Results 1 - 10
of
14
Fundamental Limits of RSS Fingerprinting based Indoor Localization
"... Abstract—Indoor localization has been an active research field for decades, where the received signal strength (RSS) fingerprinting based methodology is widely adopted and induces many important localization techniques such as the recently proposed one building the fingerprint database with crowd-so ..."
Abstract
-
Cited by 2 (0 self)
- Add to MetaCart
(Show Context)
Abstract—Indoor localization has been an active research field for decades, where the received signal strength (RSS) fingerprinting based methodology is widely adopted and induces many important localization techniques such as the recently proposed one building the fingerprint database with crowd-sourcing. While efforts have been dedicated to improve the accuracy and efficiency of localization, the fundamental limits of RSS fingerprinting based methodology itself is still unknown in a theoretical perspective. In this paper, we present a general probabilistic model to shed light on a fundamental question: how good the RSS fingerprinting based indoor localization can achieve? Concretely, we present the probability that a user can be localized in a region with certain size, given the RSS fingerprints submitted to the system. We reveal the interaction among the localization accuracy, the reliability of location estimation and the number of measurements in the RSS fingerprinting based location determination. Moreover, we present the optimal fingerprints reporting strategy that can achieve the best accuracy for given reliability and the number of measurements, which provides a design guideline for the RSS fingerprinting based indoor localization facilitated by crowdsourcing paradigm. I.
We Can Hear You with Wi-Fi!
"... Recent literature advances Wi-Fi signals to “see ” people’s motions and locations. This paper asks the following ques-tion: Can Wi-Fi “hear ” our talks? We present WiHear, which enables Wi-Fi signals to “hear ” our talks without de-ploying any devices. To achieve this, WiHear needs to de-tect and an ..."
Abstract
- Add to MetaCart
(Show Context)
Recent literature advances Wi-Fi signals to “see ” people’s motions and locations. This paper asks the following ques-tion: Can Wi-Fi “hear ” our talks? We present WiHear, which enables Wi-Fi signals to “hear ” our talks without de-ploying any devices. To achieve this, WiHear needs to de-tect and analyze fine-grained radio reflections from mouth movements. WiHear solves this micro-movement detection problem by introducingMouth Motion Profile that leverages partial multipath effects and wavelet packet transformation. Since Wi-Fi signals do not require line-of-sight, WiHear can “hear ” people talks within the radio range. Further, WiHear can simultaneously “hear”multiple people’s talks leveraging MIMO technology. We implement WiHear on both USRP N210 platform and commercial Wi-Fi infrastructure. Re-sults show that within our pre-defined vocabulary, WiHear can achieve detection accuracy of 91 % on average for single individual speaking no more than 6 words and up to 74% for no more than 3 people talking simultaneously. Moreover, the detection accuracy can be further improved by deploying multiple receivers from different angles.
Keystroke Recognition Using WiFi Signals
"... Keystroke privacy is critical for ensuring the security of com-puter systems and the privacy of human users as what being typed could be passwords or privacy sensitive information. In this paper, we show for the first time that WiFi signals can also be exploited to recognize keystrokes. The intuitio ..."
Abstract
- Add to MetaCart
Keystroke privacy is critical for ensuring the security of com-puter systems and the privacy of human users as what being typed could be passwords or privacy sensitive information. In this paper, we show for the first time that WiFi signals can also be exploited to recognize keystrokes. The intuition is that while typing a certain key, the hands and fingers of a user move in a unique formation and direction and thus gen-erate a unique pattern in the time-series of Channel State Information (CSI) values, which we call CSI-waveform for that key. In this paper, we propose a WiFi signal based keystroke recognition system called WiKey. WiKey consists of two Commercial Off-The-Shelf (COTS) WiFi devices, a sender (such as a router) and a receiver (such as a laptop). The sender continuously emits signals and the receiver con-tinuously receives signals. When a human subject types on a keyboard, WiKey recognizes the typed keys based on how the CSI values at the WiFi signal receiver end. We imple-mented the WiKey system using a TP-Link TL-WR1043ND WiFi router and a Lenovo X200 laptop. WiKey achieves more than 97.5 % detection rate for detecting the keystroke and 96.4 % recognition accuracy for classifying single keys. In real-world experiments, WiKey can recognize keystrokes in a continuously typed sentence with an accuracy of 93.5%.
