中文摘要隨著商業活動的頻繁及運輸科技的蓬勃發展，世界各地的距離無形中相對縮短，“定位”也漸漸在現代人的生活中成為一項重要的需求，其中GPS(Global

Positioning System)藉由衛星群構成的網絡及送出的同步訊號來導引使用者的位置，是目前商業用途最普及的戶外定位方法，然而，受限於傳輸方式本身的物理特性，其訊號無法穿透到室內或容易被高樓遮蔽，有其使用上的盲點。

為了解可能替代的技術及現有GPS系統缺失的輔助方法，我們根據訊號可取得的方便性，選定了GSM及Wi-Fi兩種普及性較高的無線網路來探討定位系統的適用環境及各自的優缺點，以評估GPS的可取代性及探討如何利用其他無線網路來輔助GPS的盲點。本研究將兩種不同訊號源所組成的系統實際安裝在行動裝置(手機)上，收集包括商業區、學校、以及高速高路上的定位資料，透過實驗數據了解及探討在行動裝置上定位的實際問題，以及不同定位方法的優缺點，最後並加以探討行動裝置定位潛在的應用領域。

iv

Abstract

The world is becoming smaller as a result of evolving transportation technology and the

increasing needs for commercial activities. In the meanwhile, the need for positioning is

gradually becoming a part of our modern lives. Among all the positioning technologies,

GPS (Global Positioning System), which a user locates its own position by receiving

synchronous signals from satellites constellations, is one of the most popular methods in

civil outdoor positioning. Nonetheless, the signal can be easily blocked by concrete and

steels due to its physical limitation. The application of GPS is then limited in outdoor

application, especially in rural area,

To compensate GPS’s weakness and consider the accommodation of current

technologies, we choose two kinds of signals, which are pervasive in nowadays, GSM

and Wi-Fi, to compare their performance with different methods and under different

environments. To conclude a realistic result, we deploy the positioning programs in real

cellular phones, which are capable of GSM and Wi-Fi radio transmission. Our testbed

includes commercial, rural areas, and one high way around Taipei. Following the

experiment results, we discuss practical problems that might be encountered when

localization system is deployed, compare the pros and cons of each localization method,

and discuss their potential applications afterward.

v

Contents

Chapter 1 Introduction................................................................................................... 1

1.1 Positioning Technologies....................................................................11.1.1 GPS.......................................................................................................... 2

1.1.2 GSM and Wi-Fi localization.....................................................................2

1.2 LBS (Location-Based Services)...........................................................31.3 Thesis Organization..............................................................................4

Chapter 2 Implementation.............................................................................................. 5

2.1 (Weighted) Centroid positioning.........................................................52.2 Fingerprinting........................................................................................62.3 SmartPhones and GPS.........................................................................72.4 Positioning software.............................................................................82.5 Radio beacons.......................................................................................92.6 Error distance........................................................................................9

Chapter 3 Experiments and Evaluations.......................................................................11

3.1 Urban Area...........................................................................................113.2 Rural Area............................................................................................133.3 Highway...............................................................................................153.4 Practical Issues...................................................................................16

3.4.1 Limited RSSI sources.............................................................................16

3.4.2 Resource constraints on mobile devices..................................................17

3.4.3 Training condition..................................................................................17

3.4.4 Unit of RSSI readings............................................................................18

Chapter 4 Location-aware Applications.......................................................................19

3.1 Mobile Location-Based Services.......................................................194.2 Pervasive mobile game.......................................................................21

Chapter 5 Related Works.............................................................................................26

Chapter 6 Conclusion and Future Work.......................................................................27

vi

List of Figures

Fig. 1. People skiing outdoor............................................................................2

Fig. 2. Centroid Positioning (the coordinate of beacons is a prior knowledge).. 4

Fig. 3. Fingerprinitng (no need to know the beacons coordinate)......................5

Fig. 4. The cellular phones and GPS receiver used for the research..................7

Fig. 5. The Training system software architecture …………………………… 7

vii

viii

Chapter 1 IntroductionAs the idea of pervasive and ubiquitous computing becomes popular, accurate

positioning plays an important role in the domain where location-aware applications

need precise, or sometimes roughly, geographic information. Once the location is

known, plenty of applications can be employed afterward.

In 1996, the U.S. Federal Communications Commission (FCC) directed the rules that

wireless service companies have to provide location identification for wireless

emergency call, and the accuracy of location estimation should be within 125 meters

[1]. For telecom operators, localization has been booming for both emergency and new

services (profits) consideration. Since the operators are the owner of the network

infrastructure, it is natural and economic for them to develop the system.

In this paper, we will compare the performance of GSM and Wi-Fi localization.

1.1 Positioning Technologies

Positioning technologies have been research topics for years. GPS may be one of the

most well-known technologies. GSM or 3G(UMTS/CDMA) are also important for its

pervasive existence. Recently, Wi-Fi is becoming popular as the need of wireless

communication increases and the standard of IEEE 802.11 becomes mature. Other

technologies include Bluetooth, Zigbee, Infrared, Ultra-sound, RFID, A-GPS..etc. In

this paper, we will focus on GSM and Wi-Fi performance in localization.

From the computing point of view, positioning technologies can be classified into

Network based and Mobile Station based localizations. Network based localization

takes advantage of network deployment and calculates a user’s position by coordinating

its network facilities or via its synchronous signals. Since the computing of a user’s

location is processed in core networks, the design of a mobile device can be as simple

and is power efficiency. On the other hand, as the method may locate a user without his

awareness, it could infringe a user’s privacy if this information not carefully handled.

