inferring digital and physical environment knowledge from mobile … · link this to maria...
TRANSCRIPT
Dr. Weisi Guo
Inferring Digital and Physical Environment
Knowledge from Mobile Phone Signals
Brief Bio
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I am an Assistant Professor in Engineering, at the University of
Warwick, and associated to CUSP.
• Academic Background 1. University of Cambridge: MEng (2005), MA, PhD (2011)
2. Published over 40 papers in wireless communications
3. Research interests: wireless sensing, urban communication networks.
• Industrial Background 1. 2 years as radio-engineer at T-Mobile International (2005-07)
2. 2 years undertaking joint academic-industrial research with
Vodafone, NEC and Fujitsu (2011-12)
3. Author of industrial copyright wireless network simulator
VCEsim
Idea
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To allow each user to passively monitor its local environment, and
link this to Maria Liakata’ s research on human emotions.
• Passive 1. Extract information using existing ambient radio signals, i.e., without
purpose built sensor hardware or additional electronic signals
2. Extract information in continuously, without human triggers
• Environment 1. Digital Environment in terms of data activity of yourself, and the people
around you.
2. Physical Environment in terms of terrain type and vibrancy of physical
objects (i.e., people, cars) around you.
Motivation
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Why is passive monitoring of the environment of benefit?
• Digital Activity Level Cross operator understanding of total data activity level has significant
commercial and social implications. 1. Long term trends can reveal where to improve the wireless network
2. Short term trends can advice users on where to seek greatest data service
• Physical Environment Dynamic environment changes that is not available from quasi-static
databases such as Google maps. 1. Understanding how the city environment changes over time
2. Understanding how other human and city factors are associated with physical
environment dynamism
Dr. Weisi Guo
In collaboration with S. Wang of South Australia University
Part 1: Digital Environment
Background Knowledge
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In wireless communications, data is transmitted on certain
frequency bands.
• Multiple Access System Finite frequency bands (spectrum) forces us to reuse the
spectrum across a large area, which means: 1. Multiple transmissions co-exist on same frequency
2. These co-frequency transmissions interfere with each other
• Interference Noise Tolerating the level of interference noise at the receiver underpins
design performance. Interference has properties: 1. Non-coherent and Aggregated: the noise is aggregated from one or
multiple sources, with no ability to discern which set of transmitters.
2. Measurement: Improved hardware allows us now to measure this very
cheaply ($200), compared to $10,000+ many years ago.
Note: incoherent inference channels are not to be confused with coherent pilot channels used to identify cells
Example Cellular Network
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Research Challenge: 1. How do we know which set of transmitters are transmitting data?
2. How do we know how much data is transmitted?
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Benefits over Current Methods
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There are a number of current methods used to extract data usage:
Physical
Extraction per
Device
Postcode and
Census Data
Sensing
Interference
(very novel!)
Accuracy Very High Low
(no outdoor)
High
Cost Medium Low
(offline)
Medium
(very complex)
Resolution High Medium
(residential)
Medium
(no mobile ID)
Privacy / Commercial
Sensitivity
Extremely
High
Low Low
How is it done?
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We have had our initial concept accepted for publication later this year [1]. Whilst it is
plausible to map every interference power pattern to a geographical location,
computationally it is too complex. Per spatial grid, need 105 x 2N unique patterns, where N
is the number of interference sources.
Mathematical model of network interference distribution
Using Stochastic Geometry [2], which requires knowledge of:
1. Spatial distribution of network nodes
2. Statistical radio-wave propagation model
[1] “Mobile Crowd-Sensing Wireless Activity using Measured Interference Power”, W. Guo & S. Wang, IEEE Wireless
Communications Letters, to appear, Sep 2013
[2] “Stochastic Geometry for Wireless Communications”, M. Haenggi, Cambridge University Press, Aug 2012
Expected Interference Power
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We will flash some key equations, but the paper explains their origins in
detail.
1. Let Ir denote a particular value of the aggregate interference power random variable IR:
2. Given knowledge of the way a network is distributed (random uniform in this case), the
probability distribution of IR can be found. The expectation of the interference power
received at any point in space is given by:
Relation to Traffic Intensity
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We define the traffic intensity concept and how it relates to the data
volume.