Understanding and Modeling of WiFi Signal Based Human Activity Recognition
"... Some pioneer WiFi signal based human activity recognition sys-tems have been proposed. Their key limitation lies in the lack of a model that can quantitatively correlate CSI dynamics and human activities. In this paper, we propose CARM, a CSI based human Activity Recognition and Monitoring system. C ..."
Abstract
- Add to MetaCart
(Show Context)
Some pioneer WiFi signal based human activity recognition sys-tems have been proposed. Their key limitation lies in the lack of a model that can quantitatively correlate CSI dynamics and human activities. In this paper, we propose CARM, a CSI based human Activity Recognition and Monitoring system. CARM has two the-oretical underpinnings: a CSI-speed model, which quantifies the correlation between CSI value dynamics and human movement speeds, and a CSI-activity model, which quantifies the correlation between the movement speeds of different human body parts and a specific human activity. By these two models, we quantitatively build the correlation between CSI value dynamics and a specific human activity. CARM uses this correlation as the profiling mech-anism and recognizes a given activity by matching it to the best-fit profile. We implemented CARM using commercial WiFi devices and evaluated it in several different environments. Our results show that CARM achieves an average accuracy of greater than 96%.
Enhancing WiFi-based Localization with Visual Clues
"... Indoor localization is of great importance to a wide range of applications in the era of mobile computing. Current main-stream solutions rely on Received Signal Strength (RSS) of wireless signals as fingerprints to distinguish and infer loca-tions. However, those methods suffer from fingerprint am-b ..."
Abstract
- Add to MetaCart
(Show Context)
Indoor localization is of great importance to a wide range of applications in the era of mobile computing. Current main-stream solutions rely on Received Signal Strength (RSS) of wireless signals as fingerprints to distinguish and infer loca-tions. However, those methods suffer from fingerprint am-biguity that roots in multipath fading and temporal dynam-ics of wireless signals. Though pioneer efforts have resorted to motion-assisted or peer-assisted localization, they neither work in real time nor work without the help of peer users, which introduces extra costs and constraints, and thus de-grades their practicality. To get over these limitations, we propose Argus, an image-assisted localization system for mo-bile devices. The basic idea of Argus is to extract geometric constraints from crowdsourced photos, and to reduce finger-print ambiguity by mapping the constraints jointly against the fingerprint space. We devise techniques for photo selection, geometric constraint extraction, joint location estimation, and build a prototype that runs on commodity phones. Extensive experiments show that Argus triples the localization accuracy of classic RSS-based method, in time no longer than normal WiFi scanning, with negligible energy consumption.
Non-Invasive Detection of Moving and Stationary Human With WiFi
, 2014
"... Abstract—Non-invasive human sensing based on radio signals has attracted a great deal of research interest and fostered a broad range of innovative applications of localization, gesture recognition, smart health-care, etc., for which a primary primitive is to detect human presence. Previous works ha ..."
Abstract
- Add to MetaCart
(Show Context)
Abstract—Non-invasive human sensing based on radio signals has attracted a great deal of research interest and fostered a broad range of innovative applications of localization, gesture recognition, smart health-care, etc., for which a primary primitive is to detect human presence. Previous works have studied the detection of moving humans via signal variations caused by human movements. For stationary people, however, existing approaches often employ a prerequisite scenario-tailored calibration of chan-nel profile in human-free environments. Based on in-depth under-standing of human motion induced signal attenuation reflected by PHY layer channel state information (CSI), we propose DeMan, a unified scheme for non-invasive detection of moving and stationary human on commodity WiFi devices. DeMan takes advantage of both amplitude and phase information of CSI to detect moving targets. In addition, DeMan considers human breathing as an intrinsic indicator of stationary human presence and adopts so-phisticated mechanisms to detect particular signal patterns caused by minute chest motions, which could be destroyed by significant whole-body motion or hidden by environmental noises. By doing this, DeMan is capable of simultaneously detecting moving and stationary people with only a small number of prior measurements for model parameter determination, yet without the cumbersome scenario-specific calibration. Extensive experimental evaluation in typical indoor environments validates the great performance of DeMan in various human poses and locations and diverse channel conditions. Particularly, DeMan provides a detection rate of around 95 % for both moving and stationary people, while identifies human-free scenarios by 96%, all of which outperforms existing methods by about 30%. Index Terms—Non-invasive, human detection, calibration-free, human breathing, channel state information. I.
unknown title
"... Abstract—Device-free object tracking provides a promising solution for many localization and tracking systems to monitor non-cooperative objects, such as intruders, which do not carry any transceiver. However, existing device-free solutions mainly use special sensors or active RFID tags, which are m ..."