Mobile Station based localization use mobile devices’ computing unit to calculate its

1

own position. Given the needs of positioning database and the ability to calculate the

position in time, the mobile device should equipped with a powerful processor if

Mobile Station based localization is needed. This will raise a problem of computing

resource and battery life on mobile devices.

1.1.1 GPS

Among all other location estimation technologies, the most well-known satellite

navigation system was developed by United States Department of Defense, officially

named NAVSTAR GPS (Navigation Signal Timing and Ranging Global Positioning

System). The system is free for civilian use as a public good. Since the thawing of the

Cold War, a number of companies have discovered commercial applications for the

Global Positioning System, a constellation of 24 satellites that can pinpoint the location

of people or objects with ground receivers. Today, the technology does everything from

helping ships and planes get from one point to another to measuring the movement of

glaciers. Auto makers offer GPS in their new cars as an upgrade, for a couple thousand

dollars. However, due to its physical limitation and requirements of LOS (line of sight)

to the sky, GPS signals can hardly penetrate steel or concrete buildings. Thus, the

application of GPS is constrained in outdoors, especially in rural area.

1.1.2 GSM and Wi-Fi localization

As mentioned that GPS has limited ability in localization, we are considering looking

for other radio mediums that can operate in NLOS (non line of sight) mode. Two kinds

of signal sources are considered in this paper, GSM and Wi-Fi, due to their ubiquitous

existence in modern lives. The Global System for Mobile Communications, GSM

(original acronym: Groupe Spécial Mobile) is the most popular standard for mobile

phones in the world. GSM service is used by over 2 billion people across more than 212

countries and territories. The ubiquity of the GSM standard makes international

roaming very common between mobile phone operators, enabling subscribers to use

their phones in many parts of the world. Wi-Fi is a brand to describe the underlying

technology of wireless local area networks (WLAN) based on the IEEE 802.11

specifications. It was originally developed to be used for mobile computing devices to

constitute LANs, but is now increasingly used for more services, including Internet and

data access, online gaming and connectivity of consumer electronics or intelligent

2

appliances. Recently, the standard of WiMAX (IEEE 802.16) has been mature, and it is

likely to be the solution of “last mile” wireless broadband access. Due to radio spectrum

license and high cost of chip price, compared with Wi-Fi, WiMAX is not as popular as

Wi-Fi nowadays, but it is likely to be pervasive given its broader bandwidth and wider

coverage. Moreover, WiMAX gains supports from heavyweight companies, like Intel,

Motorola, and Fujistsu..etc. This is an important criterion to success.

One important thing to be mentioned, Taipei city’s wireless network has named the

largest and densest Wi-Fi network in the world [22]. That means we can gain access to

Wi-Fi signals in most of Taipei area and therefore it has become a good candidate for

wireless localization system. In our work, we deploy our system on real cellular phones

and compare the performance of GSM and Wi-Fi localization. Both (weighted)

Centroid and fingerprinting methods are implemented and compared in our

experiments.

From the experiment results, the Centroid algorithm is recommended to be used

outdoor because of its low training cost and relatively low computing power. Usually,

since in outdoor environment, the area is quite large, we have to collect lots of data

before the tracking can take place. Due to limited CPU and storage resources within a

mobile device, a simple method to collect/calibrate training data and tracking is

necessary. Fingerprinting method is recommended to be used indoor because this kind

of application may often need a more precise accuracy, which a deliberately well

trained fingerprinting system can do. This is a tentative conclusion, but not absolutely a

rule to follow as technologies evolve exponentially. The computing power and storage

problem on a mobile device may not be a problem soon.

1.2 LBS (Location-Based Services)

LBS (Location-Based Services) have become an emerging domain of interests by

industries, while this kind of service may customize each consumer and offer a better

personalized service. Besides, location-aware gaming is also a interesting research topic

[16,18,19]. By associating the location of the player with the script, game designer can

create more interesting game which people can interact between cyber and real worlds.

3

Fig. 1. People skiing outdoor (Hakuba, Japan)

1.3 Thesis Organization

This paper starts by introducing localization technologies in nowadays. We then

propose a study and comparison between GSM and Wi-Fi localizations. Chapter 2

describes the mobile devices and system implementation. Chapter 3 presents the

conclusion of our experiments. Chapter 4 discusses the potential application of

localization system. Chapter 5 provides related research projects, followed by

conclusion and future works in Chapter 6.

4

Chapter 2 ImplementationIn order to produce a realistic and practical result for the comparison between GSM and

Wi-Fi, in this work, we deploy the localization system on real mobile devices. We use

two kinds of Smartphone and one GPS receiver. They are Dopod 585 (Windows

Mobile 2003 SmartPhone Edition), Dopod 586w (Windows Mobile 5.0 SmartPhone

Edition), Leadtek 9559X Bluetooth GPS receiver (SiRF Start III), and a laptop to

facilitate the comparisons and calculate error distances. Since the cellular phone can

provide only one GSM operator’s signal at a time, we use Chung-Hwa Telecom’s SIM

(Subscriber Identification Module) card as it is the largest telecom operator in Taiwan.

The CHT GSM operates on both 900MHz and 1800MHz frequency bands.

