Chapter 18 Location Aware Applicationscsce.uark.edu/~cwt/COURSES/2014-01--CSCE-4543--SW... · Chapter 18 – Location Aware Applications ... accessible with mobile devices through

SOFTWARE ARCHITECTURES

Chapter 18 – Location Aware Applications

Ben Gooding

1. Summary

This chapter will cover the many forms of location aware applications, the challenges associated

with these applications as well as their architecture. The majority of location aware applications

are utilized by mobile devices. There are some location aware applications for desktop

computers, such as find local singles in Fayetteville ads, however that is not the focus of this

chapter. There are four main types of location aware applications that will be discussed and they

are: proximity based applications, indoor localization, traditional localization (GPS &

triangulation), and human mobility prediction.

2. Introduction

Location aware applications are a class of computer based applications that use location data to

control the application’s functionality. These types of applications have a wide range of uses and

are continually growing in popularity. This popularity growth has been caused by the steady

increase in the number of mobile devices in the hands of potential users.

Location aware applications have grown from just being simple services to growing into

complex and ever evolving applications [8].

This is not necessarily a new concept that came around with the invention of the mobile phone.

Computers have been using the IP Address of a person searching to determine the user’s rough

geographic location. Posters that list location information for a local concert and a person

relaying information to another can be considered a location aware system.

So what exactly is a location aware application? This can be defined as

“…information services accessible with mobile devices through the mobile network and utilizing

the ability to make use of the location of the mobile device [3]”

We can further define this by making note of what questions an location aware application will

answer [3]:

“Where am I?”

“What is nearby?” or

“How can I go to?”

To fully understand how a location aware application works we will look at the five components

that go into their development [3]:

Mobile Devices – This is the tool that a user will use to gain access to the application.

Without the mobile device there is not location aware application. These can consist

of a GPS navigation unit, Smartphone, tablet or even smart watches.

Communication Network – In some fashion the mobile device needs to contact either

the application server or the positioning component. This is the network form being

used to transmit data.


Positioning component – This is the component that will be utilized to determine the

user’s position. This may consist of the GPS adaptor, a WLAN, cellular towers or

even sound waves. The user may also manually specify their location.

Service and Application Provider – The service provider is responsible for providing

a number of different services to the user and is responsible for processing the service

request. This can consist of route calculation, search results, nearby friends, etc.

Data and Content Provider – If the service provider does not control all of the

information than some requests will go to the data provider. This can be a mapping

company or the Yellow Pages.

3. GPS & Cellular Localization

3.1 Introduction

GPS and cellular localization can be used to identify the location for many of the applications

that will be discussed within this paper. However, both are deserving of their own section as they

are such an important piece of the location aware application ecosystem. Within this section the

topics of how GPS works, what cellular triangulation is, the challenges associated with these

methods, as well as a brief overview of GPS navigation applications.

3.2 How does GPS Work?

GPS stands for Global Positioning System. It is interesting to think that in 2001 GPS navigation

units first began to hit the shelves. These very expensive devices unlocked a whole new world of

possibilities. When GPS first hit consumer shelves people wanted to know how a device that can

provide your location with an accuracy of about 10m would not have a service fee. Over time, as

the price and popularity of this grew the curiosity associated with GPS began to dissipate. It was

something that was just there and did its job. So how exactly does it work?

To begin with we need to understand that the GPS network we all use is owned and operated by

the United States government, mainly the Department of Defense. The DoD doesn’t call the GPS

system just GPS, it goes by the name Navstar. Other countries or groups of countries have their

own systems similar to GPS. For example, the European Union version of GPS is known as

Galileo, named after the famous astronomer. Since Navstar is operated by the DoD it is clear that

the original purpose was for military use. Military use of Navstar will provide accuracy of

10mm! In 1983 the DoD decided to open the use of Navstar up to civilians, however the

accuracy was only up to about 100m for them. This accuracy was degraded so that the focus

could be for military use; however in 2000 this was eliminated. This may explain why there was

an explosion of GPS navigation units in 2001.