Abstract
- Add to MetaCart
(Show Context)
Abstract—Device-free object tracking provides a promising solution for many localization and tracking systems to monitor non-cooperative objects, such as intruders, which do not carry any transceiver. However, existing device-free solutions mainly use special sensors or active RFID tags, which are much more expensive compared to passive tags. In this paper, we propose a novel motion detection and tracking method using passive RFID tags, named Twins. The method leverages a newly observed phenomenon called critical state caused by interference among passive tags. We contribute to both theory and practice of this phenomenon by presenting a new interference model that precisely explains it and using extensive experiments to validate it. We design a practical Twins based intrusion detection system and implement a real prototype by commercial off-the-shelf RFID reader and tags. Experimental results show that Twins is effective in detecting the moving object, with very low location errors of 0.75m in average (with a deployment spacing of
We Can Hear You with Wi-Fi!
"... Recent literature advances Wi-Fi signals to “see ” people’s motions and locations. This paper asks the following ques-tion: Can Wi-Fi “hear ” our talks? We present WiHear, which enables Wi-Fi signals to “hear ” our talks without de-ploying any devices. To achieve this, WiHear needs to de-tect and an ..."
Abstract
- Add to MetaCart
(Show Context)
Recent literature advances Wi-Fi signals to “see ” people’s motions and locations. This paper asks the following ques-tion: Can Wi-Fi “hear ” our talks? We present WiHear, which enables Wi-Fi signals to “hear ” our talks without de-ploying any devices. To achieve this, WiHear needs to de-tect and analyze fine-grained radio reflections from mouth movements. WiHear solves this micro-movement detection problem by introducingMouth Motion Profile that leverages partial multipath effects and wavelet packet transformation. Since Wi-Fi signals do not require line-of-sight, WiHear can “hear ” people talks within the radio range. Further, WiHear can simultaneously “hear”multiple people’s talks leveraging MIMO technology. We implement WiHear on both USRP N210 platform and commercial Wi-Fi infrastructure. Re-sults show that within our pre-defined vocabulary, WiHear can achieve detection accuracy of 91 % on average for single individual speaking no more than 6 words and up to 74% for no more than 3 people talking simultaneously. Moreover, the detection accuracy can be further improved by deploying multiple receivers from different angles.
A Privacy-Preserving Fuzzy Localization Scheme with CSI Fingerprint
"... Abstract—CSI fingerprint localization is an advanced and promising technique for indoor localization, which identifies the user’s location by mapping his measured CSI against the server’s CSI fingerprint database. This approach is highlighted due to its high granularity for location distinction and ..."
Abstract
- Add to MetaCart
(Show Context)
Abstract—CSI fingerprint localization is an advanced and promising technique for indoor localization, which identifies the user’s location by mapping his measured CSI against the server’s CSI fingerprint database. This approach is highlighted due to its high granularity for location distinction and strong robustness to noise disturbances, but it also causes potential privacy leakage for the three participants in localization process: the user, the server, and the AP. Currently, there has been little research done on this issue, and the existing work often ignores the privacy concern on the AP. To fill the gap, this paper develops a privacy-preserving fuzzy localization scheme with CSI fingerprint. On one hand, it leverages the property of CSI training to guarantee the randomness and independence of the user’s measurement in each time of localization, and uses homomorphic encryption to achieve the data transmission and measurement comparison in cipher. These operations enable our scheme to preserve the location privacy of the user and APs as well as the data privacy of the server. On the other hand, the adoption of CSI fingerprint and fuzzy logic enhances the localization accuracy greatly. Through simulation experiments performed on CRAWDAD database, the efficiency of our proposed scheme is validated. I.
Sybil Attack Detection through TDOA-Based Localization Method in Wireless Sensor Network
"... Abstract: Wireless Sensor Networks are being widely used for large-scale real-time data processing due to its characteristics and is used in application areas such as in military and civilian domains. These networks are more prone to several security attacks. In this paper, we emphasized on Sybil at ..."
Abstract
- Add to MetaCart
(Show Context)
Abstract: Wireless Sensor Networks are being widely used for large-scale real-time data processing due to its characteristics and is used in application areas such as in military and civilian domains. These networks are more prone to several security attacks. In this paper, we emphasized on Sybil attack, which is a particularly harmful threat to WSNs and proposed an algorithm based on Time difference of Arrival (TDOA) localization method, which detect the malicious behavior of head node in a cluster based network.