Three kinds of positioning methods are implemented in our experiments, Centroid,

Weighted Centroid, and Fingerprinting respectively. Centroid and Weighted Centroid

have to know the beacon addresses in advance, while Fingerprinting just need to record

the radio fingerprints without the knowledge of beacon addresses. We do not apply

motion models in tracking stage during the comparison.

2.1 (Weighted) Centroid positioning

Centroid positioning [3] method assumes the location of each beacon a prior knowledge

of the system. We start from collecting beacon coordinates and use it as a database in

tracking stage. While the Centroid is working, it simply detects all available beacons

and then calculates the arithmetic mean of those beacons coordinates.

Weighted Centroid, as its name implies, add a weight to each beacon according to the

perceived signal strength while calculating the centroid. Weighted Centroid can provide

a better accuracy while slightly increasing a little bit computing resource.

The method does not need to model the radio propagation. Furthermore, if we can

cooperate with telecom operators who have the beacons coordinates together with their

network plan, we can thus reduce the effort of training. (Weighted) Centroid takes less

computing power, compared with Fingerprinting method, which is critical to a mobile

device. It is quite suitable to deploy the method on a mobile device.

5

Fig. 2. Centroid Positioning (the coordinate of beacons is a prior knowledge)

2.2 Fingerprinting

Fingerprinting [5] is a method to collect radio fingerprints in pre-defined grids, make

them a database, compare the signal strength with trained fingerprints, and find the best

match during tracking stage.

In our experiments, we construct a fingerprint database with grid size of 10x10 meters

outdoor. For an area like Hsin-Yi District in Taipei, the size of database is more than

4Mb. The response time goes down as database increases.

There are 2 ways to collect RSSI (Radio Signal Strength Indication) fingerprints in

training stage. One of both, like RADAR[5], plans several grids in a specific area,

collect an amount of radio fingerprints in a static point, and save the arithmetic mean of

perceived data to that grid. The database is formed after finishing all the grid training

works. The other way is to save RSSI fingerprints as a single grid each time when the

device scans the radio sources. Thus, people can driver or walk along the road or

hallway without staying in a static point for a certain amount of time for training. The

second method is quite efficient when the radio source is stable.

Generally speaking, Fingerprinting positioning may take a longer time in training and

additional effort in calibration as radio fingerprints or the infrastructure might change

over time. The method will consume more processor resources than Centroid on the

mobile device because the location estimation time is O(N), while Centroid takes O(1).

6

Therefore, it is relatively not suitable for the applications in mobile devices, especially

when the training database is large.

R11 R12 R13 … …

R21 R22 R23 … …

R31 R32 R33 … …

R41 R42 … …

R51 R52 R53 … …

R61 R62 R63 … …

2.3 SmartPhones and GPS

As technology evolves, the computing power of a cellular phone grows dramatically so

that we can run additional localization programs on the platform. Here we choose

Dopod 585 and Dopod 586w [12], which produced by HTC, a Taiwanese company, and

may have other names in different countries.

Dopod 585 is of Windows Mobile 2003 SmartPhone Edition and Dopod 586W is of

Windows Mobile 5.0 SmartPhone Edition. Both are of commercial GSM phones. We

choose Windows CE platform because of its complete development environment

provided by Microsoft Visual Studio 2005 and it is convenient to deploy a user’s

program to the phones or debug the system during run time.

Since the phones that we use do not equip with a GPS module, an external Bluetooth

GPS receiver is introduced in some of our experiments as a ground truth. The GPS

module can transmit a coordinate to the host phones via Bluetooth connection.

7

Fig 3. Fingerprinitng (no need to know the beacons coordinate)

Fig. 4. The cellular phones and GPS receiver used for the research

2.4 Positioning software

Thank to Intel Research’s open source project POLS [4], we save a lot of time by using

parts of their project codes while reading GSM/Wi-Fi signals from cellular phones.

With the help of POLS, we can focus on specific application programs that meet our

research targets.

Most of our software programs are written in Microsoft Visual C#, with some in C as

external libraries. For GIS map, we use Mactiontech’s Papago SDK [22], which

supports Microsoft Smartphone 2003 edition.

For the purpose of comparison between GSM and Wi-Fi location tracking system,

several programs are created to facilitate the experiments.

a. Centroid / Weighted Centroid training: A program to collect and calculate beacon

coordinates and result in the form of a Centroid database.

b. Centroid / Weighted Centroid tracking: A program to read RSSI from beacons

(GSM and Wi-Fi), calculate the centroid, and compare the estimated position with

actual coordinate, observed by the GPS module.

c. Fingerprinting training: A program to collect a specific amount of RSSI from

beacons (GSM/Wi-Fi) on a static point, calculate the average RSSI, record the GPS

coordinate, and then save them as a fingerprinting database.

8

d. Fingerprinting tracking: A program to collect RSSI from beacons (GSM/Wi-Fi),

find the most similar grid from the database, and compare the error distance with

current actual coordinate, observed by the GPS module.

Fig. 5 The Training system software architecture

Fig. 6 The Testing system software architecture

2.5 Radio beacons

GSM deployment is available in most of the area in Taiwan. Wi-Fi signals are available

in Taipei city as it claims to be the largest Wi-Fi city in the world. In our outdoor

experiments, we choose Hsin-Yi District as the testbed because it is a place with lots of

commercial activities. It is supposed to have more GSM cells than other non-

commercial areas. Within the 2k㎡ area, GSM cell density is 37 cell/k㎡ while Wi-Fi

AP (Access Point) density is 500AP/k㎡ . In NTU (National Taiwan University)

campus, the GSM cell density is 27 cells/ k㎡ ; AP density is 8000AP/ k㎡ . Along

Formosa high way, the GSM cell density is 18 cell/k㎡ while we can not perceive a Wi-

Fi signal in the same route.

