Semantic wireless localization of WiFi terminals in smart buildings

Ahmadi, Hanen; Polo, Alessandro; Moriyama, Toshifumi; Salucci, Marco; Viani, Federico

doi:10.1002/2016rs006008

Cited by 20 publications

(10 citation statements)

References 39 publications

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“…If all the SFI used in (20) are Gaussian with mean θ i,j , then the ML estimate leads to LS or WLS estimates as in the noncooperative case.…”

Section: B Si-based Localization With Spatial Cooperationmentioning

confidence: 99%

“…Location awareness enables numerous wireless applications that rely on information associated with the positions of nodes such as anchors, agents, and targets in wireless networks [1]- [5]. These applications include autonomy [6]- [10], crowd sensing [11]- [19], smart environments [20]- [25], assets tracking [26]- [30], and the Internet-of-Things (IoT) [31]- [36]. The process of locating, tracking, and navigating any possible collaborative or non-collaborative nodes (devices, objects, people, and vehicles) is referred to as Localization-of-Things (LoT).…”

Section: Introductionmentioning

confidence: 99%

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Soft Information for Localization-of-Things

et al. 2019

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“…If all the SFI used in (20) are Gaussian with mean θ i,j , then the ML estimate leads to LS or WLS estimates as in the noncooperative case.…”

Section: B Si-based Localization With Spatial Cooperationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Soft Information for Localization-of-Things

et al. 2019

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“…However, the inhomogeneous data structure is a big challenge to federated learning. • Another interesting direction is to exploit semantic information for indoor wireless localization [155], [156]. For example, in [155], authors improved the calibration of indoor wireless propagation models with the aid of the semantic target-environment relation information.…”

Section: Future Directions and Challengesmentioning

confidence: 99%

“…• Another interesting direction is to exploit semantic information for indoor wireless localization [155], [156]. For example, in [155], authors improved the calibration of indoor wireless propagation models with the aid of the semantic target-environment relation information. This combination, in an unsupervised and automatic way, is considered to be a promising solution when dealing with a complex indoor environment.…”

Section: Future Directions and Challengesmentioning

confidence: 99%

FedLoc: Federated Learning Framework for Data-Driven Cooperative Localization and Location Data Processing

Yin

Lin

Kong

et al. 2020

IEEE Open J. Signal Process.

181

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In this overview paper, data-driven learning model-based cooperative localization and location data processing are considered, in line with the emerging machine learning and big data methods. We first review (1) state-of-the-art algorithms in the context of federated learning, (2) two widely used learning models, namely the deep neural network model and the Gaussian process model, and (3) various distributed model hyper-parameter optimization schemes. Then, we demonstrate various practical use cases that are summarized from a mixture of standard, newly published, and unpublished works, which cover a broad range of location services, including collaborative static localization/fingerprinting, indoor target tracking, outdoor navigation using low-sampling GPS, and spatio-temporal wireless traffic data modeling and prediction. Experimental results show that near centralized data fitting-and prediction performance can be achieved by a set of collaborative mobile users running distributed algorithms. All the surveyed use cases fall under our newly proposed Federated Localization (FedLoc) framework, which targets on collaboratively building accurate location services without sacrificing user privacy, in particular, sensitive information related to their geographical trajectories. Future research directions are also discussed at the end of this paper.

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“…Several detection technologies have been investigated including wireless sensor networks (WSNs) [3]- [7], ultra-wideband (UWB) [8], radio frequency identification (RFID) [8], and wireless local area networks (WLANs) [10].…”

Section: Introductionmentioning

confidence: 99%

Indoor detection of passive targets recast as an inverse scattering problem

Gottardi

Moriyama

2017

J. Phys.: Conf. Ser.

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Abstract. The wireless local area networks represent an alternative to custom sensors and dedicated surveillance systems for target indoor detection. The availability of the channel state information has opened the exploitation of the spatial and frequency diversity given by the orthogonal frequency division multiplexing. Such a fine-grained information can be used to solve the detection problem as an inverse scattering problem. The goal of the detection is to reconstruct the properties of the investigation domain, namely to estimate if the domain is empty or occupied by targets, starting from the measurement of the electromagnetic perturbation of the wireless channel. An innovative inversion strategy exploiting both the frequency and the spatial diversity of the channel state information is proposed. The targetdependent features are identified combining the Kruskal-Wallis test and the principal component analysis. The experimental validation points out the detection performance of the proposed method when applied to an existing wireless link of a WiFi architecture deployed in a real indoor scenario. False detection rates lower than 2 [%] have been obtained. Recently, the channel state information (CSI) used by WLANs in the framework of orthogonal frequency division multiplexing (OFDM) has been exploited to enable location-based services. The CSI is a fine-grained feature of the channel frequency response (CFR) providing both magnitude and phase information at multiple frequencies [11]. This indicator is highly sensitive to the environmental changes and its information content can be exploited to characterize the target signature on the electromagnetic field. The multiple-input multiple output (MIMO) architecture has been introduced by the current WLAN standards to further improve the quality of the data transmission. The intrinsic spatial diversity of a MIMO system has been also exploited to enhance the performance of the wireless localization problem. IntroductionMany techniques exploiting the prominent features of the CSI exist in the state of the art, but the indoor scenario and the system setups are often dedicated to the localization itself. On the contrary, the proposed technique aims to introduce the detection capability on top of an existing wireless architecture already used for communication.

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Semantic wireless localization of WiFi terminals in smart buildings

Cited by 20 publications

References 39 publications

Soft Information for Localization-of-Things

Soft Information for Localization-of-Things

FedLoc: Federated Learning Framework for Data-Driven Cooperative Localization and Location Data Processing

Indoor detection of passive targets recast as an inverse scattering problem

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