Real Time Self Mapping Hybrid Positioning System
Hamid MEHMOOD and Nitin K. TRIPATHI
The GI_Forum Program Committee accepted this paper as reviewed full paper.
Abstract
Currently, there are numerous location technologies that can be used to calculate the
position of mobile device in a space or grid. These location technologies are based on some
mathematical model, where positioning means allowing a mobile device to be aware of its
location with different degree of precision and accuracy. Unfortunately current positioning
systems are not ubiquitous. The local positioning systems fail to work outdoors where as
the conventional GPS based positioning systems don’t work inside buildings due to the
absence of line of sight to satellites, while cellular positioning methods generally fail to
provide a satisfactory degree of accuracy. So there is a need for positioning systems that
can work both indoors and outdoors. In this paper, a concept is presented to develop a
ubiquitous hybrid positioning system (uHyPos), which uses positioning data from AssistedGPS and Wi-Fi positioning system (WPS) to provide ubiquitous positioning.
1
Introduction
The market for location-based services is expected to top more than $13 billion in the next
five years, education, business, health and social networks like Facebook and MySpace
could be major drivers of adoption (ABI-RESEARCH, 2008). Hence generating a global
need for ubiquitous positioning system which can act as a underlying framework for these
location-based services.(MENG & DODSON et al., 2007).
Availability
remote
rural
urban/ city
Bluetooth
(cell id, ID,
IA, GA)
WLAN
Cell ID
G
S
UM M (
TS Cel
(C l ID
el , T
lI A
D) )
WLAN
(RSSI)
G
SM
UM ( E
TS - OT
( O D, U
TD - T
O DO
A) A
)
sub urban
AGPS
DGPS
indoor
0
Fig. 1:
10
100
Accuracy (m)
1000
Positioning Systems accuracy vs. availability modified from (CARON &
CHAMBERLAND-TREMBLAY et al., 2007)
Real Time Self Mapping Hybrid Positioning System
131
Studies have shown no single system can provide ubiquitous positioning; rather an
integration of these systems is required to develop a ubiquitous positioning system (UPS)
(Fig. 1).
AGPS (Assisted Global Positioning System) and WLAN (RSSI) are the best candidates for
this integration, as their integration would result in highest coverage, accuracy and cost
effectiveness as compared to integration of any other systems (Fig. 1). AGPS and Wi-Fi are
the technologies which are now available in all the latest, PDA’s, smart phones, and high or
medium end user mobile phones.
Positioning systems have been developed by integrating WPS (Wi-Fi Positioning Systems)
and GPS. SINGH, GUAINAZZO et al. (2004) developed a system for positioning using GPS
and WPS, but the coverage is limited only to outdoor environment as GPS has limited
indoor coverage and their algorithm relies heavily on GPS. CHEONG et al (2008) also
developed a real time positioning system device based on the study done by SINGH,
GUAINAZZO et al. (2004). In the system a fully referenced map is used as an input for WPS,
which can be a major hindrance when implementing such a system on a large scale as the
generation of such reference map is a costly procedure.
2
Background
2.1 Wi-Fi Positioning System (WPS)
WPS has drawn great attention in recent years for indoor positioning, because of its distinct
advantages like already existing communication infrastructure and wide coverage. With
numerous new Wi-Fi nodes being added daily, the global Wi-Fi coverage is continously
growing. Recent tests have shown that indoor positioning with WPS can achieve
positioning accuracy of 1 to 4 meter indoors and 10 to 40 meter in the outdoor environment
(MOK & RETSCHER et al., Oct 2006).
Different positioning techniques are used to achieve various levels of accuracy. The
positioning techniques can be divided into three categories:

Proximity Sensing

Geometric Triangulation (including lateration and angulation methods)
 Statistical Analysis / Fingerprinting
Statistical analysis or fingerprinting is one of the easiest techniques to implement because
of no additional hardware requirements and already developed mathematical models which
can give accuracy up to sub-meter level. Geometric triangulation technique requires
additional hardware like directional antennas for angulation method, where as proximity
sensing is least accurate and easiest to implement technology also with no additional
hardware requirements.