2.6 Error distance

Error distance is the result that we need to while comparing with different localization

methods and environments. GPS is used as the ground truth in our experiments. In

9

LogRecording

Training Mediator

System Callback

Wi-Fi Module

GSM Module

LogRecording

Testing Frequency

System Callback

Wi-Fi Module

GSM Module

training phases, for both Centroid and Fingerprnting methods, we generate location

database with GPS coordinates. In testing phase, we calculate a user’s estimate GPS

location and compare it with real GPS coordinates. The distance is calculated by Great

Circle Distance Formula.

Let be the latitude and longitude of two points, respectively, the

longitude difference and the angular difference/distance

10

Chapter 3 Experiments and EvaluationsExperiments are performed in urban, NTU campus (rural area), and a high way

(Formosa High Way) respectively. Except the high way, where we could not find a Wi-

Fi signal along the route, we apply both Centroid and Fingerprinting methods to the

areas for comparison.

3.1 Urban Area

In urban area, we choose Hsin-Yi District, in Taipei, as our testbed. Hsin-Yi District is

a place of 2K㎡ and is one of the most busiest commercial area in Taipei. We suppose

there be more GSM cell towers and denser AP distribution. From data collection stage,

we found 37 cell towers and 497 Wi-Fi APs in the area.

For Centroid positioning method, we found the average error of location estimation is

264 meters, whereas Weighted Centroid is 164 meters. The average error of location

estimation for Wi-Fi is 67 meters, whereas Weighted Centroid is 85 meters.

Since Centroid is to calculate arithmetic mean of all observed beacons, it is apparently

that the accuracy is proportional to the density of beacon distribution. So we observed a

better accuracy in Wi-Fi, compared with GSM. An interesting result from the

11

Fig 7. Cell tower distribution in Hsin-Yi District.

experiments is the Weighted Centroid gets worse results than just Centroid with Wi-Fi

localization. One possible reason to explain the phenomenon is that the APs distribution

in the area does not scatter in the same plane, but locate in various levels of heights.

Thus, if we detect a weak signal of Wi-Fi while actually it is nearby but is located high

above, we will give it a lower weight and result in a larger error distance.

Therefore, in an area of dense high buildings, Centroid positioning would have a better performance

than Weighted Centroid.

Hsin-Yi District Centroid Localization　 Avg. Error

(M)Max.

Error(M)Min.

Error(M) STD(M) Beacon No.

GSM (Centroid) 264 639 11 148 37GSM (Weighted) 164 519 18 100 37Wi-Fi (Centroid) 67 290 2 56 497Wi-Fi (Weighted) 85 292 2 49 497.

For Fingerprinting positioning method, we set the size of each grid to be around 10x10

meters and record the average RSSI after collecting 20 scans of beacons in a stationary

point. We observed a relatively bad result than Centroid method in the area. The

average error distance of GSM localization is 226 meters; average error of Wi-Fi is 179

meters.

<<<< burt note: this is the preliminary results….. should change a device and do

again the test for verification >>>>.

12

Fig 8. Centroid/ Weighted Centroid Localization results in Hsin-Yi Dist,

It is hard to say whether the observed error distance 179~226 meters is suitable for

localization or not since it is in outdoor environment. People might miss a whole block

with such a big error, but some might enjoy the ambiguity while playing localization

games [18,19].

Hsin-Yi District Fingerprinting Localization　 Average(m

) Max(m) Min(m) STD(m)GSM (fingerprinting) 226.78 384.4 17.8 114.97Wi-Fi (fingerprinting) 179.28 413.5 2.3 107.53

3.2 Rural Area

To evaluate performance of GSM/Wi-Fi localization system in rural area, we choose

NTU campus as the testbed. Since wireless network is popularly used in campus, we

found lots of Wi-Fi beacon during experiments while the GSM cell towers are less than

those in commercial area. NTU is a place of 1Km2. We observed 27 GSM cell towers,

and 859 Wi-Fi Access Points around the campus.

13

Fig 10. Fingerprinting Localization results in Hsin-Yi Dist.

Fig 9.Wi-Fi AP distribution in Hsin-Yi District.

For Centroid positioning method, as stated in previous section, the density of GSM cell

tower is fewer than the Hsin-Yi Dist, so we have a slightly worse result with average

error distance of 216 meters in Centroid and 198 meters in Weighted Centroid. The

average error distance of Centroid Wi-Fi is 48 meters; Weight Centroid is 37 meters.

Though the NTU campus has a high density AP distribution similar to Hsin-Yi Dist.,

the result is quite different. In NTU campus, unlike Hsin-Yi Dist., Weighted Centroid

of Wi-Fi localization is slightly better than Centroid. We think it is because most of the

buildings in campus are below 5 or 6 floors whereas there are lots of skyscrapers in

Hsin-Yi Dist., including the famous Taipei 101 building, which has 101 floors.

NTU Centroid Localization　 Avg. Error

(M)Max.

Error(M)Min.

Error(M) STD(M) Beacon No.

GSM (Centroid) 316 730 15 168 27GSM (Weighted) 198 611 14 132 27Wi-Fi (Centroid) 48 391 4 54 859Wi-Fi (Weighted) 37 114 3 23 859

For fingerprinting positioning method, we still get bad results as it does in Hsin-Yi Dist.

