Mobile Peer-to-Peer computing

Transcript Mobile Peer-to-Peer computing

Lecture on

Mobile P2P Computing

Prof. Maria Papadopouli University of Crete ICS-FORTH http://www.ics.forth.gr/mobile 1

Agenda

• • • • • • • • 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 2

General Objectives

• • Build some background on wireless networks, IEEE802.11, positioning, mobile computing Explore some research projects and possibly research collaborations 3

Environmental Monitoring

Source: Joao Da Silva’s talk at Enisa, July 20 th , 2008

Tagged products

Source: Joao Da Silva’s talk at Enisa, July 20th, 2008

New networking paradigms for efficient search and sharing mechanisms

Source: Joao Da Silva’s talk at Enisa, July 20th, 2008

Fast Growth of Wireless Use

• • • • • • 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 

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) 11

Energy constrains

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) 13

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) 14

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 15

Peer-to-Peer systems

 Distributed system

without

  Centralized control Infrastructure any  Distinguished by the following criteria   

Self-organization Autonomy Symmetry

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 self organizing, energy-efficient manner

Server-to-Client: Trapping model from particle-kinetics

Internet

Server-to-Client Paradigm Client gets data from AP

AP Router Switch

Peer-to-Peer Paradigm User C User A

Wireless Network via an Infrastructure

User B 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 19

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 20

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 23

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 optimization problem – in the literature define The utility function depends on the performance metrics an • Parameters of the problem are chosen from the following set: – – 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 [F 1 F 2 ].

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 [F 1 F 2 ]?

Channel capacity

• According to Shannon the maximum rate that can be achieved in a channel is: • • • •

(

) 

log 2  1 S: signal power N: interference plus noise power B: width of the channel

S N dR

(

)

 ln

2 1  1

 ln

1 

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

1 ln 2

1 

ln 2 1

2 

ln 2

1 1 

1 

ln 2 1

2 

• • 

ds dR



We start by investing energy in the first channel until it’s total power becomes equal to N 2 .

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:

1  2

log( 1 

2 

)

1 ' 

log( 1 

P N

)  When R 1 ’ > R 1 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.

– 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 37