132
H. Mehmood and N. K. Tripathi
2.2 Assisted GPS (AGPS)
Conventional GPS is inefficient for indoor use or in urban areas where high buildings
shield the satellite signals (HIGHTOWER & BORRIELLO, 2001; XIANG, SONG et al., 2004;
WUTJANUN MUTTITANON, TRIPATHI et al., 2007). Assisted GPS can address these issues by
using an assistance server, which can locate the phone roughly by which cell site it is
connected to on the cellular network. The assisted server already has the satellite signal
information for each cell, which it can compute and pass on to the mobile device. As
assistance server has computation power so all the calculations can be performed on
assistance server. A typical A-GPS enabled cell phone will use a data connection (internet,
or other) to contact the assistance server. The basic architecture for Assisted GPS is given
in Figure 2.
Fig. 2:
Assisted-GPS architecture (DJUKNIC & RICHTON, Febr. 2001)
2.3 Hybrid Positioning System (HPS)
Users expect a seamless and transparent location experience regardless of application or
environment. Since no single positioning technology can provide this, the future will be
about HPS, combining A-GPS, Cell-ID, Wi-Fi, cellular, motion sensors, and even TV
broadcast and proximity technologies such as Bluetooth, NFC and RFID. A-GPS, Wi-Fi
and Cell-ID will be the winning combination offering accuracy, availability,
interoperability and short fix times at low cost. HPS are expected to represent 25% of all
positioning solutions by 2014. (ABI-RESEARCH, 2009).
Usually GPS is one major component of HPS (figure 3), combined with for instance cell
tower signals, wireless internet signals, Bluetooth sensors etc. These systems are
specifically designed to overcome the limitations of the GPS system, which is very exact in
any open area, but works poorly indoors, between tall buildings or even in cloudy weather
(HIGHTOWER & BORRIELLO, 2001; XIANG, SONG et al., 2004; WUTJANUN MUTTITANON,
TRIPATHI et al., 2007)
Real Time Self Mapping Hybrid Positioning System
Fig. 3:
3
133
Possible combinations for HPS
Ubiquitous Hybrid Positioning System (uHyPos)
An overview of the technologies, mathematical formulae, techniques and algorithm that
will be used in the proposed uHyPos is given below. uHyPos is to be developed for
deployment on a smart device (mobile, PDA, etc). It will be able to map the access points
(AP) in the surrounding. And on each visit to a specific location the algorithm will be able
to improve the accuracy by improving the previously calculated position. The algorithm is
divided into following modules i.e.
1.
2.
3.
4.
5.
Scanning
Distance Quantification
Direction Quantification
Reference Map Generation
Positioning
3.1 Scanning
The scanning module scans for the signal strength of all the access points (AP) in the
neighbouring environment called as radio scan. The radio scan is saved with the time stamp
when the scan is performed, Mac address and the signal strength for each AP. The radio
traces consist of all the radio scans performed. Seed data is defined as the Mac address,
latitude and longitude of an AP, and Mac address and the signal strength of the neigh-
134
H. Mehmood and N. K. Tripathi
bouring AP. Also if some seed data is available it is also loaded, two APs are considered as
neighbours if they are visible during the same radio scan.
seed data ={ (id1, lat1, lon1,mac11,ss11, mac12,ss12, ………, mac1n,ss1n ),( id2, lat2, lon2,mac21,ss21,
mac22,ss22, ………, mac2n,ss2n), ……… , (idn, latn, lonn,macn1,ssn1, macn2,ssn2, ………, macnn,ssnn)}
radio scan = {ts, {(id1,ss1),( id2,ss2),( id3,ss3)……………………..( ido,sso)}}
radio traces = { radioScan1, radioScan2 ,radioScan3 ,…………radioScanm }
id= Mac address of the AP
ts= time stamp
ss= signal strength
3.2 Distance Quantification using Seidel Formula
Distance quantification will consist of quantifying distance between two APs on the basis
of signal strength. Sediel Model is used for distance quantification because it takes into
consideration the environment around the AP (SEIDEL, 1992).
3.3 Direction and Direction Quantification using
Vincenty Formula
Seidel Model is to calculate distance between two APs when GPS fix is not available for
both APs, if the GPS fix is available for both the APs then Vincenty formula (VINCENTY,
1975) is used. Direction quantification is done by calculating bearing between two known
points using Vincenty Formula.