14

Fig 11. GSM cell tower distribution around NTU campus

Fig 12.Centroid localization results in NTU campus

<<<< burt note: this is the preliminary results….. should change a device and do

again the test for verification >>>>.

In a campus, it is almost certain that we can find the signals from different APs in every

corner. Thus, with the sound result of Wi-Fi localization, Wi-Fi is a very good

candidate for localization system in campus. 37 meters may be good enough for a

student to distinguish from different buildings.

NTU Fingerprinting Localization　 Average(m

) Max(m) Min(m) STD(m)GSM (fingerprinting) 231 414 52 100Wi-Fi (fingerprinting) 192 250 5 101

3.3 Highway

In additional to urban and rural area, we also conduct an experiment in the condition of

high speed driving. We choose Formosa High Way, which stretches from Taipei to

Hsin-Chu. We would like to discover the feasibility and performance of high speed

tracking with GSM signals.

15

Fig 14. Fingerprinting Localization results in NTU campus

Fig 13.Wi-Fi AP distribution around NTU campus

One thing to be mentioned is that we can only find two Wi-Fi APs along the 50km long

highway. So Wi-Fi localization is completely not suitable in the environment. The

experiment is done with average car speed of 100~110km/h. We observed an average

error of 744 meter with Centroid and 425 meters with Weighted Centroid. Assuming a

user drives at 100km/h speed in the high way, 425meters is around 15.3 seconds of

time. It seems the error is tolerable that we might miss only 15 seconds along the high

way, but may not suitable for drivers’ guide in interchanges because the error is big

enough for a driver to miss his right time to get off the highway.

Formosa Highway Centroid Localization　 Average Error

(M)Max.

Error(M)Min.

Error(M)STD(M

)Beacon

No.GSM (Centroid) 744 3192 35 564GSM (Weighted) 425 2576 16 371

16

Fig 16. Fingerprinting Localization results in Formosa highway

Fig 15. GSM Cell tower distribution alongside Formosa highway

3.4 Practical Issues

During the experiments, we discovered some of practical issues that would affect the

result of localization. We have to take these factors into consideration if the localization

system is going to be deployed to real world.

3.4.1 Limited RSSI sources

If the application, like library guidance system, needs precise location information, a

rich source of beacons will be a critical factor to bringing sound localization results.

Nonetheless, due to the embedded design with most cellular phones, we probably have

no way to know how to read RSSI from different radio sources or drivers. Even if we

could read the data from some of the cellular phones, it is far less than the expected

numbers to construct an accurate localization system. In our experiments, Dopod 585 or

586w can output 6~7 cell IDs with RSSI, but that is not enough to achieve a good

accuracy. The average error is around 150 meters in our experiments, whereas Wi-Fi

can achieve 30 meters with around 15~20 beacon readings.

For GSM localization, if we use BCCH (Broad Control Channel) to replace cell ID as

mentioned in [11], we can have richer signal sources, normally 15~18 readings for the

channels. The accuracy can then achieve 5 meters if we use channel ID to reconstruct

the GSM localization system [11]. However, due to the nature of channel reuse in GSM

networks, channel ID will be duplicated if we pass across several cells. Thus, method of

using channel ID is suitable to be applied to a small area, where each channel number is

unique in the area.

From our results, Wi-Fi signal seems good enough in both campus and commercial

area. The accuracy can achieve 30 meters. Instead, in the wild area, the Wi-Fi signal

sources are far fewer than GSM cell or nearly none. It is not possible to use Wi-Fi RSSI

as a mean to localization in such an environment.

3.4.2 Resource constraints on mobile devices

Normally, in a fingerprinting localization system, after the finding of possible grids, we

may need to apply KNN (K-nearest neighbors), Cluster, Particle filter, or other filters to

find the grid of highest probability. These methods would be useful but they introduce

17

extra computing efforts as the mathematical calculation for the filters consume lots of

computing powers on the device. Due to limited computing resources on cellular

phones or mobile devices, it is not proper to use a complicated motion algorithm

together with the tracking programs.

On the other hand, Centroid algorithm use just simple arithmetic add and division in the

tracking stage. It relatively consumes less CPU resources, and thus may be a good

solution for localization system on a mobile device.

From the data storage point of view, in the Fingerprinting system, the data occupy

around 850 bytes (including 33 beacons) in each grid, assuming 10*10 meters grid size.

According to the sample above, if we need to construct a database for a place of 1Km2,

we will need at least 10,000*850 = 8,500,000 (bytes). For a city like Taipei, it is of

272Km2. That is to say, we may need 8,500,000 * 272 = 2,312,000,000 (bytse) which

is a big volume that we cannot even put it in a 2G memory card on mobile device. In

Centroid localization system, a typical storage space of a beacon is 43 bytes. Assuming

the density of beacon in a city is 500 beacons/Km2 in average, in Taipei city, we will

need 43*500*272 = 58,480,000 (bytes) to complete a radio map. This is relatively a

more feasible way for designing a large scale localization system on mobile device.

3.4.3 Training condition

In Centroid localization, it is important to get more beacons as the tracking result is

proportional to the number of beacons. During our experiments, we use the Cooking

Algorithm, provided by POLS, to find all available beacons. If we collect the radio

sources by wardriving, the number of total beacons will be fewer than the result from

walking. We get a better result in collecting signals by riding a bicycle or walking.

