Souvik Sen (Duke University), Bozidar Radunovic(Microsoft Research), Romit Roy Choudhury(Duke University), Tom Minka(Microsoft Research)
This paper explores the viability of precise indoor localization using physical layer information in WiFi systems. We find evidence that channel responses from multiple OFDM subcarriers can be a promising location signature. While these signatures certainly vary over time and environmental mobility, we notice that their core structure preserves certain properties that are amenable to localization. We attempt to harness these opportunities through a functional system called PinLoc, implemented on off-the-shelf Intel 5300 cards. We evaluate the system in a busy engineering building, a crowded student center, a cafeteria, and at the Duke University museum, and demonstrate localization accuracies in the granularity of 1mx 1m boxes, called “spots”. Results from 100 spots show that PinLoc is able to localize users to the correct spot with 89% mean accuracy, while incurring less than 6% false positives. We believe this is an important step forward, compared to the best indoor localization schemes of today, such as Horus.
Paper Link: http://synrg.ee.duke.edu/papers/pinloc-final.pdf
Public Review uploaded by AnthonyLaMarca:
Accurate indoor location on commodity mobile platforms remains elusive. The best technology to date is 802.11 "fingerprinting" which yields 1.5-3 meter accuracy in typical indoor environments. This paper aims to improve this accuracy by making use of the subchannel signal strength information available on some newer wireless chipsets. Using the per-packet signal strengths available on 30 subchannels, the authors show resulting fingerprints have resolution as small as 2cmx2cm. Since these subchannels are sensitive to environmental changes, the author's system collects a large number of subchannel prints within a 1mx1m region and shows that these regions can accurately be detected, effectively creating an indoor location system with near 1m accuracy.
The use of subchannel information is a clever evolution of RSSI-based location estimation. The paper introduces the idea and algorithms in a thorough and understandable way and the evaluation of the algorithm is good; including variations in location, number of beacons, etc. This paper is enjoyable to read and contains an extensive discussion of performance.
While this work is a nice step forward, there are a few key challenges that still need solving before this will be a replacement for traditional 802.11 fingerprinting for laptops, smartphones, etc. The first is that fingerprint matching happens in 3D, not 2D. The paper makes clever use of a Roomba to collect fingerprints. Unfortunately, this technique won't directly create radio maps for devices carries by people at varying heights. Thus either a volumetric signal mapping system or a model for transforming 2D maps into 3D maps will need to be developed. In addition, RSSI is known to be affected by user's hands, bodies and bags used to carry devices. While the paper investigates having a human near the device, the effect may be more significant when the device is held or in a pocket. Finally, fingerprint matching accuracy can be affected by device and antenna characteristics and subchannel fingerprints may be especially sensitive. Understanding how to map with one device and have the results translate to other models will be essential to letting this technique scale to large deployments.
Thanks to Anthony Lamarca for the insightful comments. While the review aptly summarizes PinLoc's contributions and shortcomings, we intend to clarify a few points below:
(1) Per-subcarrier information: We emphasize that we extract the complex-valued channel frequency response of each OFDM subcarrier, and compute multi-dimensional fingerprints from them. We believe this is PHY layer information, and far richer than either RSSI or per-subcarrier based RSSI, that have been used in literature.
(2) 3D war-driving: PinLoc requires 3D war-driving since real-world users will carry phones at different heights. While this may lead to higher war-driving overhead, optimizations may be feasible based on signal mapping techniques, i.e., estimating the signal properties at a specific location based on measurements from a nearby location. Alternatively, when war-driving with the robot, antennas could be placed at different heights to characterize the variation -- 3D fingerprints can be extracted from them. Of course, in its current stage, PinLoc does not demonstrate the viability and accuracy of any of these techniques, and hence, the criticism is valid.
(3) Cross-platform calibration: To our knowledge, Intel 5300 were the only wireless cards that exposes subcarrier level channel information, hence, our results are all hinged to it. We observe that PinLoc achieved the desired accuracy on all four Intel 5300 cards we used in the experiments and all devices that use this card should be amenable to PinLoc without calibration. For cards from different vendors, cross-platform calibration will be necessary, as is the case with existing RSSI based schemes. We note that such calibration may not be hard, as it should be sufficient to obtain only a few readings on a new radio hardware from a known location to estimate and remove the shift in the frequency response introduced by the radio. We could not find cards from different vendors that support subcarrier level channel information, and hence, we were unable to evaluate this scheme at present.