Regression-Based Estimation of Individual Errors in Fingerprinting Localization

Lemić, Filip; Handziski, Vlado; Aernouts, Michiel; Janssen, Thomas; Berkvens, Rafael; Wolisz, Adam; Famaey, Jeroen

doi:10.1109/access.2019.2903880

Cited by 37 publications

(53 citation statements)

References 32 publications

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“…This is because, in contrast to regression, ANNs has a substantially larger number of tunable hyperparameters, which enables their optimization and fine-grained tuning for the problem at hand. More details on the proposed methods and their performance results can be found in our previous work [25,26].…”

Section: Location Estimation Reliabilitymentioning

confidence: 99%

A Multimodal Localization Framework Design for IoT Applications

Aernouts

Lemić

Moons

et al. 2020

Sensors

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Multiple Radio Access Technology (multi-RAT) communication with Low Power Wide Area Networks (LPWAN) significantly increases the flexibility of Internet of Things (IoT) applications. Location-based services that build upon such a multimodal communication architecture are able to switch to an optimal localization method depending on the constraints of the active wireless technology. Furthermore, the resulting location estimate can aid location-based handover mechanisms to reduce the energy consumption of a multi-RAT IoT device. In this research, we present our design of a multimodal localization framework and illustrate the benefit of such a framework with two IoT use case examples. For the first use case, valuable artwork is tracked during transportation to a museum. In the second use case, we monitor the usage and location of large construction tools. Finally, we propose how our localization framework can be improved to deal with implementation challenges and to reduce location estimation errors.

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Section: Location Estimation Reliabilitymentioning

confidence: 99%

A Multimodal Localization Framework Design for IoT Applications

Aernouts

Lemić

Moons

et al. 2020

Sensors

Self Cite

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show abstract

“…Combining two radios (e.g. LoRa or NB-IoT) on the same chip would increase the weight while giving poor localisation accuracy ( 300m error; [29,30]). LoRa and NB-IoT (two similar forms of long range low power wireless systems) have limited global coverage, particularly in Sub-saharan Africa where this system was originally designed to be used.…”

Section: Introductionmentioning

confidence: 99%

ProxLogs: Miniaturised proximity loggers for monitoring association behaviour in small mammals

Kirkpatrick

Hererra-Olivares

Sabuni

et al. 2021

Preprint

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The ability to monitor associations between wild animals is essential for understanding the processes governing gene transfer, information transfer, competition, predation and disease transmission.Until recently, such insights have been confined to large, visible or captive animals. However, the rapid development of miniature sensors for consumer electronics is allowing ecologists to monitor the natural world in ways previously considered impossible.Here we describe miniature (<1g) proximity loggers we have developed that use Bluetooth Low Energy transmission to register contacts between individuals. Our loggers are open source, low cost, rechargeable, able to store up to 2000 contacts, can be programmed in situ and can download data remotely or through a mobile phone application, increasing their utility in remote areas or with species which are challenging to recapture.We successfully trialled our loggers in a range of field realistic conditions, demonstrating that Bluetooth Low Energy is capable of logging associations in structurally complex habitats, and that changes in received signal strength can be equated to short range changes in distance between loggers. Furthermore, we tested the system on starlings (Sturnidae vulgaris).The ability to include other sensors is retained in our prototypes, allowing for the potential integration of physiological and behavioural inference into social networks derived from our approach. Due to its open source nature, small size, flexibility of use and the active research currently being undertaken with Bluetooth Low Energy, we believe that our approach is a valuable addition to the biologging toolkit.

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“…Both closed-form solutions and iterative algorithms were investigated for the line-of-sight (LOS) environment [4]- [9]. For non-line-of-sight (NLOS) propagation, geometric constraint conditions [10]- [12] and machine learning theory [13]- [16] were developed to mitigate NLOS error. Most of these studies are based on a single path of measurements.…”

Section: Introductionmentioning

confidence: 99%

Connectivity-Based Localization in Ultra-Dense Networks: CRLB, Theoretical Variance, and MLE

Huang

Wang

et al. 2020

IEEE Access

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Performance analysis of connectivity-based geolocation in ultra-dense networks (UDNs) is a very important task. Although several performance analyses have been presented for range-free localization, determining the best achievable positioning accuracy of range-free localization remains an open problem. In this paper, we first derive the Cramer-Rao lower bound (CRLB) for the performance evaluation of range-free localization. All the current performance analyses in the literature for range-free localization are used to evaluate the real performance of a given algorithm, whereas the proposed CRLB provides a benchmark to evaluate the performance of any unbiased range-free location algorithm and determines the physical impossibility of the variance of an unbiased estimator being less than the bound. To the best of our knowledge, this is the first time in the literature that the CRLB for range-free localization has been derived. Second, the theoretical variance of centroid-based localization (CL) with an arbitrary node distribution is derived in this paper. In contrast to the existing theoretical variance of CL for uniform node distribution, the proposed theoretical variance can be used to evaluate the performance of CL in the case of an arbitrary node distribution. Additionally, characteristics of the proposed CRLB and theoretical variance are given in this paper. Finally, an optimal estimator based on a maximum likelihood estimator (MLE) is proposed to improve positioning accuracy. Since both prior information on the spatial node distribution and the connectivity property are effectively utilized in our algorithm, the proposed method performs better than the CL method and can asymptotically attain the CRLB. INDEX TERMS Connectivity-based localization, Cramer-Rao lower bound (CRLB), theoretical variance, maximum likelihood estimator (MLE), centroid-based localization (CL).

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Regression-Based Estimation of Individual Errors in Fingerprinting Localization

Cited by 37 publications

References 32 publications

A Multimodal Localization Framework Design for IoT Applications

A Multimodal Localization Framework Design for IoT Applications

ProxLogs: Miniaturised proximity loggers for monitoring association behaviour in small mammals

Connectivity-Based Localization in Ultra-Dense Networks: CRLB, Theoretical Variance, and MLE

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