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Mapping WiFi measurements on OpenStreetMap data for Wireless Street Coverage Analysis

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OK everybody we're going to have a talk here from continuous severity about nothing like measurements of letter started the key to get good morning everybody In the thank you for being in this presentation I mountainous already and the I mean to presented in this work that this title mapping way a measurement on OpenStreetMap data for why suite coverage analysis a young from the Polytechnic University and Turing and this work has been done in collaboration with the the uh day joint open lot of steam and that this was formerly known as telecommuter idea that that is some major telecom company in the tunnel it is is the outline
of this presentation I will start with the motivation of this work and why it was started developing the software then I will briefly described in our work and how it correlates to other works In the literature I with fine and then presented there software framework with the real application that we applied to a case study with the dataset of the city of Turin where I come from and then I will summarize the conclusions and I will present some ideas for a few further improvements we
know that that almost every is that is invention Wi-Fi the Internet's router to extend its coverage outside this is the only thing here is an example of the a screenshot of my Mac and when I connected to the my wireless network from in front of the policy and you see there are a lot of access point of wireless networks with good coverage so you can get this signal from of the Wi-Fi wrote also from the streets around the house and to exploit this extended coverage a growing number of all the internet service provider that has created the Wi-Fi communities where data users can connected to the internet to to other people Wi-Fi when they are moving around door when they're traveling here is an example of this uh amount but also for access point to them in the center of bone and the uses so the and access point to from other than income companies for example all the in Europe say so in this section of the could Wi-Fi callers scenario where for life I wrote this cover public location area nearby the ideology providing Internet access to will user we felt that in the future also smart devices can use is internet connections in the neighborhood so for example model downstairs can when they are full so that they the collection router can be optimized not the way that users can move around to find a better coverage of the this in a sense the objects of devices cannot move around so we have to study the coverage of the streets if they are what color the on
and so we begin his work with the research question is it possible to use Wi-Fi hotspots often in service provider and we were working with an inter service providers to offer Internet connectivity to devices along the streets similar so the traffic lights uh downstairs and so on and we developed at this is a softer that we call the Wi-Fi Street coverage analysis and we started asking at the question for example of all 5 the wireless routers extend the coverage outside the building so or if we can compute all much of a given history discovered by their wine nearby Wi-Fi so we need information from the wider sets of the acid wireless access point their location called the original and also information about this seeking the streets for example and that's why we use the information from OpenStreetMap and then we collected Wi-Fi measurement and they started mapping them on OpenStreetMap data relating them to open data as we will see most of the maps you find around they just put the location of the export to maybe they do not uh uh relate to this location to what is what's there is around 10 we publish this software on . but this year and this is a screenshot of an application we developed the with this software and the given an uh are of this city it can visualize their location and coverage of the hotspots and year the coverage is represented by this you could then it is proportional to the signal strength of the access point and after that the we retrieve state information from OpenStreetMap and that we use this information to compute the which we call street called the duration that is the portion of the street this is called the by the way the sink not so that is inside of the this of course the case so you can know if a is fully covered only partially there when we look at other works in the literature we so that there are no works that exploit the mapping information it to analyze the areas covered by wi-fi exports uh for example to know if there streets gardens square and so on previous works related to a wireless coverage and white excesses . 2 was mediated through collective measurement with the outsourcing for estimating the locations and the strength of the coverage area of the base stations and publishing this information of in online maps but they were just point on up they were not related to the information The map is represented only related to images here are some examples yeah yeah a close its Suez online maps from the internet service providers mainly where they're Wi-Fi communities for example of all the phone 1 around Europe and then orange in France they provide a reliable information is also real time updated data I think that most of the cases by but the the notion of the data is example of the 1st as in the 1st talk they have these information on the web site but that most of the cases is just images not the Open on the other side of the on-line maps the top that's a open source and they come from comes out counts the Wi-Fi data there is an example of the legal while I'm at the end of the week application the use those application for example for either to do driving and collect information of the was fault that people see on the streets and then they try to locate the uh data and base station and the ability to get the location on a map and they provided there the these Mitzi open doctor but that it's not reliable the end of it's not real time you get people were driving in industry denoting the other and the information is not updated their dating and so on and the mean streets are covered better than smaller states and if you look at the previous month of maps of of the Wi-Fi communities you see that they are not so informative day for example about the advertise only the location and the number of full of hot spot in a CD or in a region if you look at that the following website today uh publisher they better from ice information as the number of hotspots in a CD but you don't know where they are in which location of a this city is called so we voted that that would be a better the information to give for example the percentage of a given IRA or at given CT that the school by the way centuries and that is for
this reason that we started the development of a publicist after that is to provide more informative description of the Wi-Fi coverage and the I was softer takes Romanies romance on site using the mobile application or from a web service as in the case so we go then the our application renders the sports location of online interactive maps you can query for the location and also users search bottoms and move around and we're using the flat and the angular for this part of the application that we get the information from OpenStreetMap about the location of the in this case and then we calculate the streets cholera iterations
here we I will present a case study for the city of Turin and that is an over an area of 15 by 16 kilometres Junior has a population of 1 million and a density of 7 thousand people per square kilometres and we analyze the this CD dividing it in blocks of I don't know if you really can see the blocks there are all of 1 kilometres or 15 meters so uh size their
size is 1 2 meters long by the need them and yet the back bonds days what sports we got from the legal in this particular case yeah
