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62
IODetector: A generic service for indoor outdoor detection
- In SenSys’ 12
"... The location and context switching, especially the indoor/outdoor switching, provides essential and primitive information for upper layer mobile applications. In this paper, we present IODetector: a lightweight sensing service which runs on the mobile phone and detects the indoor/outdoor environment ..."
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Cited by 19 (5 self)
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The location and context switching, especially the indoor/outdoor switching, provides essential and primitive information for upper layer mobile applications. In this paper, we present IODetector: a lightweight sensing service which runs on the mobile phone and detects the indoor/outdoor environment in a fast, accurate, and efficient manner. Constrained by the energy budget, IODetector leverages primarily lightweight sensing resources including light sensors, magnetism sensors, celltower signals, etc. For universal applicability, IODetector assumes no prior knowledge (e.g., fingerprints) of the environment and uses only on-board sensors common to mainstream mobile phones. Being a generic and lightweight service component, IODetector greatly benefits many location-based and context-aware applications. We prototype the IODetector on Android mobile phones and evaluate the system comprehensively with data collected from 19 traces which include 84 different places during one month period, employing different phone models. We further perform a case study where we make use of IODetector to instantly infer the GPS availability and localization accuracy in different indoor/outdoor environments.
Walkie-Markie: Indoor Pathway Mapping Made Easy
"... We present Walkie-Markie – an indoor pathway mapping system that can automatically reconstruct internal pathway maps of buildings without any a-priori knowledge about the building, such as the floor plan or access point locations. Central to Walkie-Markie is a novel exploitation of the WiFi infrastr ..."
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Cited by 17 (2 self)
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We present Walkie-Markie – an indoor pathway mapping system that can automatically reconstruct internal pathway maps of buildings without any a-priori knowledge about the building, such as the floor plan or access point locations. Central to Walkie-Markie is a novel exploitation of the WiFi infrastructure to define landmarks (WiFi-Marks) to fuse crowdsourced user trajectories obtained from inertial sensors on users ’ mobile phones. WiFi-Marks are special pathway locations at which the trend of the received WiFi signal strength changes from increasing to decreasing when moving along the pathway. By embedding these WiFi-Marks in a 2D plane using a newly devised algorithm and connecting them with calibrated user trajectories, Walkie-Markie is able to infer pathway maps with high accuracy. Our experiments demonstrate that Walkie-Markie is able to reconstruct a high-quality pathway map for a real office-building floor after only 5-6 rounds of walks, with accuracy gradually improving as more user data becomes available. The maximum discrepancy between the inferred pathway map and the real one is within 3m and 2.8m for the anchor nodes and path segments, respectively. 1
0 From RSSI to CSI: Indoor Localization via Channel Response
"... The spatial features of emitted wireless signals are the basis of location distinction and determination for wireless indoor localization. Available in mainstream wireless signal measurements, Received Signal Strength Indicator (RSSI) has been adopted in vast indoor localization systems. However, it ..."
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Cited by 14 (1 self)
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The spatial features of emitted wireless signals are the basis of location distinction and determination for wireless indoor localization. Available in mainstream wireless signal measurements, Received Signal Strength Indicator (RSSI) has been adopted in vast indoor localization systems. However, it suffers from dramatic performance degradation in complex situations due to multipath fading and temporal dynamics. Breaking-through techniques resort to finer-grained wireless channel measurement than RSSI. Different from RSSI, the PHY layer power feature, channel response, is able to discriminate multipath characteristics, thus holds the potential for the convergence of accurate and pervasive indoor localization. Channel State Information (CSI, reflecting channel response in 802.11 a/g/n) has attracted many research efforts and some pioneer works have demonstrated sub-meter or even centimeter level accuracy. In this article, we survey this new trend of channel response in localization. The differences between CSI and RSSI are highlighted with respect to network layering, time resolution, frequency resolution, stability, and accessibility. Furthermore, we investigate a large body of recent works and classify them overall into three categories according to how
Twins: Device-free Object Tracking using Passive Tags
, 2013
"... Abstract—Without requiring objects to carry any transceiver, device-free based object tracking provides a promising solution for many localization and tracking systems to monitor non-cooperative objects such as intruders. However, existing device-free solutions mainly use sensors and active RFID tag ..."
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Cited by 8 (0 self)
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Abstract—Without requiring objects to carry any transceiver, device-free based object tracking provides a promising solution for many localization and tracking systems to monitor non-cooperative objects such as intruders. However, existing device-free solutions mainly use sensors and 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 such phenomenon by presenting a new interference model that perfectly explains this phenomenon and using extensive experiments to validate it. We design a practical Twins based intrusion detection scheme and implement a real prototype with commercial off-the-shelf reader and tags. The results show that Twins is effective in detecting the moving object, with low location error of 0.75m in average.
