Ad Hoc Networks For Cooperative Mobile Positioning
Ad Hoc Networks For Cooperative Mobile Positioning
Ad Hoc Networks For Cooperative Mobile Positioning
1. Introduction
Wireless ad-hoc networks have received huge attention during recent years due to the
potential applications in different fields such as emergency, disaster relief, battle-fields,
automotive, social networks and entertainment. They are rapidly deployable, selforganizing, and require no fixed infrastructure for communications. (Huang et al., 2008)
At the same time, localization in wireless networks is becoming a hot topic for society,
industry and research. The needs of location information has driven companies to build
mobile handsets with embedded GPS receivers (which is nowadays the most popular mass
market solution for positioning), causing huge increase in costs, size, battery consumption,
and a long time for a full market penetration (Sayed et al., 2005). However, it is also known
that the GPS is not always the most suitable solution for localization. In adverse
environments, such as outdoor urban canyons and indoor, it is not an easy task to obtain
location information, due to the signal blocking, multipath conditions and the infeasibility to
have a continuous tracking of at least four satellites (Mayorga et al., 2007).
The Fourth generation (4G) communication systems also stimulate the need of providing
alternative ubiquitous localization solutions, regardless the environment (i.e., outdoors and
indoors), which should overcome, or at least complement, the drawbacks of GPS-based and
GPS-free systems (Della Rosa, 2007). Traditional alternative technologies make use of time
difference of arrival (TDOA) measurements from the serving cellular system where the Base
Stations (BSs) are considered as fixed reference points (Sayed et al., 2005).
Different type of measurements, such as received signal strength (RSS), are widely used in
local area scenarios, where Wi-Fi Hot Spots deployed in big cities allow user terminals to
predict their locations by means of known fixed positions (Sayed et al., 2005).
Unfortunately, when localization is performed in indoor environments the accuracy is
highly dependent on the wireless channel conditions since several error sources cause huge
signal fluctuations detected at terminal level, severely decreasing the final location
estimation accuracy (Della Rosa et al., 2010).
Recently, in alternative to traditional methods, a new branch of positioning techniques has
been developed: the Cooperative Mobile Positioning (Figueiras & Frattasi, 2010), which makes
use of hybrid schemes and exploits the benefits in terms of accuracy of short-range
measurements provided by the ad-hoc networks (Della Rosa, 2007).
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In this chapter we will explain the basics of Cooperative Mobile Positioning and
demonstrate the applicability of the technique in real cases, demonstrating that the
exploitation of the most reliable RSS measurements detected in the ad-hoc links represent a
valid and complementary approach to traditional non-cooperative methods, and that the
hybrid network model adopted is the most natural environment in which cooperation among
terminals is established and best exploited without additional hardware components
(Figueiras & Frattasi, 2010) (Della Rosa et al., 2010).
2. Mobile positioning
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needed. The principle of fingerprinting based positioning is illustrated in Fig.5. For each
location, from the off-line collected data a typical signal pattern is extracted and saved to the
fingerprint database with the coordinates of the location (Fig.5.a). In positioning phase, the
current set of RSS measurements from the APs in the coverage area are compared to the
patterns stored in database. The coordinate estimate is obtained from the database entry
whose stored signal pattern has the closest match with the measured signal vector (Fig.5.b).
Compared to other RSS based methods, fingerprinting algorithms are considered to be more
robust against signal propagation errors such as multipath or attenuations generated by
walls and other structures; fingerprinting actually make use of these location dependent
error characteristics of radio signals. In estimation phase, new measurement vectors are
related with the information stored in fingerprint database. A known disadvantage in
fingerprinting approaches is the fact that the collection of the data for fingerprint database is
laborious and time consuming (Wallbaum et al., 2005; Bahl et al., 2000).
Fig. 5. Fingerprinting
2.4.3 Pathloss-Based positioning
Pathloss models of radio signals are used to translate RSS measurements to distances
between the MS and APs. After the distances are estimated from RSS measurements,
trilateration methods are used to estimate the position of the MS (Fig.6). To obtain a unique
solution, the MS needs from measure RSS to at least three distinct APs. As in cell ID based
methods, the MS needs prior information about the MAC addresses and locations of APs,
which is easily acquired, at least when compared with fingerprint databases. In indoor
environments, multipath and attenuation caused by walls, other structures, and even people
complicate the modeling of signal propagation. Because of this, the positioning errors in
pathloss-based positioning are typically larger than in fingerprinting (Bahl et al., 2000). On
the other hand, methods that utilize path-loss models to estimate distances are needed for
example if signal properties of ad-hoc WLAN connections between two MSs need to be used
for positioning, because dynamic information about moving AP locations is difficult if not
impossible, to be incorporated in fingerprint databases. Because of the low system set up
cost of pathloss-based positioning, and its better suitability for incorporating measurements
from ad-hoc connections, we concentrate on pathloss-based positioning in this research.