but we use the to that the set 1 that is uh that the said that we collected the under the application for work driving that we developed the but obviously we got the results from these discounting and only into the regions of the city so I will presented the results with the the information we don't know did the once from we got there we go to the website and they have an API that you can query to get some information but a we didn't we don't think it's really reliable we tried the other that the basis that we have still searching for open that the bases with this information there but we got will and so we use that we believe that
the set there here are some
numbers of the 1st the results so we computed that is the I p density in a block that is the number of access points in a block it's on profits the nations of the Wi-Fi coverage and there you see in this matter that that that every bill could as a circle and the
size of the circle is proportional to the number of that P in that block and they are there are more of a people blocking the city center than in the the surrounding area but obviously the isn't it results in some limitations because it does not take into account the position of AP if they are overlapping in order to and so it's not that an effective the measure of the coverage it we find the divided the this seating in some regions that we call the macroblocks they have different sizes around the city center here are some examples uh number 2 3 4 5 then here is a figure where we blocked the the distribution of the number of ATP the block at the end at different ideas and we see that the more we move away from the city center obviously had the moral for example of the inter-agency the number of that people brought could become easy so we have an average of 150 close to the city center and then it decreases to 100 there and know that wages then the final here are the results of all this coverage and ratio for each block in this small amount you see that markers the that identi fied the blocks where there is street coverage ratio that is the ratio between the total length of the streets and the length of the streets that are covered by the Wi-Fi signal is higher than 50 per cent and here is the cumulative distribution function of the blocks at college ratio In the macroblocks a number 4 5 7 and so on and then from this plot to you see that the let's say in the smallest the macroblocks the 1 closest to the city center there we have that that that 50 per cent of of the blocks of the coverage of the show that these higher than 50 % of and the if you move farther from the city center there are at a lower percentage of blocks that have been there a color ratio of idea than 50 % and if you are more selective it increases the only blocks that have been a color ratio higher than 70 % but also close to the city center you have a small ratio of rock that is 10 % there that the red ones but the probably the most interesting results of this of our case studies the relation between the density the number of the a P the number of people block with respect to street cover duration here is an example of for blocks of 500 meters and the we built the the number of ATP the block the various solution this state the corre duration they're considering at the coverage of made use of all 4 25 meters since that are the red the points or 50 meters the blue points and from this plot that you see that since they the x-axis is another weakening from there is a a logarithmic relation between the number of the axis pointing a block and street cover iterations and for example in is underlined AP deployment to you see that that 1 and that the baby feisty needed to cover all region I've heard of this trade off 500 by 5 100 meters city blocks OK so you need atleast that around 100 bp to cover up to the streets of a city block and so the outcome of this case study is that it is relatively easy to us he or she achieved up diverse and coverage ratio in a block with these company the deployment it's something that because it depends on the user so if you want to by your 8 itself from these ideas I think and service provider or another and there but it is impractical to increase above that presentation and you need idea percentages percentages if you want to provide a service industry if you are an internet service provider and you want to provide the internet connection to devices industry there for example from these results you need that 1 thousand people to get an 80 % street coverage their ratio in a block of 15 hi 100 meters so to achieve a buddhist address the coverage ratio requirements playing in the position of additional ATP from the continental provided but this is left for future work where these additional be required to be located you have to offer DSL connection to some only uses to achieve that so here are the conclusions a we presented the a free and open source software that we call the Wi-Fi State College Explorer you find it on the top of that would extend out of our colonial J is is the 1st application that the relates Wi-Fi hotspots college with to a map information we use the OpenStreetMap information and then we developed his applications with the idea of analyzing the Wi-Fi network coverage to provide the internet access not only to traveling users but also to smart devices that as a state cannot move around and to find a better spot to for Internet connection and in our case study for this reduced during the day we show that the employer and the deployment of DAP struggled to go beyond 50 % of State College rich and and
yet if have time some ideas for future work and the we problem is to find the the label information also real-time information about the access point it's related to the 1st presentation and we don't we still don't have antenna straight we haven't been in our pockets I say but the councils of crowdsourced measurement of not so reliable and real time and we're looking around the for better sources now we are exploring the form that the set because the former needs to advertise in the location of the lost for to the users so that they can find the right spot there and they're using and API that seems to be updated daily this is this screenshot that we got around the Polytechnical off during of the location of the AP and this is the information they provide and we plan
to do extended the results of that that can be conquered with application uh for example to know gaps along the street that are not college and to visualize this information or as they say that to plan the optimal placed the placement of the minimum amount of access point in a block to reach the desired their level of coverage became the
axons my presentation I think 1 time they use your think thank you tenure near the questions the audience and the D. N. A. D. capture data about things like and channel copper and overlap in order to sort of and not only see if there's coverage but if it's really busy coverage or if it could be better optimized coverage In this moment the
this that the Bayes we used to get information about the access point not publish the channel of the access the the wireless networks so we don't have this information and I find think that also In the
case of falling you get network and some other information but not the channel it can be very useful tool that statement the noise of the way 5 in the CD and you can do that if you are collecting by word alignment information by doing this yet but we're not doing that at the moment thank you knew the questions this section more
common to have you have you heard about the free func a
project some and in German this service of protocol 205 Fred Funk yet where people organize it so many would make sense to to get in contact with people because they they have a lot of a lot of most amazing they might be quite interested in and then the coverage so basically that they could nowhere are way could they placed more and more nodes to to get better coverage yeah we have contact with the community a lot of Google 0 really define also sort of get yeah yeah we didn't I didn't least the open source project that are mopping Wi-Fi networks and there are others like open signal and and so on and the we use
legal because it's in it was the first one we could get information about them the location of the at this point but yeah we we're fine with other sources and thank you for the suggestive here in the equations for the geezer
Martin and take
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Metadaten