Figure 1: GPS Satellite Network [5]

Navstar consists of a network of satellites orbiting the Earth. Each of these satellites transmits is

location and the current time as of transmission. All the satellites will transmit this information at

the exact same instant. A GPS receiver (phone, navigation unit, etc.) will receive this

information. Since each satellite is at varying locations their signals will reach the receiver at a

different moment in time. The receiver then calculates its position based on the time delays

between the receptions of data sent from the satellites. When a receiver has received the signal

from at least four different satellites it is capable of mapping its location in 3D [5].

Figure 2: GPS Sending Signals to a Receiver [5]

3.3 Cellular Triangulation

Triangulation is the method of using the location of other things to locate a specific item.

Cellular triangulation can only be performed if the location of the cellular tower is known. This


information is not made public which makes it very difficult to accurately perform triangulation.

There are techniques such as wardriving to determine the rough location of a cellular tower.

In order to properly perform triangulation a least three signals are required. With the known

location of a cellular tower and strength of signal a rough distance from that tower can be gained.

Repeat this for the remaining two towers that are providing a signal. Using a map or algorithm

on the phone the rough location can be achieved. For example, we have distances of 50m from

tower 1, 200m from tower 2, and 150m from tower 3. By drawing circles on a map with the

given distance from each tower the location is then achieved. The user location will be roughly

near the intersection of all three circles. Theoretically, the more towers available for use the more

precise the user’s location should be.

Figure 3: Triangulation of Earthquake Origin [12]

3.4 Challenges

There are very few challenges remaining in regards to traditional localization. The most

overwhelming issues involve efficiently using the GPS device to optimize battery use. GPS is a

battery drainer and inefficient applications can drain the battery quicker than a user would like.

The other issue involves lack of signal for these methods. Being inside can obscure the strength

and ability to accurately determine a user’s location. Cellular signal strength is at sometimes

completely unavailable or extremely weak. There may also be a lack of cellular towers in the

area to accurately determine a user location.

3.5 GPS Navigation

If a GPS Navigation unit is to be useful it is extremely important that the maps are kept up to

date. Each year roughly 5 percent of roads change. This can mean the addition of stoplights, new

roads, speed limit changes, etc. Each of these things can affect the suggested route by a GPS

unit. A road that was once a two street and is now a one way, since the last update, can cause an

erroneous route to be provided by the GPS navigation unit. This is why there may be navigation

differences between two mapping technologies. GPS satellites do not provide a navigation unit

with the direction required to reach the destination. This information is provided by the software

using the GPS coordinates of both your current location and those of where you wish to go [6].

A modified version of routing algorithms such as A* are used to calculate the route to a given

destination. Unfortunately the majority of mapping companies do not release this information.

The purpose of this is to keep their technology private in hopes of having the best algorithm to


provide the fastest, shortest or any other given type of route. The routing algorithms are

considered propriety information and can provide a competitive advantage.

These algorithms will take into account time of day, average speed, number of stops along the

route, etc. Combining all of this information an estimated best route can be provided. Certain

navigation companies such as Waze will learn as more people use their application. The more

people driving down roads a better estimate of average speed and variations based on the time of

day can be obtained [10]. There is always room for improvement when it comes to mapping

technologies. Companies are constantly working to perfect their algorithms.

4. Proximity Based Location Aware Applications

4.1 Introduction

A proximity based application will answer the following question:

What or who is near me?

One of the original versions of a proximity based application was the Yellow Pages. The Yellow

Pages would be distributed to different areas and list businesses that are located in that specific

area. Yellow Pages are the Yelp without reviews.

Figure 4: Finding a business in the Yellow Pages [11]

There are a vast number of different proximity based applications. Although there is a diverse

and large number of proximity based applications many of them fall into the following categories

[3]:

Travel and tourist guides

Mobile yellow pages

Shopping guides

Buddy finder

Date finder

Road tolling

Location sensitive billing

Advertising


4.2 Architecture

The general architecture of a proximity based application works in the following way.

1. A user loads the application

2. The application will signal the device to activate the GPS adaptor if it is not currently

running.

