Secure Crowdsourced Indoor Positioning Systems

Li, Tao; Chen, Yimin; Zhang, Rui; Zhang, Yanchao; Hedgpeth, Terri

doi:10.1109/infocom.2018.8486398

Cited by 29 publications

(16 citation statements)

References 19 publications

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“…In this section, we proposed the budget feasible reverse auction, which was conducted in each edge cloud, to solve the BFQO problem defined in Formula (2) and Formula (3).…”

Section: Budget Feasible Reverse Auctionmentioning

confidence: 99%

“…With probability 3 5 (Lines 5-20), the reverse auction consists of the winner selection and payment determination phase. In the winner selection phase, we processed each user i ∈ S * k \S k iteratively according its effective marginal quality…”

Section: Proof According To Definition 1 We Needed To Show Thatmentioning

confidence: 99%

“…Mobile crowdsensing is a human driven activity that leverages pervasiveness of wireless connectivity, various mobile devices with built-in sensing capabilities, and inherent user mobility to create dense and dynamic data sets, which can effectively characterize our environments. Mobile crowdsensing has become an efficient approach to data acquisition in large-scale sensing applications, such as photo selection [1], public bike trip selection [2] and indoor positioning systems [3].…”

Section: Introductionmentioning

confidence: 99%

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Incentivizing for Truth Discovery in Edge-assisted Large-scale Mobile Crowdsensing

Yang

Lü

et al. 2020

Sensors

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The recent development of human-carried mobile devices has promoted the great development of mobile crowdsensing systems. Most existing mobile crowdsensing systems depend on the crowdsensing service of the deep cloud. With the increasing scale and complexity, there is a tendency to enhance mobile crowdsensing with the edge computing paradigm to reduce latency and computational complexity, and improve the expandability and security. In this paper, we propose an integrated solution to stimulate the strategic users to contribute more for truth discovery in the edge-assisted mobile crowdsensing. We design an incentive mechanism consisting of truth discovery stage and budget feasible reverse auction stage. In truth discovery stage, we estimate the truth for each task in both deep cloud and edge cloud. In budget feasible reverse auction stage, we design a greedy algorithm to select the winners to maximize the quality function under the budget constraint. Through extensive simulations, we demonstrate that the proposed mechanism is computationally efficient, individually rational, truthful, budget feasible and constant approximate. Moreover, the proposed mechanism shows great superiority in terms of estimation precision and expandability.

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“…In this section, we proposed the budget feasible reverse auction, which was conducted in each edge cloud, to solve the BFQO problem defined in Formula (2) and Formula (3).…”

Section: Budget Feasible Reverse Auctionmentioning

confidence: 99%

Section: Proof According To Definition 1 We Needed To Show Thatmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Incentivizing for Truth Discovery in Edge-assisted Large-scale Mobile Crowdsensing

Yang

Lü

et al. 2020

Sensors

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show abstract

“…Specially, for crowd-sourcing based indoor localization, fingerprints are from different devices at different times, which greatly exposes the security problem. For attacker models, there are three general scenarios for RSS fingerprinting based localization [57]. First, the attacker does not know the true RSS fingerprints and injects fake RSS data at random.…”

Section: Secure Indoor Localization Using Deep Learningmentioning

confidence: 99%

Deep learning for indoor localization based on bimodal CSI data

Wang

Mao

2019

Applications of Machine Learning in Wireless Communications

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In this chapter, we incorporate deep learning for indoor localization based on channel state information with commodity 5GHz WiFi. We first introduce the state-of-the-art deep learning techniques including deep autoencoder network, convolutional neural network, and recurrent neural network. We then present a deep learning based algorithm to leverage bi-modal CSI data, i.e., average amplitudes and estimated angle of arrivals (AOA), in both offline and online stages of fingerprinting. The proposed scheme is validated with extensive experiments. Finally, we examine several open research problems for indoor localization based on deep learning techniques.

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“…Indoor localization is essential to enable location based services (LBS) such as indoor navigation [27,35,37,84], health rehabilitation [14,45,49,54] and human-computer interaction (HCI) devices [47,70,83,85]. Aiming at higher accuracy, shorter time latency and lower infrastructure requirement, many localization systems have been designed for various scenarios.…”

Section: Introductionmentioning

confidence: 99%

Indoor acoustic localization: a survey

Liu

Cheng

Qian

et al. 2020

Hum. Cent. Comput. Inf. Sci.

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Applications of localization range from body tracking, gesture capturing, indoor plan construction to mobile health sensing. Technologies such as inertial sensors, radio frequency signals and cameras have been deeply excavated to locate targets. Among all the technologies, the acoustic signal gains enormous favor considering its comparatively high accuracy with common infrastructure and low time latency. Rangebased localization falls into two categories: absolute range and relative range. Different mechanisms, such as Time of Flight, Doppler effect and phase shift, are widely studied to achieve the two genres of localization. The subcategories show distinguishing features but also face diverse challenges. In this survey, we present a comprehensive overview on various indoor localization systems derived from the various mechanisms. We also discuss the remaining issues and the future work.

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Secure Crowdsourced Indoor Positioning Systems

Cited by 29 publications

References 19 publications

Incentivizing for Truth Discovery in Edge-assisted Large-scale Mobile Crowdsensing

Incentivizing for Truth Discovery in Edge-assisted Large-scale Mobile Crowdsensing

Deep learning for indoor localization based on bimodal CSI data

Indoor acoustic localization: a survey

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