Lecture on Mobile P2P Computing
Prof. Maria Papadopouli
University of Crete
ICS-FORTH
http://www.ics.forth.gr/mobile
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Agenda
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Introduction on Mobile Computing & Wireless Networks
Wireless Networks - Physical Layer
IEEE 802.11 MAC
Wireless Network Measurements & Modeling
Location Sensing
Performance of VoIP over wireless networks
Mobile Peer-to-Peer computing
Exciting research problems
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General Objectives
• Build some background on wireless networks,
IEEE802.11, positioning, mobile computing
• Explore some research projects
and possibly research collaborations
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Environmental Monitoring
Source: Joao Da Silva’s talk at Enisa, July 20th, 2008
Tagged products
Source: Joao Da Silva’s talk at Enisa, July 20th, 2008
Source: Joao Da Silva’s talk at Enisa, July 20th, 2008
Source: Joao Da Silva’s talk at Enisa, July 20th, 20
New networking paradigms for efficient search
and sharing mechanisms
Source: Joao Da Silva’s talk at Enisa, July 20th, 20
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Fast Growth of Wireless Use
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Social networking (e.g., micro-blogging)
Multimedia downloads (e.g., Hulu, YouTube)
Gaming (Xbox Live)
2D video conferencing
File sharing & collaboration
Cloud storage
Next generation applications
• Immersive video conferencing
• 3D Telemedicine
• Virtual & Augmented reality
• Assistive Technology
 Rapid increase in the multimedia mobile Internet traffic
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Fast Growth of Wireless Use (2/2)
• Video driving rapid growth in mobile Internet traffic
• Expected to rise 66x by 2013 (Cisco Visual
Networking Index-Mobile Data traffic Forecast)
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Energy constrains
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Paradigms of Mobile Information Access
 Wireless Internet via APs
 Data Access via Infostations
 Data Access using the Peer-to-Peer paradigm

Hybrid mobile information access
(manifesting a combination of the above paradigms)
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Wireless Internet via APs
Aims at “continuous” wireless Internet access
broadly defined by three types networks:
 Wireless wide area networks (WANs)
 Wireless local area networks (LANs)
 Wireless personal area networks (PANs)
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Infostations
• Wireless-enabled server attached to data repository
• Wireless devices in range can query the infostation to acquire data
• Can be
– stand-alone servers
– clustered with other infostations connected over terrerstrial links
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Peer-to-Peer systems
 Distributed system without any
 Centralized control
 Infrastructure
 Distinguished by the following criteria
 Self-organization
 Autonomy
 Symmetry
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Mobile Peer-to-Peer Computing
• When two devices (peers) are in wireless range of
each other, they may share resources:
– Share data
– Network connection
– Relay packets on behalf of each other
• Enable resource sharing among peers in a selforganizing, energy-efficient manner
Internet
Server-to-Client Paradigm
Client gets data from AP
AP
Server-to-Client:
Trapping model from
particle-kinetics
Router
Switch
Peer-to-Peer Paradigm
User C
User A
User B
Wireless Network via an Infrastructure
How does information diffuse in mobile peer-to-peer systems ?
Applications Using Mobile P2P
• Location-based applications
• Social networking application
 For example: Facebook integrated with positioning,
google maps, 7DS, photojournal
• User-centric access of the spectrum
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Photojournal
• Sharing multimedia files with your friends
• Mobile P2P paradigm
• Superimpose multimedia information on google
maps by correlating the timestamps of multimedia
files and recorded positioning information
• Review, share, search multimedia files across a
(single-hop) network of friends
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http://www.ics.forth.gr/mobile/
http://www.ics.forth.gr/mobile/
Research Issues on Cognitive Radios
INFORTE Lecture Series
Prof. Maria Papadopouli
University of Crete
ICS-FORTH
http://www.ics.forth.gr/mobile
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Underutilization of licensed spectrum
• Licensed portions of the spectrum are underutilized.
– According to FCC, only 5% of the spectrum from 30 MHz to 30
GHz is used in the US.
Cognitive radios
• Intelligent devices that can coexist with licensed users without
affecting their quality of service
– Licensed users have higher priority and are called primary users
– Cognitive radios access the spectrum in an opportunistic way and are
called secondary users
• Networks of cognitive radios could function at licensed portions
of the spectrum
– Demand to access the ISM bands could be reduced
Coexistence of secondary users
• Usually, in cognitive radio networks, a large number of
secondary users compete to access the spectrum
• A protocol should define the behavior of all these users such
that the network’s performance is maximized
• Performance metrics:
– Spectrum utilization
– Fairness
– Interference to primary users
Performance optimization
• Proposed protocols in
optimization problem
the
literature
define
an
– The utility function depends on the performance metrics
• Parameters of the problem are chosen from the following
set:
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Channel allocation
Adaptive modulation
Interference cancellation
Power control
Beamforming
Definition of the problem
1. Channel allocation
• Problem formulation:
– 2 secondary users compete for access in the band [F1 F2].
– The interference plus noise power as observed by the first user
is:
• Question: Which is the best way for this user to distribute
its transmission power at the interval [F1 F2]?
Channel capacity
• According to Shannon the maximum rate that can be
achieved in a channel is:
S

R( S )  B log2 1  
 N
• S: signal power
• N: interference plus noise power
• B: width of the channel
dR(S )
B
1 1
B
1


dS
ln 2 1  S N ln 2 S  N
N
• As the power that is introduced to a channel increases,
the achievable rate increases more and more slowly.
Energy investment in two channels
B 1
B 1


ln 2 N1 ln 2 N 2
B
1
B 1


ln 2 N1  P1 ln 2 N 2
dR1 dR2

ds
ds
dR1 dR2

ds
ds
• We start by investing energy in the first channel until it’s
total power becomes equal to N2.
• After that point, energy is divided equally among the two
channels.
Water filling strategy
• The best way for a
user to invest it’s
power is to distribute
it in the whole range
of frequencies.
Channel allocation problem
• M users compete to access a band
– They do not use the selfish water filling strategy
– Instead they cooperate and divide the spectrum among them in
the most efficient way
• The initial band is divided into a number of non
overlapping frequency bins
– An algorithm maps the bins to users in such a way that a global
utility function is maximized
Cooperation
Is it possible for the two users to achieve a better rate if they
cooperate?
Example:
R1  2 B log(1 
P
)
P2  2 N
R1  B log(1 
'
P
)
N
 When R1’> R1 then dividing the bandwidth among the two users
is more effective than water filling.
Channel allocation algorithm
• There are various ways that a channel allocation
algorithm could be designed.
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Distributed or centralized.
Proactive or on demand.
Predetermined channel allocation.
Allocation of contiguous or non contiguous bins to devices.
Primary and secondary channels
• Channels that are allocated to a user are called primary
• Channels that a user borrows from the neighborhood are
called secondary
• Predetermined channel allocation is not so suitable for
cognitive radio networks, duo to:
– Changes of channel conditions caused by primary user activity
– Network topology changes very often
User-centric Spectrum Sharing
• Spectrum is a valuable resource!
 Improve its spectrum utilization
• Primary users “sub-lease” part of spectrum
• Secondary users take advantage of the unused
spectrum
• Different algorithms for bin allocation across
secondary and primary users
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