Experiencing and handling the diversity in data density and environmental locality in an indoor positioning service
- In MobiCom
, 2014
"... ABSTRACT Diversity in training data density and environment locality is intrinsic in the real-world deployment of indoor localization systems and has a major impact on the performance of existing localization approaches. In this paper, through micro-benchmarks, we find that fingerprint-based approa ..."
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Cited by 7 (0 self)
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ABSTRACT Diversity in training data density and environment locality is intrinsic in the real-world deployment of indoor localization systems and has a major impact on the performance of existing localization approaches. In this paper, through micro-benchmarks, we find that fingerprint-based approaches are preferable in scenarios where a dense database is available; while model-based approaches are the method of choice in the case of sparse data. It should be noted, however, that practical situations are complex. A single deployment often features both sparse and dense sampled areas. Furthermore, the internal layout affects the propagation of radio signals and exhibits environmental impacts. A certain number of measurement samples may be sufficient for one part of the building, but entirely insufficient for another. Thus, finding the right indoor localization algorithm for a given large-scale deployment is challenging, if not impossible; there is no one-size-fits-all indoor localization approach. Realizing the fundamental fact that the quality of the location database capturing the actual radio map dictates localization accuracy, in this paper, we propose Modellet, an algorithmic approach that optimally approximates the actual radio map by unifying modelbased and fingerprint-based approaches. Modellet represents the radio map using a fingerprint-cloud that incorporates both measured real fingerprints and virtual fingerprints, which are computed from models with a local support, based on the key concept of the supporting set. We evaluate Modellet with data collected from an office building as well as 13 large-scale deployment venues (shopping malls and airports), located across China, U.S., and Germany. Comparing Modellet with two representative baseline approaches, RADAR and EZPerfect, demonstrates that Modellet effectively adapts to different data densities and environmental conditions, substantially outperforming existing approaches.
Accurate indoor localization with zero start-up cost
- In Proceedings of ACM Annual International Conference on Mobile Computing and Networking (MobiCom
, 2014
"... Recent years have seen the advent of new RF-localization sys-tems that demonstrate tens of centimeters of accuracy. However, such systems require either deployment of new infrastructure, or extensive fingerprinting of the environment through training or crowdsourcing, impeding their wide-scale adopt ..."
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Cited by 7 (0 self)
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Recent years have seen the advent of new RF-localization sys-tems that demonstrate tens of centimeters of accuracy. However, such systems require either deployment of new infrastructure, or extensive fingerprinting of the environment through training or crowdsourcing, impeding their wide-scale adoption. We present Ubicarse, an accurate indoor localization system for commodity mobile devices, with no specialized infrastructure or fingerprinting. Ubicarse enables handheld devices to emulate large antenna arrays using a new formulation of Synthetic Aper-ture Radar (SAR). Past work on SAR requires measuring mechan-ically controlled device movement with millimeter precision, far beyond what commercial accelerometers can provide. In contrast, Ubicarse’s core contribution is the ability to perform SAR on hand-held devices twisted by their users along unknown paths. Ubicarse is not limited to localizing RF devices; it combines RF localiza-tion with stereo-vision algorithms to localize common objects with no RF source attached to them. We implement Ubicarse on a HP SplitX2 tablet and empirically demonstrate a median error of 39 cm in 3-D device localization and 17 cm in object geotagging in com-plex indoor settings.
Otrack: Order tracking for luggage in mobile rfid systems
- in Proceedings of Infocom
, 2013
"... Abstract—In many logistics applications of RFID technology, goods attached with tags are placed on moving conveyor belts for processing. It is important to figure out the order of goods on the belts so that further actions like sorting can be accurately taken on proper goods. Due to arbitrary goods ..."
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Cited by 7 (3 self)
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Abstract—In many logistics applications of RFID technology, goods attached with tags are placed on moving conveyor belts for processing. It is important to figure out the order of goods on the belts so that further actions like sorting can be accurately taken on proper goods. Due to arbitrary goods placement or the irregularity of wireless signal propagation, neither of the order of tag identification nor the received signal strength provides sufficient evidence on their relative positions on the belts. In this study, we observe, from experiments, a critical region of reading rate when a tag gets close enough to a reader. This phenomenon, as well as other signal attributes, yields the stable indication of tag order. We establish a probabilistic model for recognizing the transient critical region and propose the OTrack protocol to continuously monitor the order of tags. To validate the protocol, we evaluate the accuracy and effectiveness through a one-month experiment conducted through a working conveyor at Beijing Capital International Airport. I.