Formale Metadaten

Titel Mapping WiFi measurements on OpenStreetMap data for Wireless Street Coverage Analysis
Serientitel FOSS4G Bonn 2016
Teil 192
Anzahl der Teile 193
Autor Valenzano, Andrea
Mana, Dario
Borean, Claudio
Servetti, Antonio
Lizenz CC-Namensnennung 3.0 Deutschland:
Sie dürfen das Werk bzw. den Inhalt zu jedem legalen Zweck nutzen, verändern und in unveränderter oder veränderter Form vervielfältigen, verbreiten und öffentlich zugänglich machen, sofern Sie den Namen des Autors/Rechteinhabers in der von ihm festgelegten Weise nennen.
DOI 10.5446/20454
Herausgeber FOSS4G
Open Source Geospatial Foundation (OSGeo)
Erscheinungsjahr 2016
Sprache Englisch

Inhaltliche Metadaten

Fachgebiet Informatik
Abstract The growing interest on smart cities and the deployment of an ever increasing number of smart objects in public locations, such as dumpsters, traffic lights, and manholes, requires ubiquitous connectivity for these devices to communicate data and to receive configurations. Opportunistic WiFi connectivity is a valid alternative both to ad hoc solutions, like LoRa, which require costly deployments, and to communicating through the mobile network, which is both pricey and battery power hungry. In this paper we present a tool to analyze the WiFi coverage of home Access Points (AP) on the city streets. It can be of interest to ISP or other providers which want to offer connectivity to Internet of Things smart objects deployed around the city. We describe a method for gathering WiFi measures around the city (by leveraging crowdsourcing) and an open source visualization and analysis web application to explore the accumulated data. More importantly, this framework can leverage the semantic information contained in OpenStreetMap data to extract further knowledge about the AP deployment in the city, for example we investigate the relationship between the AP density per square kilometer within the city and the WiFi street coverage ratio

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