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.
1.1 Positioning Technologies.........................................................................1
1.2 LBS (Location-Based Services)...............................................................3
1.3 Thesis Organization..................................................................................4
2.1 (Weighted) Centroid positioning...............................................................5
2.3 SmartPhones and GPS............................................................................7
2.4 Positioning software.................................................................................8
2.5 Radio beacons..........................................................................................9
2.6 Error distance...........................................................................................9
1.3 Urban Area.............................................................................................11
3.2 Rural Area...............................................................................................13
3.4 Practical Issues......................................................................................17
4.1 Mobile Location-Based Services............................................................20
4.2 Pervasive Mobile Games.......................................................................21
List of Figures
Fig. 1. People skiing outdoor (Hakuba, Japan)................................................................4
As 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
. 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
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.
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
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 . 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
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.
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.
In 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  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.
Fig. 2. Centroid Positioning (the coordinate of beacons is a prior
Fingerprinting  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, 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).
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 … …
Fig 3. Fingerprinitng (no need to know the
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 , 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.
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 , 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
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 , 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.
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.
GSM Wi-Fi GSM Wi-Fi
Module Module Module Module
Fig. 5 The Training system software
Fig. 6 The Testing system software
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
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
Experiments and Evaluations
Experiments 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.
1.3 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.
Fig 7. Cell tower distribution in Hsin-Yi District.
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
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
Avg. Error Max. Min. Beacon
(M) Error(M) Error(M) No.
GSM (Centroid) 264 639 11 148 37
164 519 18 100 37
Wi-Fi (Centroid) 67 290 2 56 497
85 292 2 49 497
. Fig 8. Centroid/ Weighted Centroid Localization results in Hsin-Yi Dist,
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
<<<< burt note: this is the preliminary results….. should change a device and do
again the test for verification >>>>.
Fig 9.Wi-Fi AP distribution in Hsin-Yi District.
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
Hsin-Yi District Fingerprinting Localization
Average(m) Max(m) Min(m) STD(m)
GSM (fingerprinting) 226.78 384.4 17.8 114.97
Wi-Fi (fingerprinting) 179.28 413.5 2.3 107.53
Fig 10. Fingerprinting Localization results in Hsin-Yi Dist.
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.
Fig 11. GSM cell tower distribution around NTU campus
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.
Avg. Error Max. Min.
STD(M) Beacon No.
(M) Error(M) Error(M)
GSM (Centroid) 316 730 15 168 27
198 611 14 132 27
Wi-Fi (Centroid) 48 391 4 54 859
37 114 3 23 859
Fig 12.Centroid localization results in NTU campus
For fingerprinting positioning method, we still get bad results as it does in Hsin-Yi Dist.
<<<< burt note: this is the preliminary results….. should change a device and do
again the test for verification >>>>.
Fig 13.Wi-Fi AP distribution around NTU campus
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 52 100
Wi-Fi (fingerprinting) 192 250 5 101
Fig 14. Fingerprinting Localization results in NTU campus
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.
Fig 15. GSM Cell tower distribution alongside Formosa highway
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
Average Error Max. Min. STD(M Beacon
(M) Error(M) Error(M) ) No.
GSM (Centroid) 744 3192 35 564
425 2576 16 371
Fig 16. Fingerprinting Localization results in 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 , 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 . 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
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
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
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(???).
GSM (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  with
GSM and 3 meters  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
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.
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
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
-- 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
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.
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
items of defensive equipment; safe areas (which could be sponsored); customisable
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
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
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.
The 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..
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 and Cricket are the pioneers in the domain, but
the system work in limited environment and they need lots of assisted sensors for the
For outdoor localization, GPS is good with 10 meters error distance, but poor in indoor
and urban canyon. Chen et al. 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. 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?” , which
combine cyber space with physical world, and is relied on the positioning of users for a
seamless interaction. Hitcher  and Tycoon  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.
Conclusion and Future Work
This 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
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
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 AsiaMedia http://www.asiamedia.ucla.edu/article.asp?parentid=48362