Name: Locationoid。安卓平臺的精準節能定位供應商 (Locationoid: An Accurate Energy-Efficient Location Provider for the Android Platform)
Uploaded: 2021-01-14T06:23:58.000Z
Duration: 56 min 54 s
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YOUSSEF: Hello everyone and thank you for coming. I'm Moustafa Youssef from Egypt-Japan

University of Science and Technology. And today, I'm going to share with you our recent

result on the Location--Locationoid project where we target an accurate and energy-efficient

localization for GSM phones in general and for the Android platform in particular. So,

as we all know, location-based services has been gaining momentum recently, and application

rangees from finding the nearest pharmacy to, "My--are there any of my friends near

by now? Is there anyone interested in playing Chess now?" and so on. And the applications--the

list of applications keep increasing everyday. And there are commercial services that are

being offered in a larger scale like Google Latitude and Places, Facebook Places and they

keep coming everyday as I said. However, whenever we talk about localization for cell phone,

we need to keep in mind the energy consumption of the localization technology we are using,

because of course if your localization algorithms leads to depleting the battery very fast no

one will be willing to use it because energy is a very scarce resource on mobile phones.

Currently, the most commonly used and known technique for cell phone localization is the

GPS. However, GPS, as we all know, requires--is an energy hungry device. It require--it consumes

a lot of energy as I would go and define later in my talk today, and it doesn't work in every

place. So, for example, it doesn't work in the indoor environments, it doesn't work in

areas with tall buildings because it require line of sight to the satellites. And at the

same time, the recent applications, like navigation for example in Google phones, Android-based

phones, and Nokia phones and other popular phone--cell phones requires that you do your

localization very frequently. So, for example, in order to track your car you need to get

the GPS location very frequently and this can lead to killing the battery very fast

on your cell phone. So the problem we are trying to target is how to do localization

on cell phones with high accuracy but at the same time you should be able to do it in an

energy efficient manner so that you conserve the energy on your cell phone. So the Locationoid

project, what we--has different components. One of them, for example, is to perform GSM-based

localization using GSM network, not using GPS so that you are not using the energy hungry

GPS device. Another component is to do power-profiling for the different location components on the

Android OS so that we can quantify how much energy each component takes when it's running.

And once we do this profiling, we propose an energy efficient technique for obtaining

the cell phone location using the internal sensor. It is not using the GSM network nor

using the GPS, but depending on the internal sensors, mainly the accelerometer and the

compass. Finally, what we do in this project is that we propose a bar management layer

that runs on the--on top of the location provider in the Android system that can combine all

these components together to obtain, as I said, accurate and energy efficient localization

system. So in the rest of my talk, I have two main components. The first one is CellSense

where we try to obtain an accurate location estimate in GSM network using Probabilistic

RSSI's fingerprinting techniques. And in the second part, we propose GAC, which is a hybrid

GPS/Accelerometer/Compass technique, that uses the internal sensors to obtain the cell

phone location with minimum energy. And the final I will conclude my talk. So the first

part of my talk is about CellSense: A Probabilistic RSSI-based GSM Localization System. And if

we try to compare the GPS localization to GSM localization we can see that GPS is an

energy hungry device, doesn't work in all areas. As I said, it doesn't work indoors,

for example. And it is not available in all cell phones, it's just--it's only available

on high-end cell phones. On the other hand, GSM-based localization systems consumes minimal

energy or if any on top of the standard phone operation. Since your phone is already connected

to the GSM network and it's performing the standard GSM protocol, if you do localization

using the GSM signal you are consuming minimal amount of power in addition to the standard

GSM operation. So this is good. And it works everywhere GSM signal is available. So when

you are indoor you can obtain a GSM signal. Your localization technique can work in indoor

environments. And finally, GSM Standard, according to Wikipedia, is available on 80% to 85% of

the cell phones in the world. So again, you have a very large coverage in terms of devices.

So it's attractive. So if you focus on GSM-based localization, the current techniques that

are being deployed on cell phones or handset-based techniques are mainly Cell-ID based techniques

or RSSI-Based. By Cell-ID based, what we mean is that if you have your cell phone and you

can hear from a cell tower, the system estimates your location as the location of the cell

tower you are associated with. This provides an efficient way of obtaining your location.

However, it's a very coarse-grained accuracy because on the average, your accuracy will

be half of the coverage range of the cell tower. If you have more than one cell tower,

like here we have three cell towers, your estimated location will be the center of mass

of all the cell towers you can hear from. So, as we said, this provides an efficient

way of obtaining your location but it's a very coarse-grained accuracy. Recently, there

have been techniques that uses RSSI that receives the signals strength to estimate the cell

phone location. However, since RSSI is a very complex function of distance we can not use

a propagation model to estimate our location. Typically, what's being done is you use what

we call an RF fingerprint. An RF fingerprint is basically you tabulate the characteristics

of the signal strengths at different locations in the area of interest. So, for example,

if I want to do a cell phone localization in Mountain View I go to different locations

in Mountain View and collect signal strengths to information at different location and I

store this information in what we call an RF fingerprint. It's a database of signal

strengths characteristics at different locations. During an online phase, the tracking phase,

I am standing at an unknown location and I am receiving a signal strength's vector from

different cell towers and I'm trying to find the location in the fingerprints that I stored

in offline phase that is closest to where I received signal strengths. You can imagine

of course that using an RF fingerprint is a time-consuming process because you have

to do--go to different locations and stand there to collect the signal strengths. However,

this is typically done in a process that's called wardriving where you ride your car

and you have a cell phone with GPS that's connected to a computer and you are collecting

the location information and signal strengths information at the same time. And many of

the current commercial systems like StreetView under My Location from Google currently have

cars that roams the streets. So in order, for example, to get the pictures of the screen--as--the

streets in the Google Map application, and while you are doing this you can piggyback

your radio map construction in the same process. So it is not something that you had to do

particularly for localization, you can do it for other applications, too. So our wardriving

overhead is very minimal here, too. So currently, there are up to my knowledge, there are two

main techniques for using RSSI localization in GSM networks. One is deterministic fingerprinting.

And by this, what I mean is that the red dots represent the locations where you have the

fingerprint, so at each of these locations you stand and collect the signal strength's

characteristics. And here I assume you have only one cell tower, and the characteristics

that is stored in the radio map in deterministic techniques it's average signal is--signal

strength from this cell tower. So you stand at this location, you collect a number of

samples and you store in your radio map's average signal strengths you hear it from

this cell tower. The red dot represents an unknown location. You are currently standing

at this yellow dot and you want to know which of the red dots I am standing at currently.

So in order now to do that, deterministic fingerprinting techniques, what they do is

they calculate and locate the distance between the unknown location, the signal strengths

at the unknown location and the signal strengths stored in the radio map, and they return the

closest location in the radio map in terms of signal strengths as the estimated location.

So in this example, the closest location is -32 to -28 and it's returned it as your estimated