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(a) Conventional
(b) Cooperative
Fig. 8. Block Scheme for conventional and cooperative positioning (Della Rosa et al., June
2007) (Della Rosa et al., 2010).
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(a) Outdoor
Fig. 10. Protocol for measurements and data exchange.
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(b) Indoor
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4. Results
This section analyzes the results, where computer simulations and experiments have been
performed by developing proof of concepts for different scenarios: (i) a hybrid cellular/ adhoc framework implemented in Matlab (Della Rosa, 2007) (Mayorga et al., 2007) and (ii)a
small-scale experiment using real devices in a WLAN/ ad-hoc network (Della Rosa et al.,
June 2007) (Della Rosa et al., 2010).
While the cellular/ ad-hoc scenario is a simulated hybrid MobileWiMAX/ WLAN system,
the WLAN/ Ad-hoc framework proves the feasibility of the cooperative techniques for
heterogeneous MSs with different embedded wireless cards. In the latter it is also shown
that the cooperation can be used to avoid long time-consuming calibration phases of
different mobiles when performing RSS-to-distance conversions for AP-MS and MS-MS
links(Della Rosa et al., 2010).
4.1 Outdoor: Cellular/Ad-hoc:
The system architecture of the simulator is shown in Fig. 11. While the cellular system is
simulated according to the IEEE 802.16e standard (Mayorga et al., 2007), the ad-hoc links
between MSs are modeled according to the IEEE 802.11a PHY (Mayorga et al., 2007). The
scenario reproduces four synchronized BSs, with maximum synchronization error of 1ms
among them. The cell radius is r = 3 km, and two MSs placed at distance of 20m from each
others. MSs are assumed to be connected to the serving BS, (e.g. BS1). A mobility model
simulates users moving with constant velocity of 3 km/ h along parallel straight lines.
Typically (Della Rosa, 2007) 20 meters are enough for establishing ad-hoc connections;
specially when the devices are in LOS, as in our simulated environment.
The full chain of blocks (cellular environment, mobility models, positioning estimators) is
depicted in Fig. 11 where the physical layer (PHY) of the IEEE 802.16e standard is
Orthogonal Frequency Division Multiplexing (OFDM) modulation. While in free-space the
traveling time of the radio signal is only dependent upon the distance BS-MS, in real
situations it is strongly delayed by channel impairments, having a direct impact on the
TDOA values estimated at the receiver. For this reasons a channel model has been simulated
according to (Della Rosa, 2007) (Mayorga et al., 2007).
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Also the IEEE 802.11a PHY is based on OFDM modulation (for more details the reader can
refer to (Della Rosa, 2007) and (Mayorga et al., 2007)). But, differently from the AP-MS links,
the MS-MS links measure RSS values, meaning that the implementation of a path loss model
with small scale fading effects for a LOS scenario is also required.
Finally an EKF is used as data-fusion algorithm and positioning filter according to
(Figueiras & Frattasi, 2010) (Della Rosa, 2007).
TDOA measurements are generated according to the 802.16e standard and combined with
the RSS measurements within the ad-hoc network in the cooperative case. In noncooperative case only TDOA measurements are considered.
Fig. 12 describes the simulated and estimated path of the users moving in parallel where the
estimated positions for MS1 and MS2, respectively, with and without cooperation are
shown. The average Root-Mean-Squared-Error (RMSE) is evaluated through the estimated
path and the resulting Cumulative Distribution Function (CDF) of the RMSE describes the
improvements by using only two cooperative MSs in the simulated environment (Fig. 13). It
is worth mentioning that the proposed example requires the handsets to be equipped both
with WiMAX and Wi-Fi modules. The resulting performances achieved show that
cooperation reduces the average RMSE with respect to conventional stand-alone positioning
methods (Figueiras & Frattasi, 2010) (Della Rosa, 2007).
(a) Estimated Path (b) Example of RMSE improvements for one MS.
Fig. 12. Estimated Path and RMSE with and without cooperation.
Fig. 13. CDF of RMSE With and Without Cooperation for two MSs.
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Fig. 14. RSS of laptops placed at same distance from AP, with different embedded wireless
cards.
Theoretical path-loss models provided in literature are not accurate enough to reach high
localization accuracy performances and exhaustive device calibrations are needed to find
precise models for each mobile in use. Even after calibration, the obtained model is usually
useful only for the calibrated one (Della Rosa et al., 2010).
What if we would like to develop robust and more scalable positioning applications? Every
mobile (every wireless card) should be accurately re-calibrated. The cooperative technique
helps in the aforementioned problem by exploiting ad-hoc connections and spatial
constrains allowing the on-the-fly calibration of peer heterogeneous mobiles with different
embedded wireless cards. We can imagine the situation described in Fig. 15.
