He Wang, Souvik Sen, Ahmed Elgohary, Moustafa Farid, Moustafa Youssef and Romit Roy Choudhury
We propose UnLoc, an unsupervised indoor localization scheme that bypasses the need for war-driving. Our key observation is that certain locations in an indoor environment present identifiable signatures on one or more sensing dimensions. An elevator, for instance, imposes a distinct pattern on a smartphone’s accelerometer; a corridor-corner may overhear a unique set of WiFi access points; a specific spot may experience an unusual magnetic fluctuation. We hypothesize that these kind of signatures naturally exist in the environment, and can be envisioned as internal landmarks of a building. Mobile devices that “sense” these landmarks can recalibrate their locations, while dead-reckoning schemes can track them between landmarks. Results from 3 different indoor settings, including a shopping mall, demonstrate median location errors of 1.69m. War-driving is not necessary, neither are floorplans
– the system simultaneously computes the locations of users and landmarks, in a manner that they converge reasonably quickly. We believe this is an unconventional approach to indoor localization, holding promise for real-world deployment.
Public Review uploaded by sseshan:
This public review was prepared by Srinivasan Seshan.
Effective indoor localization remains an elusive, "holy grail"-like goal for mobile systems research. No existing proposal simultaneously addresses the core need of providing highly accurate, low-power and ubiquitously available indoor localization while requiring no additional handset hardware, no additional infrastructure, and no significant measurement effort.
The proposed system, UnLoc, uses a wide range of techniques including inertial navigation, WiFi fingerprinting, and floor plan based landmarks to eliminate the measurement needs of past designs. It would be easy to argue that none of the individual techniques, as used in the paper, is truly novel. In fact, there do exist research papers on some of these particular topics that leverage more sophisticated process to produce better results than UnLoc. However, the key contribution of this work is not in the individual techniques but in how it melds together these different techniques in the cellular phone context to make a significant step towards deployable indoor localization.
While this paper makes an important step forward in indoor localization, there are numerous concerns that this paper does not address. First, this work took a sensor fusion style approach to localization. While this helped greatly in terms of avoiding war-driving measurement in this design, it is not clear if the final solution to indoor localization will need this approach. One of the weaknesses of this approach is that each sensor uses energy, reducing the battery life of the mobile device. Unfortunately, the discussion of energy costs is relatively short and superficial. Second, while the techniques appear promising, a three building study is very limited and the techniques need to be proven in a much broader collection of environments. For example, how representative are the location of seed and organic landmarks in these buildings? How painful is it to identify seed landmarks before the system is deployed?
Looking at the broad area of indoor localization, there are a number of different directions that have been proposed in recent papers (including several at this MobiSys). It will be interesting to see which of these paths forward produces a lightweight design that gets widely deployed.
We appreciate Srinivasan’s public review for UnLoc and agree with him entirely that indoor localization remains an unsolved problem, especially under the numerous constraints such as low-energy, zero-infrastructure, zero-calibration, high-precision, etc. We also agree with the key points raised in the review, however, argue below that with some more work, UnLoc may indeed be a candidate for real-world deployment.
Aggregation of techniques:
UnLoc certainly builds on existing building blocks in mobile computing literature; however, we believe the contribution lies in making the connection between them, i.e., connecting SLAM techniques from robotics, with ambience sensing from context-awareness and localization, with inertial sensing from activity recognition.
The UnLoc paper is indeed thin on sensing energy; however, energy-awareness was actually accounted for in the very design phase itself. The use of microphones and light-sensors were deliberately avoided to reduce the energy footprint, even though they may have offered many additional landmarks. Instead, UnLoc restricts itself to inertial sensors alone, that together can support continuous sensing for long durations, while enabling both landmarks and motion tracking using the same sensor measurements. We intend to formalize these claims in subsequent works.
We note that seed landmarks need not be explicitly identified per building; rather they are universal signatures that should occur independent of the building. We instantiated this universality through measurements from 3 different buildings in Egypt and USA, while appealing to intuition that our observations would scale to almost all other places, i.e., we assumed that the elevators operate using similar machinery worldwide; staircases will be similar everywhere; so would escalators. If it is necessary to validate this assumption more carefully, we can certainly do so.
Finally, we emphasize that, in our opinion, the core strength of UnLoc lies in its robustness. The first robustness comes from the fact that, when a given sensing dimension is measured across an entire building, some patterns are likely to emerge, thereby offering landmarks. Put differently, there is always likely to be a "tail" in the distribution (of patterns).
The second form of robustness comes from the fact that even if a user's location estimate diverges completely, encountering a landmark can bring her back to her actual location. This form of resetting is valuable to cope with unanticipated, worst-case scenarios. Finally, even if the environment changes (say a WiFi AP is moved, or electrical equipment relocated), UnLoc can learn this quickly, resulting in automatic addition and deletion of landmarks.
Real world environments are typically harsher than labs, and such forms of robustness to worst-case scenarios would be necessary.
Whether it makes UnLoc sufficient is still not known, and will require much more measurements and experimentation.