3. The adaptor will capture the user’s location

4. The application will then send the user’s location and/or other relevant information to the

application server.

5. The application server will then process this information.

6. The server will return the queried data or relevant information to the user.

Figure 5: General Architecture for Proximity Based Applications


4.3 Challenges

The main challenge with the creation of these types of applications

deals with privacy. An application maker will be dealing with

information about either the specific location, or rough location of

users throughout time. If this information is gained it can be used

to learn the identity of a user, who may want to remain anonymous.

For example, in Tinder, you can see mutual friends between

yourself, and the user you are being matched with. Using this

information one can go on Facebook and learn the real identity of

the person in question.

4.4 Yelp

Yelp, in my opinion is the modern day and updated version of the

Yellow Pages. Yelp was founded in 2004 with the idea of helping

others find great local businesses. In this regard Yelp has truly

succeeded. Yelpers, people who use Yelp, have contributed over 53

million reviews and over 120 million unique visitors used Yelp in

the Fourth Quarter of 2013. Any business can create a Yelp

account to help reach customers or to correct information about

their business. Yelp is not an ad-free experience. A local business can purchase an advertisement

inside of Yelp. However, these ads are labeled as such.

The key behind Yelp’s success can be linked to the ability to review local businesses and their

automated software that recommends the most helpful and reliable reviews first [1]. Yelp works

by gaining a user’s GPS location through the phone’s built in adapter. With this information in

hand a list of local businesses and categories is provided for the user to browse and find the

company in their area they are interested in.

4.5 Tinder

Tinder is a proximity based “dating” application. Tinder works by

using OAUTH to allow a user to create an account with their Facebook

information. The user’s Facebook pictures, age and about me section

are pulled into the application. A user will then modify his/her

description to display what they want about themselves. The will also

choose from pictures currently attached to their Facebook profile or on

their phone to display to other users. After completing their profile the

user can set their options.

From the figure to the left, one has the ability to set which gender they

are interested, the age range they are interested in and then the distance

from their current position. To determine the user’s current position the

phone’s built in GPS adapter is used. The application will get the

user’s position and then using an algorithm, most likely Euclidean

distance, they find other users within the specified range that meet the

specific requirements previously set by the user.

Figure 6: Nearby Fayetteville, AR

Figure 7: Tinder Options


Once a user has been found that meets the requirement that user’s profile is loaded. The screen

displays the found user’s profile image, an x button, an ‘i’ button and a heart button. Pressing

each of these buttons will perform a different functionality. Pressing the x or swiping left will

produce Figure 8 as well as bringing up the next qualified user.

Figure 9: Results of Dislike Figure 10: Results of Liking

\If you are interested in finding out more about the person meeting your requirements you can

click on their picture or the ‘i’ button. This will bring up the full user profile allowing you to see

more pictures and read their description. If you find the user attractive you can swipe to the right

or press the heart button. This produces Figure 9. If it turns out the person that was found

attractive also finds the user attractive Tinder will alert both users. This will then provide the

users a chance to communicate with each other.

Where things go from there is truly up to the users. Tinder is a rather simple proximity

application that has brought a spun to the dating world. Rather than worrying about matching

algorithms it allows users to set specific requirements and use looks or a witty description in

their profile to grab someone’s attention.

5. Indoor Localization

5.1 Introduction

Indoor localization is still very much in its infancy. The most accurate form of indoor

localization is very expensive. It requires placing specialized hardware in specific locations.

These are then used to calculate a person’s approximate location. Using specialized hardware is a

fairly accurate method for determining location. However, due to the cost people have begun to

turn towards using Wireless Signals to determine a user’s location. To examine these techniques

Figure 8: Tinder Conversation


and how they are being developed one research paper will be analyzed. Others have come up

with rather interesting techniques which will be explored in the section over SurroundSense.

