Accurate rogue access point localization leveraging fine-grained channel information

Zheng, Xiuyuan; Wang, Chen; Chen, Yingying; Yang, Jie

doi:10.1109/cns.2014.6997488

Cited by 18 publications

(4 citation statements)

References 22 publications

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“…Locating unknown Txs helps the administrators secure the network when security loopholes are detected or when there are intruders that breach the area managed by the administrators [2]. Also when administrators update their network infrastructure within an authorized area, a map of existing Tx locations helps to determine optimal locations for new Txs.…”

Section: Introductionmentioning

confidence: 99%

A Survey on Wireless Transmitter Localization Using Signal Strength Measurements

Nurminen

Dashti

Pich

2017

Wireless Communications and Mobile Computing

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Knowledge of deployed transmitters’ (Tx) locations in a wireless network improves many aspects of network management. Operators and building administrators are interested in locating unknown Txs for optimizing new Tx placement, detecting and removing unauthorized Txs, selecting the nearest Tx to offload traffic onto it, and constructing radio maps for indoor and outdoor navigation. This survey provides a comprehensive review of existing algorithms that estimate the location of a wireless Tx given a set of observations with the received signal strength indication. Algorithms that require the observations to be location-tagged are suitable for outdoor mapping or small-scale indoor mapping, while algorithms that allow most observations to be unlocated trade off some accuracy to enable large-scale crowdsourcing. This article presents empirical evaluation of the algorithms using numerical simulations and real-world Bluetooth Low Energy data.

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Section: Introductionmentioning

confidence: 99%

A Survey on Wireless Transmitter Localization Using Signal Strength Measurements

Nurminen

Dashti

Pich

2017

Wireless Communications and Mobile Computing

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“…Clearly, much work exists on voice radiation and in using acoustics for ranging [27], localization, motion tracking [21,24,38,49,50], etc. Radiation patterns (of antennas) have also been used for RF localization and sensing [7,15,18,25,34,[44][45][46][47]51], however, source signals are often known in such systems. Facing direction is difficult due to unknown source signals that vary slightly over adjacent facing directions.…”

Section: Related Workmentioning

confidence: 99%

Inferring Facing Direction from Voice Signals

Wei¹,

Li²,

Mehrotra³

et al. 2021

Preprint

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Consider a home or office where multiple devices are running voice assistants (e.g., TVs, lights, ovens, refrigerators, etc.). A human user turns to a particular device and gives a voice command, such as "Alexa, can you ... ". This paper focuses on the problem of detecting which device the user was facing, and therefore, enabling only that device to respond to the command. Our core intuition emerges from the fact that human voice exhibits a directional radiation pattern, and the orientation of this pattern should influence the signal received at each device. Unfortunately, indoor multipath, unknown user location, and unknown voice signals pose as critical hurdles. Through a new algorithm that estimates the line-of-sight (LoS) power from a given signal, and combined with beamforming and triangulation, we design a functional solution called CoDir. Results from 500+ configurations, across 5 rooms and 9 different users, are encouraging. While improvements are necessary, we believe this is an important step forward in a challenging but urgent problem space.

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“…In the case of a single AP, Splicer is unable to localize only with the TOF of DRL. In order to get the AOA of DRL, Aloc adopts the algorithm in Zheng et al 20 to calculate AOA. First, a mobile object stands at different positions around the AP.…”

Section: Implementation and Evaluationmentioning

confidence: 99%

Fine-grained wireless propagation ambience sensing

Jing

Sun

Wang

et al. 2018

International Journal of Distributed Sensor Networks

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The ubiquitous wireless network infrastructure and the need of people's indoor sensing inspire the work leveraging wireless signal into broad spectrum for indoor applications, including indoor localization, human-computer interaction, and activity recognition. To provide an accurate model selection or feature template, these applications take the system reliability of the signal in line-of-sight and non-line-of-sight propagation into account. Unfortunately, these two types of signal propagation are analyzed in static or mobile scenario separately. Our question is how to use the wireless signal to estimate the signal propagation ambience to facilitate the adaptive complex environment? In this paper, we exploit the Fresnel zone theory and channel state information (CSI) to model the static and mobile ambience detectors. Considering the spatiotemporal correlation of indoor activities, the propagation ambience can be divided into three categories: line-of-sight (LOS), non-line-of-sight (NLOS), and semi-line-of-sight (SLOS), which is used to represent the intermediate state between the LOS and NLOS propagation ambience during user movement. Leveraging the hidden Markov model to estimate the dynamic propagation ambience in the mobile environment, a novel propagation ambience identification method, named Ambience Sensor (Asor), is proposed to improve the real-time performance for the upper applications. Furthermore, Asor is integrated into a localization algorithm, Asor-based localization system (Aloc), to confirm the effectiveness. We prototype Asor and Aloc based on commodity WiFi infrastructure without any hardware modification. In addition, the real-time performance of Asor is evaluated by conducting tracking experiments. The experimental results show that the median detection rate of propagation ambience is superior to the existing methods in absence of any a priori hypothesis of static or mobile scenarios.

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Accurate rogue access point localization leveraging fine-grained channel information

Cited by 18 publications

References 22 publications

A Survey on Wireless Transmitter Localization Using Signal Strength Measurements

A Survey on Wireless Transmitter Localization Using Signal Strength Measurements

Inferring Facing Direction from Voice Signals

Fine-grained wireless propagation ambience sensing

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