1. We define the traffic intensity (A) as a normalized value between 0 and 1, where A =
Traffic Load / Maximum Capacity of Cell. For a multi-cell network, this corresponds directly
to the ratio between the number of transmitting cells λ, and the total number of deployed
cells χ, at some particular frequency band f0.
2. Given we know the mean interference power as a function of the number of transmitting
cells, we can find the sensed traffic intensity:
Validation Results
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Below are some validation results
Traffic sensing error as a function of sample size
We need ~50 interference power samples per frequency band. The advantage
of this technique is that the spatial sample resolution only needs to be within a
cell location area (no GPS data is needed). We are working on an alternative
version that improves spatial resolution of output and input.
Single Network Test Results
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We simulated an example urban network
This figure shows a cellular network’s coverage area. Some accuracy is lost on
the borders of the map. This is a temporal-spatial heat map of wireless activity
level for a single wireless network.
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Single Network Test Results
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Single network results are only for validation purposes. It is unlikely to reveal data that
is more information than what the network operator already knows. Nonetheless, here
are some maps for a single operator.
Across Network Test Results
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We now expand the method to cover 5 commercial operators (Vodafone, EE, O2, 3, BT),
and across their different services Wi-Fi + 3G + 4G.
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Discovering New Hotspots
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We now expand the method to cover 5 commercial operators (Vodafone, EE, O2, 3, BT),
and across their different services Wi-Fi + 3G + 4G.
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1 Regent Park “People in parks are wirelessly very active,
but who is providing this service and what is
their usage pattern?”
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New Business & Commerce
Centre “People in shopping centre is very active,
but who is providing their wireless
network?”
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Westminster Uni. and BT “Not as active as imagined, but this area
has proportionally more customers on
competitor networks.”
Urban Science Application
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Clearly, what has been demonstrated is from an engineering and industrial
perspective. There is however strong urban science application:
• Understanding the Digital Economy 1. Understand the economic relationship between wireless information flow and local
economic growth.
2. Quantify the benefit of wireless infrastructure investment
• Understanding the Human Emotion 1. Our thoughts and emotions are increasingly connected with digital information
availability. What is the relationship between digital activity and our emotions?
2. We communicate our thoughts and observations wirelessly through the internet,
can this mobile traffic volume uncover patterns in the city?
Dr. Weisi Guo
Part 2: Physical Environment
Some Basic Knowledge
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In wireless communications, data is transmitter on certain frequency
bands, and the signal propagates through a complex environment.
• Multipath Different time of arrival due to multiple terrain paths in cities: 1. Causes constructive and destructive combining at receiver
2. Causes phase / frequency shifts
• Multipath Models Different mathematical models describe the nature of the signal’s propagation: 1. Long range Manhattan model: Rayleigh Distribution
2. Short range model: Rician Distribution
Experimental Setup
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Recently in an Mobicom paper [3], researchers said that they can accurately
detect body movement using Wi-Fi signals, rather than cameras.
• Novelty If the signal transmitter and receiver is positioned for
this single purpose, then this is not new (Radar)
• Our Experiment
We aim to use ambient signals that already exist in the world, and analyse their multipath
and other property (i.e., Doppler shift).
[3] “Whole-Home Gesture Recognition Using Wireless Signals”, Q. Pu and S. Gupta and S. Gollakota and S. Patel, Mobile
Computing and Networking Conference, to appear, 2013
Urban Science Application
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The question is, why would you want to monitor urban vibrancy and why can’t
it be observed from alternative methods (e.g. cameras):
• Why 1. Our emotions are affected by what happens around us. Continuous high resolution
data of the volume of vibrancy around us is of interest.
2. It also serves as a city wide data collection service to detect human movement on a
statistical level, without intruding into their privacy.
• Methodology 1. Passive: no additional hardware is required. It is possible to do this with
smartphones.
2. Private: no user identity is sensed in the process. The data is statistical.
Dr. Weisi Guo
Thank You for Listening and
Thank you Maria for Organising