VTC2005 fall 1

Understanding the effect of environmental factors on link quality

for on-board communications

Irene Chan, Albert Chung, Mahbub Hassan

University of New South Wales Kun-chan Lan, Lavy Libman

National ICT Australia (NICTA)

VTC2005 fall 2

About NICTA

• A national research institute funded by Australia Government

• Our research staff includes– regular researchers – contributed staff

• from major universities such as Australian National Univ., Univ. of Sydney, Univ. of Melbourn, New South Wales of Univ.

• Our focus– Research, commercialization, education,

collaboration

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About NICTA

• 5 research labs – located in Sydney, Canberra and Queensland

• 14 Research programs– Empirical Software Engineering; – Interfaces, Machines, And Graphic Environments– Networks and Pervasive Computing.– Embedded, Real-Time, and Operating Systems– Formal Methods – Symbolic Machine Learning and Knowledge Acquisition– Statistical Machine Learning; – Systems Engineering and Complex Systems  – Wireless Signal Processing– Logic and Computation; – Autonomous Systems and Sensing Technologies – Statistical Machine Learning.– Sensor Networks; – Network Information Processing.

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Agenda

• Motivation– Search of the environmental factors that can

be utilized for outage prediction for on-board communication

• Measurement results

• Conclusion and future work

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Mobile Network

• Growing interest in providing broadband service for public transport passengers

• Mobile Network– On-board LAN– Mobile Router (MR)

• Standardized protocol (RFC3963): NEMO– Extension of MIPv6

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Exciting on-board applications

Data Server

Mobile Router

On-Board LAN

entertainment

email

VoIP

surveillance

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Products/services already appearing!

Cisco mobile router

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On the research side

• IETF NEMO working group [2002]• Nautilus6 working group [Japan, 2003]• Network Mobility project [Korea, 2003]• WirelessCabin [Europe, 2002]• Network On Wheels (NOW) [Europe,

2005]• PATH project [Berkeley]• Diesel project [Umass, Amherst]

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Outages

outage

outage

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Outage prediction

• Important problem for on-board communication– Can impact a large number of users

• Mobility pattern of public transport vehicles are known in advance– Utilize this feature for outage prediction– Mobile Router records signal strength and available

bandwidth information at different times and at different locations

– Predict outage by analyzing recorded information

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Effect of environmental factors

• Various factors might affect quality of wireless signal

• While physical factors like noise, multi-path will obviously affect the signal strength,– In practice, not easy to utilize them for outage

prediction

• Environmental factors such as location, weather, time of the day, crowdedness, vehicle velocity, etc. – easier to observe/obtain and use

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Contribution of this work

• We conducted wide-area measurements by recording GPRS signal in different locations and under a variety of conditions in Sydney metropolitan area

• We found location is the most dominating environmental factor affecting signal quality

• Some cellular operators might have done this – but their measurements are not public available

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Agenda

• Motivation

• Measurement results– preliminary results on location, speed,

humidity, people and tunnel

• Conclusion and future work

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Data collection

• We recorded signal strength of Vodafone’s GPRS network under different conditions in a 6-month period

• Measurements were taken on the link between the receiver and base station

• GPS is used to record location and time of measurements

• Traces in collected on train, bus, car and some chosen locations

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location• Signal strength is strongly correlated with

locations across different times of the day

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Signal strength map

• 3 bus routes• Good quality: green• Bad quality: red• Similar patterns

across different times and different days

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Speed

• No significance in signal strength level

• Larger variations at a lower speed– Hypothesis: other

environmental factors have a better chance to affect the signal at the lower speed

• More handoffs at a lower speed

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Handoff sequences

• Sequence of switching between different base stations exhibit certain predictability

• Suggest the feasibility of deploying some resource reservation scheme for on-board network

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Humidity• Rainy: 2 millimeters per

hour• No significant difference• Rain attenuation has a

stronger in higher frequency band such as microwave link– GPRS uses 900MHz/1.8G– Some base stations uses

microwave links to connect to Mobile Service switching Center (MSC)

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People

• Crowded: – lunch time

• Non-crowded– Eastern break

• No significant differences

• Larger variation for crowded scenario

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Tunnel

• Tunnel does not lead to continuous outages

• Micro-cell around the platform

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Distribution of signal strength level

• Gaussian distributed

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Throughput during the day

• Similar patterns across different days– Lower throughput during the business hours

and in the evening

• Hypothesis: user behavior

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Conclusion and future work

• We conducted wide-area GPRS measurements under a variety of conditions in Sydney metropolitan area

• We found location is a better predictor for outage prediction than other environmental factors

• Use measurement results to design practical outage prediction algorithm for on-board communication