5.2 Wi-Fi Localization Using RSSI Fingerprinting [9]

There are two main methods for determining the user’s location based on the Received Signal

Strength Indicator (RSSI). The first method involves triangulation. The goal of triangulation is to

map RSSI as a function of distance. If this method is to be used than a steep linear

characterization curve will be used. A function will be used to describe the curve, and from this

information determine the approximate (x,y) location. This method is much simpler than

fingerprinting; however it is not as accurate. The second method of locating a user is through

fingerprinting. Fingerprinting creates a radio map of a given area based on RSSI data from

several access points and generates a probability distribution of RSSI values for a given (x,y)

location. Real-time RSSI values are then compared to these fingerprints to determine the user’s

location.

The authors of the paper “Wi-Fi Localization Using RSSI Fingerprinting” focus on using

Fingerprinting to track children inside of a playground. To do this they created a floor plan of the

playground and created fingerprints in 10 foot increments in both x and y directions.

Figure 11: Fingerprint Flow Chart [9]

To gather the RSSI values from each of the fingerprints a fingerprint gathering utility was used.

When using this utility the data owner (person performing the fingerprinting) will enter the

current (x, y) coordinate as well as web URLs to status pages for the given routers nearby.

However, if a data owner doesn’t know all of the routers in the area this can be an inefficient

method. The WiFi Manager functionality built into the Android OS can help ease this process if

an application to perform this specific function is created.


With this information a fingerprint map is created. Along with this, a parser is used to build the

RSSI probability distribution for each reference point.

Two prediction methods can be used to determine the application user’s location. The first

method is to use the Nearest Neighbor. The nearest neighbor simply calculates the Euclidean

distance between the real-time readings and those that have been stored at the fingerprint. The

minimum Euclidean distance is the nearest neighbor and the likely (x, y) location.

√∑(𝑅𝑖 − 𝐹𝑃𝑖)2𝑛

𝑖=1

R represents the real-time RSSI values. FP represents the fingerprint RSSI value.

The second option for prediction location is to use a Markov model. Using a Markov to predict

the location consists of two steps. The first step is to perform the prediction and the second step

is correct the prediction. The prediction model calculates the probability that a user will be given

at a certain location given a previous location and time the user was there. The correction step

calculates the probability that a user is at a given location given the RSS values at a given time.

Upon testing their methodology using Nearest Neighbor was more accurate due to the ability to

look at all fingerprints at any given time. When the Nearest Neighbor predicted an incorrect

location it was almost always immediately fixed. However, when the Markov predicts an

incorrect location it is extremely difficult to predict an additional correct location. Based on the

table below it is clear that both have a fairly low mean error rate.

Figure 12: Error Distance Measured in Feet [9]

5.3 SurroundSense [2]

When analyzing the definition it covers a broad range. Location can stand for the current

coordinates of a user or a logical location, such as Qdoba. Within the realm of computer science

there have been very few attempts at recognizing logical location. With the lack of skill in

recognizing logical many people assume

that physical location can be used to

determine logical location. However, that

is not the case. As mentioned earlier GPS

has an accuracy of roughly 10m and WiFi

localization can have an error of up to 30+

meters. If a user is standing next to a wall

dividing two stores, the physical location

may not be enough to determine which

store the user is actually inside. Figure 13: Logical Location cannot be Determined with GPS [2]


On top of this, WiFi may not be available in developing regions. Besides the lack of precise

accuracy within WiFi localization this lack of WiFi is also an issue. However, mobile phones are

becoming more popular in these regions.

SurroundSense aims to solve this issue by combining the effect of ambient sound, light, color,

and motion an accurate logical location can be provided. This can also be described as using a

comparison of the overall ambience of a given location to determine its logical identity. Think

about the ambient sound of different stores or logical locations near you. The sound inside of a

piano bar is very different from that of a nightclub or bookstore. The piano and people singing

along can be used to help identify a piano bar. The thumping of bass or people yelling could be

used to identify a nightclub. An overall quiet ambience with few people talking could be used to

identify a bookstore. However, on their own these are

not enough to correctly identify the exact logical

location. Combine this with the thematic colors inside

of the location, how the floor looks and even the

lighting a picture will begin to form as to the exact

location. Add on to this the type of motion being

performed by a user and the WiFi signals nearby an

even more precise picture can be painted.

