37 lines
8.3 KiB
Plaintext
37 lines
8.3 KiB
Plaintext
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Dear Reviewer,
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-> At first we would like to start with a short overview over all changes. The individual answers follow directly after this text. All additions to the text are highlighted in blue. Words or text passages suggested by the reviewers to be removed are highlighted in red. As you will see, abstract and introduction are completely revised to better highlight the novel contributions. We were able to implement many suggestions of the reviewers. The transition was highly extended, to achieve a better understanding of the method. We have also added a detailed description of how and by what means our system is installed in a building. This also leads to a better description of the experimental setup.
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We added a complete new section, evaluating the activity recognition. Additionally, you will find mind smaller changes and addition throughout the paper as well as further improvements of the writing. In the following our answers are marked with "->".
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The paper presents a smartphone-based localization system using a particle filter to incorporate different probabilistic models. The comments and suggestions as follows:
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1. The authors mention that "a setup-time of under 120 min for the complete building" in abstract. But I don't find any context about the setup-time in the whole paper. How does the "under 120 min" calculate? How long does the navigation mesh for the whole buliding take? How long does the 42 WiFi beacon installation take? How does the measuremnet of the reference points take? etc. The authors should give the details.
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-> Thank you for pointing that out. We added a detailed description at the beginning of the experiments. Here we describe the individual steps to set up the system. Furthermore, the concrete setup-times for the museum are given. To get a better understanding of our approach, we have added screenshots of the software involved. Finally, we have described the navigation mesh more clearly, as it is automatically generated from the floor plan.
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2. The authors mention that the historical buildings "environments that are not built with localization in mind or do not provide any wireless infrastructure". But the WiFi beacons still need to be plugged into the power outlets. That means the whole building need 42 available power outlets. Does the WiFi beacon install in special position? I think the historical buildings don't have enough power outlets or the power outlets don't be available in a suitable position, maybe there is no power outlets at all in the whole corridor for example.
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-> Your criticism is justified. However, since the building presented here is a museum, it has a certain number of outlets due to the exhibits distributed all over and which have to be illuminated. Nevertheless, in a corridor without a single outlet, the Wi-Fi beacons could be powered by batteries as well. Thanks for mentioning the positioning of the beacons, this was missing within a previous version of the text. We added a description between line 563 and line 577. To sum this up, the beacons were installed very freely. We just plugged them directly into the available outlets. Sometimes they hang from the ceiling and sometimes they were placed directly under a showcase.
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3. The authors meation that "This leads to problems for methods using received signal strengths indications (RSSI) from Wi-Fi or Bluetooth, due to a high signal attenuation between different rooms". However the WiFi beacon this paper used will meet the same issues, the authors also use the RSSI of the WiFi beacons? How does the WiFi beacon avoid the high signal attenuation between different rooms?
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-> We clarified this statement within the text. The text passage you mentioned should not refer to the beacons, but to our used optimization method, see line 56 to 69. As you correctly stated, the beacons are not able to avoid high signal attenuation. However, as they are very cheap (less then $10), we are able to increase the coverage by identifying weak spots. This makes the localization system a bit more independent, e.g. if some building should provide its own Wi-Fi infrastructure.
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4. The authors mention that an optimization scheme can avoid inaccuracies that "outdated fingerprints caused by changes of the environment or inaccurate building plans". However the paper also use the recent RSSI measurements of nearby AP’s and signal strength predictions and "Each reference location was scanned 30 times (≈ 25 s scan time) using a Motorola Nexus 6 at 2.4 GHz band only". How can the reference poits to deal with the changes of the environment? Only 25 scan for each poit at the setup? I can't find any special details to deal with the environment changing issues.
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-> Thank you very much for pointing this out. Your concerns are valid. This was a misformulation within the introduction of the paper. Of course, the optimization is not able to compensate for outdated fingerprints! We have improved the relevant section of the text and made the basic statement clearer. Please refer to line 70 and 83. Thanks again!
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5. The author mention that "Such buildings are often full of nooks and crannies, what makes it hard for dynamical models using any kind of pedestrian dead reckoning (PDR)","the error accumulates not only over time, but also with the number of turns and steps made". So "Thus, this paper presents a robust but realistic movement model using a three-dimensional navigation mesh based on triangles". However, Why does the three-dimensional navigation mesh can deal with the turns and steps error? The author should give the more detail description. The navigation graph uses 30*30 grid-cell, the navigation mesh uses triangles. But I don't find very clear that how does the triangles plan? More triangles can improve the accuracy or not? Why the the ground floor need 320 triangles? This the minimum?
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-> We completely changed the transition part, describing how both, graph and nav-mesh are generated automatically, based on the building's floorplan. The number of required triangles strongly depends on the building's layout. 320 were required for the building presented within the picture. We hope that the revised chapter will be easier to understand and give clearer insights into the method, as it is a very important part of our system.
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6. The authors emphasize that "The goal of this work is to propose a fast to deploy and low-cost localization solution, that provides reasonable results in a high variety of situations". But for the 2500m2 building they used 42 WiFi beacon. I don't think the number is few. Is the whole 42 beacon necessary? The author should discuss the impact of the number of WiFi beacon. How many are the reference poits? What's the impact of the density of the reference points? If the authors want to emphasizen the fast deploy and low-cost, they should give more detail discussion, also the "high variety".
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-> At first, "within the 2500m2 building" was a bad misformulation, as the building is far bigger. The 2500m2 refers only to the area that is actually walkable by the visitors. As can be seen in figure 7, the building has a very large courtyard in its center. A second reason for the high number of beacons, are the very thick walls. To prevent to much attenuation, we tried to install at least two beacons per room and a third one in an approximate radius of 10 meter. As said before, this was done very quickly without analyzing the Wi-Fi coverage. As the beacons are very cheap (less then $10), they represent only a small part of the total cost of the system. They only require a power source in order to operate, which keeps the need for additional infrastructure small. Furthermore, we believe that a janitor is able to set up our system independently. This means that there is no need to pay an external contractor to utilize the system and only the hardware costs and, if applicable, the price of the software have to be calculated. Nevertheless, these considerations could not apply to all buildings and scenarios, which is why the property "low cost" is removed. Please also refer to line 563 to 586. The number of reference points (133) was added to the text. The term "fast to deploy" is discussed in great detail at the beginning of the experiments. Experiments providing the impact of the density of the reference points, as well as the access-points can be found in our previous paper, ""
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-> Thank you again for your time and the good suggestions to further improve this work.
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