3.4 Reference Map Generation
The flow chart for reference map generation is given below with a graphical
implementation for each step. In the graphical representation 50% of the total nodes are
considered to be unknown nodes.
COMPUTE DISTANCE BETWEEEN
AN3 & p1 AND DISTANCE
BETWEEN AN3 & p2
SEARCH FOR SEED DATA SET
FOR ANCHOR NODE , AN3,
WITH UN1 AS A NEIGHBOUR
STOP
SELECT THE POSITION OF UN1
WITH THE LEAST DISTANCE
FROM AN3
COMPUTE DISTANCE BETWEEN
UN1, AN1 AND UN1, AN2 USING
SEIDEL FORMULA
CALCULATE TWO POSSIBLE
POSITIONIS p1, p2 FOR UN1
USING VINCENTY FORMAULA
CHECK IF ALL THE NODES HAVE
BEEN ASSIGNED LOCATION
SELECT UNKNOW NODE,
UN1, WHICH IS A
NEIGHBOUR OF AN1 AND
AN2
SELECT TWO ANCHOR NODES
WHICH ARE ALSO
NEIGHBOURS, AN1, AN2
YES
Fig. 4: The General flowchart for uHyPos
SCANNING
QUANTIFY DISTANCE BETWEEN
ALL ANCHOR NODES USING
VINCENTY FORMULA
Real Time Self Mapping Hybrid Positioning System
135
H. Mehmood and N. K. Tripathi
136
UN
UN
AN
AN
UN
AN
AN
UN
Access Point
AN = Anchor Node
UN = Unkown Node
Fig. 5:
Randomly distributed Anchor Nodes (AN) and Unknown Nodes (UN)
UN
UN
AN
AN
UN
AN
UN
AN
Access Point
AN = Anchor Node
UN = Unkown Node
Fig. 6:
Computation of distance between anchor nodes using Vincenty formulae
UN
UN
AN1
AN
d1
UN1
Dmax
AN
d2
UN
Access Point
AN2
Fig. 7:
AN = Anchor Node
UN = Unkown Node
Selection of unknown node (UN1) and Anchor Nodes AN1, AN2
Real Time Self Mapping Hybrid Positioning System
UN
UN
AN1
AN
d1
p1
UN1
Dmax
137
AN
p2
d2
UN
Access Point
AN2
Fig. 8:
AN = Anchor Node
UN = Unkown Node
Position calculation p1,p2 for unknown node using Seidel Model
UN
UN
AN1
AN3
d1
p1
UN1
Dmax
AN
p2
d2
UN
Access Point
AN2
AN = Anchor Node
UN = Unkown Node
Fig. 9:
Calculating two possible position calculation p1,p2 using Vincentry Formulae
UN
UN
AN1
AN3
d1
UN1
Dmax
AN
p2
d2
UN
AN2
Fig. 10:
Access Point
AN = Anchor Node
UN = Unkown Node
Selection of position of UN1 with the least distance from AN3
H. Mehmood and N. K. Tripathi
138
UN
UN
AN1
AN3
AN4
Dmax
AN
UN
Access Point
AN2
Fig. 11:
AN = Anchor Node
UN = Unkown Node
Concvert unknown node UN1 to know node AN4
UN
UN
AN1
AN3
GPS FIX
AN
Dmax
AN4
UN
Access Point
AN2
AN = Anchor Node
UN = Unkown Node
Fig. 12:
4
Position calculation in case of GPS fix
Conclusion and Future Work
Integration of WPS and GPS based technologies holds great potential to fulfil the needs of
a ubiquitous positioning system. uHyPos addresses issues like, availability of relative and
absolute positioning, requirement for additional hardware, and seamless coverage, which
are vital elements for development of a ubiquitous HPS. For, future work the accuracy and
coverage of the system are to be tested in terms of node distribution, ratio of unknown
nodes, and positioning environment. Furthermore technologies like accelerometer are
planned to be used for indoor positioning with WPS to increase the accuracy.
Real Time Self Mapping Hybrid Positioning System
139
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