One MS, (MS1) is calibrated according to the accurate procedure depicted in Fig. 15(a) ( and
discussed in (Della Rosa et al., June 2007) (Della Rosa et al., 2010)) and another MS, the non-
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calibrated (MS2) enters the coverage area of the ad-hoc network. MS1 and MS2 are placed at
distances d1 and d2, respectively, from AP1 as shown in Fig. 15(b), and recording the RSS
from AP1. MS2 sends the recorded RSSs to MS1 via ad-hoc connection. MS1, after having
measured also the RSS of the ad-hoc connection with MS2, estimates the distance between
the MSs; it is assumed that the MS2 transmits also info about its transmission power. MS1
estimates the distance d1 from AP1 and the distance d3 from MS2. The distance d2 should
not exceed the radius of d3 estimated by MS1. At this point MS1 calculates a correction
parameter for MS2, to allow MS2 to apply the path-loss model of MS1. After receiving the
correction parameter, MS2 can finally estimate the distance from AP1.
(a) Conventional
(b) Cooperative
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were logged into text files and processed with Matlab scripts in both calibration and
positioning phase. A Cooperative-NLLS algorithm was performed according to (Figueiras &
Frattasi, 2010) (Della Rosa et al., 2010) (Frattasi, 2007) and results were compared with the
non cooperative approach (Mayorga et al., 2007).
Fig. 17 shows the averages of the estimated positions for the three MSs with cooperation
(circles with border) and without cooperation (circles without border). Laptops icons
represent the real positions of the mobiles. It is demonstrated as in such adverse
environments, the ad-hoc network has a beneficial impact in positioning accuracy for all the
devices. Moreover, as the number of cooperative users increases, also the positioning
accuracy gets improved (Figueiras & Frattasi, 2010).
5. Conclusion
In this chapter we have described the basics of Cooperative Mobile Positioning and the
exploitation of ad-hoc networks in adverse positioning environments. Our test results from
simulations and real life experiments show that, thanks to the short-range measurements
available from ad-hoc links, the positioning accuracy is improved when compared to the
accuracy of the non-cooperative approach. The ad-hoc link measurements present lower
absolute errors than measurements in long-range cellular links; they are more stable and
contain less signal fluctuations.
Although we have provided examples on Mobile WiMAX and WiFi technology, the
cooperative technique can be adapted and exploited by replacing one or both technologies
with different and newer ones.
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6. References
Bahl, P. & Padmanabhan, V.N. (2000). Radar: An in-building RF-based user location and
tracking system, IEEE INFOCOM 2000 Conference on Computer Communications, vol.
2, pp. 775-785, Tel Aviv , March 2000, IEEE.
Breed, G. Wireless Ad Hoc Networks:Basic Concepts. Summit Technical Media, LLC..
Della Rosa, F. (2007). Cooperative Mobile Positioning and Tracking in Hybrid Mobile
WiMAX/ WLAN. M.Sc. Thesis, Aalborg University (AAU), Denmark, June, 2007.
Della Rosa, F., Paakki, T., Leppkoski, H., Nurmi, J., (2010). A Cooperative Framework for
Path Loss Calibration and Indoor Mobile Positioning, Proceedings of 7thWorkshop
on Positioning, Navigation and Communication 2010 (WPNC10),Dresden, Germany,
March 2010.
Della Rosa, F. Wardana, S.A., Flores Mayorga, C.L., Simone, G., Raynal, M.C.N., Figueiras,
J., Frattasi, S. (2007)Experimental Activity on Cooperative Mobile Positioning in
Indoor Environments. Proceedings of 2nd IEEE Workshop on Advanced Experimental
Activities on Wireless Networks and Systems (EXPONWIRELESS), ,Helsinki, Finland,
June, 2007.
Figueiras, J., Frattasi, S., (2010). Mobile Positioning and Tracking: From Conventional to
Cooperative Techniques. (1st Edition),Wiley, ISBN 978-0470694510.
Frattasi S.(2007). Link layer techniques enabling cooperation in fourth generation (4g)
wireless networks, Ph.D. Thesis, Aalborg University AAU, Denmark,(September,
2007).
Frattasi S., Monti M. (2007). Ad-Coop Positioning System (ACPS): positioning for
cooperative users in hybrid cellular ad-hoc networks. EUROPEAN
TRANSACTIONS ON TELECOMMUNICATIONS. Wiley InterScience. 2007.
Huang E., Hu W., Crowcroft J., Wassell I. (20xx). Towards Commercial Mobile Ad Hoc
Network Applications: A Radio Dispatch System, xUrbana-Champaign, Illinois,
USA.
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ISBN 978-953-307-416-0
Hard cover, 514 pages
Publisher InTech
How to reference
In order to correctly reference this scholarly work, feel free to copy and paste the following:
Francescantonio Della Rosa, Helena Leppkoski, Ata-ul Ghalib, Leyla Ghazanfari, Oscar Garcia, Simone
Frattasi and Jari Nurmi (2011). Ad Hoc Networks for Cooperative Mobile Positioning, Mobile Ad-Hoc Networks:
Applications, Prof. Xin Wang (Ed.), ISBN: 978-953-307-416-0, InTech, Available from:
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