<<describe the cooker’s algorithm here… and its parameters to interfere the result of

training>>……

In Fingerprinting localization, the accuracy may depend on the stability of signal

sources. Since the radio signal fluctuates frequently in different weather or

environments, it is important to have a stable training data so we will not have a big

error in tracking stage. We see during our experiments that the quality of Fingerprinting

18

localization heavily replies on the quality of training. If we stay on each grid for a

longer time for data collection, the tracking accuracy will improve.

3.4.4 Unit of RSSI readings

We found a fact that the unit of signal reading scale may significantly affect the

accuracy of location estimation. With Dopod 586W, the unit of Wi-Fi RSSI readings is

10dbm. Most of the time, we read -90, -80, or -70dbm from the Wi-Fi driver. Since the

device have only 3 kinds of RSSI, localization system can easily find similar grids in

tracking stage and we will have bad results in consequence.

After we change the device to Dopod P900, which can provide 1dbm variance in RSSI

readings, the tracking results improve significantly from 100 meters to 30 meters(???).

19

Chapter 4 Location-Aware ApplicationsGSM (or UMTS/CDMA) and Wi-Fi (or WiMAX in near future) signals are supposed to

be pervasive in modern lives following the increasing demands for wireless

communication. Due to their physical interface, the ability to penetrate concrete walls is

better than GPS, so we can consider using the kinds of signals to couple or replacing

GPS as an omni-environmental positioning system.

From our Centroid experiments, we observe that the error distance of GSM is 164

meters in urban area, and 198 meters in rural area, depending on the density of GSM

cell towers. The error of Wi-Fi localization is 40 meters in Urban, and 30 meters in

NTU campus. In indoor environment, the error distance is about 5 meters [11] with

GSM and 3 meters [5] with Wi-Fi. However, the methods need considerable training

and calibration efforts if fingerprinting method is adopted.

An idea is to couple GSM and Wi-Fi localization with GPS. In rural area, GPS is used

and is also a reference of GSM and Wi-Fi calibration. In urban canyon or indoor

environment where GPS signals can not arrive, the system can switch to GSM or Wi-Fi

localization. Considering the loading of large database for GSM or Wi-Fi, a user should

download the target map on demand to ease the CPU or memory loading on mobile

devices.

In this chapter, we would like to discuss Location-aware applications that may of

interests to industries or individuals.

4.1 Mobile Location-Based Services

Mobile location-based services can be delivered by telecom operators or other service

providers who take advantage of the networks.

Basic Location Information Service: This is already a commercialized service from

operators in many countries. Telecom operators can provide basic location information

services to their subscribers or provide E911 services via network-based localization.

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Local Search: A service subscriber can connect to the server and search for a specific

topic according to his location. For example: to look for the nearest gas station, or

restaurant. In this scenario, a user can observe the ambient radio beacons and estimate

his own location by querying the localization system on his mobile device. After the

location is known, he can send the information to the online search engine.

Local Advertisement: When a store is registered to the localized advertisement

service, the system would send an advertisement SMS or an electronic coupon to the

consumer once he is approaching the location. Pre-condition is that the consumer must

agree to receive advertisements in advance or it will raise another spam problems on

mobile networks.

Route Tracking: People who want to climb an unfamiliar mountain will have the need

to record his route. This is very important and helpful once the climbers really miss

their route back. The route database can be stored on mobile device or on a central

server, which can be provided to rescue team if something bad does happen.

Child/Elder People Care: This kind of service has been commercialized by many

security service companies, in conjunction with telecom operators. A user can submit a

cellular phone to the service provider and define the activity territory in advance. Once

the cellular phone is out of the range or is far from a regular place, the system would

notify the user for the abnormality. Another similar application is to install a bio sensor

to the person who needs real-time cares, for ex: the Asthma patients, together with

localization system. Once something happens with the patient, the emergency personnel

can find the location and adopt adequate measures.

Social Networking: Similar to the concept with internet community, an individual can

submit himself and register to a community in advance. If a person travels to a place

and he would like to know whether any of his friends is nearby to do for a lunch

together, he can check the system and find out whom.

4.2 Pervasive Mobile Games

-- Online ma-chan

-- Near me system

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-- Children security (if the cell id can’t be recognized…. Send an alarm)

-- moving…….. game…. Encourage people to move across gsm cells.. so they can get

higher scores……

Location based games offer interesting possibilities as generators of mobile voice and

data revenues. Location based games are a specialist sector of the rapidly growing

mobile games market. Pioneers in this area are the Swedish company It’s Alive. It’s

Alive is a games development company focused on what it calls “pervasive games”, or

massively multiplayer online games (MMOG) with the real world as the games arena.

It’s Alive has two games that it has developed and markets itself and also develops

games for third parties. The two games currently on the market under the It’s Alive

brand, BotFighters and Supafly, are good examples of the genre and illustrate how this

type of game works.

BotFighters is a location based mobile game where participants design a robot from the

company’s website and then engage in battles with other players out in the real world.

It’s Alive likens BotFighters to a “game of virtual paintball”. It is played via SMS using

any standard GSM phone, or via a downloadable J2ME client. Players using the J2ME

client have a richer user interface than those using SMS. Having built their robot,

players locate themselves using their mobile phones, move physically to get within

fighting range and fight by using an array of weapons. Mobile positioning is used to

determine whether the player is close enough to get a good hit. Hits gain points, and

points gain status and playing money. Actions (e.g. “FIRE”) are invoked by short code

messaging. Recharging batteries and rearming are also actioned by text message.

