

A Sustainable, Interactive Elderly Healthcare System for Nursing Homes: An Interdisciplinary Design

Yang

Wang

et al. 2022

Sustainability

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Population ageing becomes a perplexing conundrum with social and economic development. Many senior citizens are now empty nesters because the younger generation prefer to stay in metropolises for a better life. Therefore, living in a nursing home is a popular choice for the aged. This objective-oriented paper proposes a sustainable elderly healthcare system for nursing homes. The main work is the design and implementation of a new rapid and interactive assistance service. Based on cost-effective fingerprint indoor-positioning technology, the alert message that a person is at risk will be immediately sent to nearby people before professionals arrive. Warning messages are available when nearing marked areas (e.g., slippery floors). The parallel path-finding algorithm plays a significant role in finding nearby people and alerting people who approach specific areas. Furthermore, this system provides application programming interfaces to connect to health devices, such as smart bracelets, watches, and glasses. In general, the system is designed to ensure the safety of the elderly and improve management efficiency, which corresponds to present smart elderly care proposals from governments.

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“…This is a technology that has already awakened but that in a short period of time will suffer a big explosion, as happened with the systems of positioning by satellite in exteriors and its applications. This paper introduces the R package ipft (Sansano, 2017), a collection of algorithms and utility functions to create models, make estimations, analyze and manipulate RSSI fingerprint data sets for indoor positioning. Given the abundance of potential applications for indoor positioning, the package may have a broad relevance in fields such as pervasive computing, Internet of Things (IoT) or healthcare, among many others.…”

Section: Introductionmentioning

confidence: 99%

Indoor Positioning and Fingerprinting: The R Package ipft

Sansano-Sansano¹,

Montoliu²,

Belmonte³

et al. 2019

The R Journal

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Methods based on Received Signal Strength Indicator (RSSI) fingerprinting are in the forefront among several techniques being proposed for indoor positioning. This paper introduces the R package ipft, which provides algorithms and utility functions for indoor positioning using fingerprinting techniques. These functions are designed for manipulation of RSSI fingerprint data sets, estimation of positions, comparison of the performance of different positioning models, and graphical visualization of data. Well-known machine learning algorithms are implemented in this package to perform analysis and estimations over RSSI data sets. The paper provides a description of these algorithms and functions, as well as examples of its use with real data. The ipft package provides a base that we hope to grow into a comprehensive library of fingerprinting-based indoor positioning methodologies.

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Indoor Positioning and Fingerprinting: The R Package ipft

Cited by 7 publications

References 27 publications

Fingerprint localisation for fine-scale wildlife tracking using automated radio telemetry

Fingerprint localisation for fine-scale wildlife tracking using automated radio telemetry

A Sustainable, Interactive Elderly Healthcare System for Nursing Homes: An Interdisciplinary Design

Indoor Positioning and Fingerprinting: The R Package ipft

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