Luxapose: Indoor Positioning with Mobile Phones and Visible Light
"... We explore the indoor positioning problem with unmodified smart-phones and slightly-modified commercial LED luminaires. The luminaires—modified to allow rapid, on-off keying—transmit their identifiers and/or locations encoded in human-imperceptible optical pulses. A camera-equipped smartphone, using ..."
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Cited by 6 (1 self)
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We explore the indoor positioning problem with unmodified smart-phones and slightly-modified commercial LED luminaires. The luminaires—modified to allow rapid, on-off keying—transmit their identifiers and/or locations encoded in human-imperceptible optical pulses. A camera-equipped smartphone, using just a single image frame capture, can detect the presence of the luminaires in the image, decode their transmitted identifiers and/or locations, and determine the smartphone’s location and orientation relative to the luminaires. Continuous image capture and processing enables continuous posi-tion updates. The key insights underlying this work are (i) the driver circuits of emerging LED lighting systems can be easily modified to transmit data through on-off keying; (ii) the rolling shutter effect of CMOS imagers can be leveraged to receive many bits of data encoded in the optical transmissions with just a single frame cap-ture, (iii) a camera is intrinsically an angle-of-arrival sensor, so the projection of multiple nearby light sources with known positions onto a camera’s image plane can be framed as an instance of a sufficiently-constrained angle-of-arrival localization problem, and (iv) this problem can be solved with optimization techniques. We explore the feasibility of the design through an analytical model, demonstrate the viability of the design through a prototype system, discuss the challenges to a practical deployment including usability and scalability, and demonstrate decimeter-level accuracy in both carefully controlled and more realistic human mobility scenarios.
ZIMO: building cross-technology MIMO to harmonize ZigBee smog with WiFi flash without intervention
- in Proc. of ACM MobiCom
, 2013
"... Recent studies show that WiFi interference has been a ma-jor problem for low power urban sensing technology ZigBee networks. Existing approaches for dealing with such inter-ferences often modify either the ZigBee nodes or WiFi nodes. However, massive deployment of ZigBee nodes and uncoop-erative WiF ..."
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Cited by 5 (4 self)
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Recent studies show that WiFi interference has been a ma-jor problem for low power urban sensing technology ZigBee networks. Existing approaches for dealing with such inter-ferences often modify either the ZigBee nodes or WiFi nodes. However, massive deployment of ZigBee nodes and uncoop-erative WiFi users call for innovative cross-technology coex-istence without intervening legacy systems. In this work we investigate the WiFi and ZigBee coex-istence when ZigBee is the interested signal. Mitigating short duration WiFi interference (called
ash) in long du-ration ZigBee data (called smog) is challenging, especially when we cannot modify the WiFi APs and the massively deployed sensor nodes. To address these challenges, we pro-pose ZIMO, a sink-based MIMO design for harmony coex-istence of ZigBee and WiFi networks with the goal of pro-tecting the ZigBee data packets. The key insight of ZIMO is to properly exploit opportunities resulted from differences between WiFi and ZigBee, and bridge the gap between inter-ested data and cross technology signals. Also, extracting the channel coefficient of WiFi and ZigBee will enhance other co-existence technologies such as TIMO [1]. We implement a prototype for ZIMO in GNURadio-USRP N200, and our ex-tensive evaluations under real wireless conditions show that ZIMO can improve up to 1.9 × throughput for ZigBee net-work, with median gain of 1.5×, and 1.1 × to 1.9 × for WiFi network as byproduct in ZigBee signal recovery.
Use It Free: Instantly Knowing Your Phone Attitude
"... The phone attitude is an essential input to many smartphone ap-plications, which has been known very difficult to accurately esti-mate especially over long time. Based on in-depth understanding of the nature of the MEMS gyroscope and other IMU sensors com-monly equipped on smartphones, we propose A3 ..."
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Cited by 5 (1 self)
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The phone attitude is an essential input to many smartphone ap-plications, which has been known very difficult to accurately esti-mate especially over long time. Based on in-depth understanding of the nature of the MEMS gyroscope and other IMU sensors com-monly equipped on smartphones, we propose A3 – an accurate and automatic attitude detector for commodity smartphones. A3 pri-marily leverages the gyroscope, but intelligently incorporates the accelerometer and magnetometer to select the best sensing capa-bilities and derive the most accurate attitude estimation. Extensive experimental evaluation on various types of Android smartphones confirms the outstanding performance of A3. Compared with other existing solutions, A3 provides 3 × improvement on the accuracy of attitude estimation.