Revenue to the service provider is generated primarily through premium rate SMS, with

are charged at approx €0.20 + VAT per outgoing SMS (there is no charge for incoming

SMS). Currently there is no additional charge for gaming actions such as recharging a

robot’s batteries, but potentially differential charges could be made for different

actions, either thorough different rate SMS codes, or through adding content charge to

the messaging charge.

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This is a gross over-simplification of BotFighters, as there are many more nuances to

the game, but the principle remains true. Some elements of the game can be carried out

by logging on to the BotFighters web site e.g. buying weapons and upgrading robots.

BotFighters has been launched in Sweden (Telia), Finland (DNAL), Eire (Vodafone),

and Russia (MegaFon). To play the game participants have to register, so are opting-in.

In Russia, the game reached 1.5% of MegaFon subscribers within the first two weeks of

launch in November 2002.

During the first quarter of 2003 BotFighters was reported to be generating over 1

million SMS per week.

eis the other location-based game developed and marketed by It’s Alive. This is a

“virtual soap” game where participants create and amend characters for themselves and

interact with other characters in a virtual soap opera. The objective of the game is to

stay on top of the social tree – achieving celebrity status. Currently the game is played

using SMS and MMS messaging, but a WAP and J2ME version is shortly to be

launched. The mechanics of playing the game are similar to BotFighters, although the

context is different.

All It’s Alive games are based on a proprietary platform for location-based games, the

Matrix game platform. Games are licenced to service providers, who generate their own

business models within the constraints of the game.

Another mobile applications producer that has a location based games product is

Portuguese developer, YDreams. Their location based game is called Undercover and

was released to “Vodafone live” subscribers in Portugal in July 2003 and to customers

of mobile operator Sunday in Hong Kong in November 2003. A number of other

operators have been trialling the game.

Undercover is a massively multi-player adventure game where the player’s location is

key. Players, who are part of a ficticious international coalition of intelligence services,

use mobile phones to fulfill missions in a hostile environment under threat from

terrorists. The game is multiplatform (SMS/MMS/Java) and features: time and location

dependent missions with various levels of difficulty; a rich graphical interface and 3D

maps of terrorist locations; cooperation with other “friendly” agents; scanning for

terrorist agents, weapons, documents; attacking with different weapons; use of various

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items of defensive equipment; safe areas (which could be sponsored); customisable

characters.

At 3GSM in Cannes in February 2005, YDreams announced the release of Undercover

2: Merc Wars, for Q2 2005, as a sequel to Undercover. This is the first wireless game to

incorporate realworld maps for 44 countries (supplied by NAVTEQ). This enables

players to travel to travel all over the world looking for real streets and landmarks. The

application also incorporates optional live location where players with GPS enabled

phone, or customers of carriers with operative location platforms can engage in live

location gameplay.

Some in the Games industry see location based, or “pervasive” games as key to the

games of the future. Others are more sceptical, seeing them as niche products. Some

games industry veterans are rather dismissive of the sophistication of mobile games

generally, comparing many with levels of sophistication achieved in the consol games

sector many years ago. Whilst that may be true, it is missing the point that mobile

games are a completely new sector of the market, which provides entertainment on a

multi-purpose, personal communications device, available wherever you are. Real

games enthusiasts may be prepared to carry special devices optimised for gaming, but

the vast majority of “mobile gamers” will not.

An interesting factor in location based games is that they bridge the gap between the

enthusiast and mass mobile games markets. Whilst the level of sophistication of the

game itself may not be at the leading edge by some people’s definitions, the added

dimension of location creates a market of its own. There is a difference between those

that prefer to sit in their front room and play chess and those that would rather go out

on a treasure hunt. Location based games appeal to the latter group.

Whilst at the moment the appeal of location based games is probably very much in the

enthusiast camp, this will change as innovation in location based games design

progresses and user acceptance increases. Levels of sophistication that can be achieved

with location based games will undoubtedly increase as MMS is introduced into the

concept and Java versions are released. Whether greater positioning accuracy will boost

the location based games market is more debateable. It may enable more sophistication

in games design, but the market is already showing strong growth on networks using

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Cell-ID, with games based on SMS. There is still a lot of “novelty value” to use up.

More usability will be more important than precise positioning in the immediate term.

Greater sophistication in charging is also possible than seen at present, with potentially

enhanced revenue streams for service providers e.g. charging for more individual

elements of a game, or adding additional chargeable content. However additional

charges will be detrimental to market development and the location based games market

is still in the “early adopter” stage. The name of the game at this stage is to promote

increased usage and familiarity with the concept. As with many mobile products aimed

primarily at the youth market, the “viral” spread of users is likely to be a key vehicle of

growth. Photo messaging is not too attractive unless the group of people you would

want to message have the means to participate in the communication. The

encouragement of friends to join in was a significant driver in the early development of

photo/multimedia messaging. Peer group pressure will be the just as significant in the

development of location based gaming.

Unlike some other community applications, lack of network interoperability is not so

much of a constraint as gaming communities are not necessarily “hand picked”. Games

enthusiasts play with any others available in the location, restriction to own network

subscribers just limits the numbers available.

The interactivity of location based games is an attractive source of revenue for

operators and service providers. There are also opportunities for sponsorship and

advertising in the games arena created for players. In addition, location based games

promote the use of location data generally and help increase the return on operator

investment in location and positioning platforms.