The overall architecture of SurroundSense will be explained along with how the system was

created; however it would take too many pages to explain all of the details behind their

methodology.

Figure 15: SurroundSense Architecture [2]

The first portion of determining the logical location is to use sensors built into the phone to

gather data. The sensors used include the WiFi adaptor, camera, microphone, GSM antenna and

the accelerometer. Upon collecting this information the accelerometer, WiFi, microphone and

camera data is sent to the “Fingerprinting Factory.” This is where all of the key attributes from

each sensor will be extrapolated and used to determine the precise logical location. In order to

prevent the comparison of all Fingerprints to the sensed data the GSM location, which has an

accuracy of roughly 150m, is sent to the Candidate Selection component. This component will

Figure 14: Ambience of various locations [2]


take the location and transmit it to the Geographic database. The geographic database stores a list

of logical location. The database will then return the list of locations within the 150m radius to

the Candidate Selection Component. This component will then send the list of locations to the

Fingerprint database. The Fingerprint database will then compare the list provided by the

candidate selection component to the data it has stored. If any of the locations match they will be

sent to the matching component. The Fingerprint Factory will send its processed information to

the matching component as well. The matching component will then compare the fingerprints

provided to the sensed and processed data. The results will then be ranked based on probability

of being the correct location. The top ranked location will then be output as the logical location.

Fingerprints need to be created for SurroundSense to work properly. To do this the authors of [2]

gather sensor data from various locations under various settings. The end result was that 85% of

the time the correct location was output. There is still much work to be done in improving the

accuracy.

6. Human Mobility Prediction

6.1 Introduction

Academic research has long focused on solving the problem of human mobility prediction.

Human mobility prediction is the ability to predict the next location a user will visit at a set time.

This can be applied to a vast array of application types. Human mobility can be used to better

understand human behavior, migration patterns, the evolution of epidemics and the spread of

disease [7]. As discussed earlier, location aware applications are growing in popular and with

this growth an increased interest in human mobility prediction has come to focus.

6.2 Challenges

It is currently extremely difficult to get accurate predictions for human mobility. This will be

discussed in sections 6.3 and 6.4.

6.3 Mining User Mobility Features for Next Place Prediction in Location-Based Services [7]

Foursquare is a proximity based mobile application that allows a user to “check-in” at a nearby

location. By checking in at a location the user has provided the exact time and coordinates of the

location being visited. This information can be extremely helpful in predicting human mobility.

By mining the publicly available Foursquare check-ins that have been tweeted a data set of check

in information can be completed. The challenge of this is to be able to predict the exact next

location a user will go out of the thousands of options available.

In order to create a prediction for a user there must be a set of prediction features that can be

analyzed. These include:

User Mobility

o Historical visits

o Categorical Preference – the types of locations the specific user prefers to visit

(ex/ mostly checks in at bars would lead to a preference for bars)

o Social Filtering – this considers the user and his set of friends

Global Mobility

o Popularity – determined by the total number of check ins


o Geographic Distance – Distance from the user

o Rank Distance – describes the relative density between the user and all other

places

o Activity Transitions – Assuming that the succession of human activities is not

random, but more logical. For example a user is more likely to go to the

grocery store after work and not from work to work again.

o Place Transitions – used to predict consecutive transitions of users across

venues

Temporal

o Category Hour

o Category Day

o Place Day

o Place Hour

With this information mobility prediction can be tested for each of these categories. However, it

is not an accurate measurement. By combining all of these features, meaning that one feature

alone is not enough to identify a location; a better picture can be made. The authors of [7] used

an M5 tree and linear regression to test their methods.

The end results of their experiments were M5 Trees performing at a minimum of 50% accuracy

in regards to predicting the next location. Considering the myriad of location possibilities and an

incomplete dataset the work is a wonderful step forward towards human mobility prediction.

6.4 Contextual Conditional Models for Smartphone-based Human Mobility Prediction [4]

This paper uses a generalized predictive framework to predict human mobility. There are two

fundamental questions that must be answered: what is the next place the user will visit and how

long with the user stay at his current location?