Community & entertainment applications are forecast to offer the second largest

revenue opportunity after tracking applications. Key to the size of the opportunity is

that they can be deployed on both 2G and 2.5/3G networks, their low demand on

sophisticated content and the viral nature of their growth. The constraint is the

desireability for network interoperability for many applications. However network

interoperability issues will diminish over the forecast period.

Global available revenues are forecast to grow from $292m in 2005 to over $2.5bn in

2010 as network interoperability increases.

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Chapter 5 Related WorksThe methods of positioning had been a research topic for a long period. There can be

classified into mobile station based methods [2,6-11] and network based methods.[13].

Researches on network based methods will need telecom operator’s support and will

involve in many network equipments, whereas mobile station based methods are

relatively easier for individual to implement a localization system.

For indoor localization, RADAR[5] and Cricket[20] are the pioneers in the domain, but

the system work in limited environment and they need lots of assisted sensors for the

positioning.

For outdoor localization, GPS is good with 10 meters error distance, but poor in indoor

and urban canyon. Chen et al.[2] implements metropolitan-scale GSM positioning

system on both cellular phone and laptop via GSM modem. They do experiments based

on five factors and provide some useful test results for references. In addition to their

experiments, they also open their POLS source codes for other researchers. We benefit

lots with our work from their kind sharing. Cheng et al.[6] provides the evaluation of

Wi-Fi based positioning system in wild area. Their experiment shows a good accuracy

between 13 and 40 meters median positioning error with very low calibration effort.

In pervasive game domain, the BLAST invented “Can you see me now?” [17], which

combine cyber space with physical world, and is relied on the positioning of users for a

seamless interaction. Hitcher [18] and Tycoon [19] are designed specifically to

accommodate the characteristics of GSM signal. They do not pursue the accuracy of

GSM positioning, but take advantage of the imperfect of GSM positioning to create a

special kind of interaction.

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Chapter 6 Conclusion and Future WorkThis work targets the problems of applicability of GSM and Wi-Fi location tracking

system. We have conducted several combinations of experiments to show the accuracy

of a localization system may have with different algorithms. Fingerprinting methods

can be applied to both indoor and outdoor environment, but the training and calibration

efforts and costs should be considerably high. This is an issue while people are going to

deploy such a system. In outdoor environment, since the training process takes less time

and there are few calibration efforts, the Weighted Centroid algorithm is quite a proper

candidate.

In additional to the technical problems, the evolution of cellular phone may also play an

important role in future positioning system. In our experiments, we did not mention lots

of motion model, but just did straight comparisons in the experiments. However, to

achieve a better accuracy, a good motion model is strongly needed to filter noises

during signal collection and unexpected turbulences. If motion model is introduced, the

cellular phone will have to dedicate more computing power for the mathematic

calculations. As “Moore’s Law” hinted, we believe the computing issue on a mobile

device will be solved soon.

Pervasive mobile game is also a very interesting domain that can take advantage of

localization methods mentioned in this work. The game designers will be able to invent

an interactive mobile game based on a user’s physical location. With the new factor of

mobile game, gamers should have a brand new experience while playing interaction

with their own location.

For future work, we would like to study a hybrid localization system to be applied to

both indoor and outdoor environments. Automatic calibration should be a critical factor

while Fingerprinting method is used in a dynamic environment. Furthermore, if GPS is

available in future mobile devices, we can switch different types of radio RSSI on

detection of ambient environments. And discover the possible applications and

interaction rules that can take advantage of the ubiquity of GSM and Wi-Fi radio

sources.

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[2] Mike Y. Chen, Mike Y. Chen1, Timothy Sohn, Dmitri Chmelev3, Dirk Haehnel1,

Jeffrey Hightower, Jeff Hughes, Anthony LaMarca1, Fred Potter3, Ian Smith1,

Alex Varshavsky. “Practical Metropolitan-scale Positioning for GSM Phones”,

ACM Ubicomp 2006

[3] A. LaMarca, et al, "Place Lab: Device Positioning Using Radio Beacons in the

Wild," in Proceedings of the Third International Conference on Pervasive

Computing, May 2005.

[4] http://pols.sourceforge.net/

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[6] Yu-Chung Cheng, Yatin Chawathe, Anthony LaMarca and John Krumm,

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[11]Veljo Otsason1, Alex Varshavsky2, Anthony LaMarca3, and Eyal de Lara2,

“Accurate GSM Indoor Localization”, ACM Ubicomp 2005

[12]GSM Association, “Location Based Services”, veriosn:3.1.0, January, 2003 (PRD

SE.23)

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[13]Caffery, J.J.   Stuber, G.L.  “Overview of radiolocation in CDMA cellular

systems”, IEEE Communication Magazine, Apr. 1998

[14]L Barkhuus, A Dey. “Location-based services for mobile telephony: a study of

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[15]B Rao, L Minakakis. “Evolution of mobile location-based services”,

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[16]BLAST Theory “Can you see me now?” http://www.blasttheory.co.uk/bt/

work_cysmn.html

[17]http://www.dopodasia.com/dopod/taiwan

[18]Gregor Broll, Steve Benford, Leif Oppermann, “Exploiting Seams in Mobile Phone

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[19]Adam Drozd, Steve Benford, Nick Tandavanitj, Michael Wright, and Alan

Chamberlain, “Hitchers: Designing for Cellular Positioning”, ACM Ubicomp 2006

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[21]http://www.mactiontech.com/

[22]AsiaMedia http://www.asiamedia.ucla.edu/article.asp?parentid=48362

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