In order to satisfy these two questions the authors test conditional probability and an ensemble

method to predict the next location and current duration. The authors also consider that the user

is in an always online setting. This means that with each visit the prediction model is updated.

Over time this model will become more accurate for the user.

There are 8 context variables used:

1. Location – ID of the current place

2. Hour – hour of the day

3. DOW – day of the week (from Monday to Sunday)

4. WE – workday/weekend indicator

5. FREQ – frequency of visits to the current place. It is broken into 5 possible values

based on the number of visits.

6. DUR – the average visit duration of the current place

7. BT – the number of nearby BT devices during the first 10 minutes of the visit

8. PC – binary value which indicates if the user makes a call or sends an SMS during the

first 10 minutes of the visit

Conditional probability models are then created from this information as well as combining them

all in an ensemble method.


`

Figure 16: Results for predicting next location [4]

Based on the above results it is clear that their ensemble method is more accurate than using

plain conditional probability. It is also nice to note that the accuracy of the ensemble method is

great than the accuracy achieved in Section 6.3.

Figure 17: Results for predicting duration stay [4]

Human behavior in regards to length of stay at a location is overwhelmingly similar. The authors

compared personalized accuracy to a generalized method with similar results. In the end the

main contributors to an accurate duration stay was just the current hour and location.

Unfortunately based on the table above there is still a lot of work to be down on predicting stays

between 1-8 hours.

Based on their work the authors believe they could improve the accuracy with a larger dataset.

7. Future of Location Aware Applications

The future of Location Aware Applications is vast. Some people are working on GPS navigation

systems that utilize the most fuel efficient routes rather than the fastest or shortest routes. Others

are working on utilizing cameras in conjunction with GPS positioning to determine if the user is

truly in the correct location.

The sky is truly the limit for location aware applications.

8. References

[1] About Us: Yelp. http://www.yelp.com/about (accessed April 16, 2014).

[2] Azizyan, Martin, Ionut Constandache, and Romit Roy Choudhury. "SurroundSense: Mobile

Phone Localization via Ambience Fingerprinting." MobiCom. Beijing: ACM, 2009. 261-272.

[3] Edwards, Alistair, Moritz Neun, and Stefan Steiniger. "Foundations of Location Based

Services." 2006.

[4] Gatica-Perez, Daniel, and Trinh Minh Tri Do. "Contextual Conditional Models for

Smartphone-based Human Mobility Prediction." UbiCom. Pittsburgh: ACM, 2012. 163-172.


[5] Griffin, Darren. How does the Global Positioning System work? June 26, 2011.

http://www.pocketgpsworld.com/howgpsworks.php (accessed April 16, 2014).

[6] How does GPS Mapping Software Work? http://www.mio.com/technology-gps-mapping-

work.htm (accessed April 16, 2014).

[7] Lathia, Neal, Cecilia Mascolo, Anastasios Noulas, and Salvatore Scellato. "Mining User

Mobility Features for Next Place Prediction in Location-based Services." International

Conference on Data Mining. IEEE, 2012. 1038-1043.

[8] Location-based Service: Wikipedia. http://en.wikipedia.org/wiki/Location_based_service

(accessed April 16, 2014).

[9] Navarro, Eduardo, Benjamin Peuker, and Michael Quan. Wi-Fi Localization Using RSSI

Fingerprinting. San Luis Obispo: California Polytechnic State University, 2010.

[10] Routing Server: Waze Wiki. https://wiki.waze.com/wiki/Routing_server (accessed April 16,

2014).

[11] Stewart, Mike. State of the Yellow Pages Industry. http://www.smbseo.com/state-of-the-

yellow-pages-industry (accessed April 16, 2014).

[12] What is Triangulation? http://www.qrg.northwestern.edu/projects/vss/docs/Navigation/1-

what-is-triangulation.html (accessed April 16, 2014).

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Chapter 18 Location Aware Applicationscsce.uark.edu/~cwt/COURSES/2014-01--CSCE-4543--SW... · Chapter 18 – Location Aware Applications ... accessible with mobile devices through