Optimizing network resources in opportunistic networks

Se Gi Hong, Sunghoon Seo, and Henning SchulzrinneIRT Lab, Columbia University

Internet

• 0.26 seconds Google search results for iPad with 85,400,000 results

• Cellular, WiFi, DSL, FiOS Network connection

• 1.5% Median failure rate of accessing 80 websites from Alexa list (V. N. Padmanabhan et. al, “A study of end-to-end web access failures”, ACM CoNEXT, 2006)

• Current Internet– Fast– Available– Stable and reliable

What if?

• In a subway tunnel

I want to read WSJ, but no connection.

I want to read NY times, but no connection.

I want to send email to my boss, but no

connection.

• Subway station has an access point

What if?

We have a connection. We only have 20 seconds to

download the webpage. Hurry up.

Oops! I missed the chance. I will send my

email next stop

Introduction

• Opportunistic networks– A communication network designed to withstand intermittent connection

• Challenged network– No continuous path

• Intermittent, scheduled

– Unstable path• Path break and change quickly and frequently

– No end-to-end reliability• Sometimes no return path

• Applications– Intermittently connected networks

• Vehicular networks

– Disruption tolerant networks• Store-carry-forward routing

Challenges

• There are two phases in opportunistic networks– Neighbor and service discovery– Message delivery

• Neighbor and service discovery– Real-time discovery– Energy efficiency for service discovery

• Message delivery– Maximize delivery ratio and minimize delivery delay

• Increase number of copies and select appropriate multiple nodes (carriers)• Many solutions have been proposed

– Epidemic routing, spray-and-wait routing, encounter-based routing, geographical routing, etc

– Requires to maximally utilize limited resources• Limited link capacity, limited storage capacity, short contact time• Still need to research this issue

Challenges

• There are two phases in opportunistic networks– Neighbor and service discovery– Message delivery

• Neighbor and service discovery– Real-time discovery– Energy efficiency for service discovery

• Message delivery– Maximize delivery ratio and minimize delivery delay

• Increase number of copies and select appropriate multiple nodes (carriers)• Many solutions have been proposed

– Epidemic routing, spray-and-wait routing, encounter-based routing, geographical routing, etc

– Requires to maximally utilize limited resources• Limited link capacity, limited storage capacity, short contact time• Still need to research this issue

Done in 7DS project

• Public transportation (bus-stop) model• Deterministic knowledge (temporal and spatial information)

– Location of next bus stations (stops)– Expected next opportunity: (calculated by average speed of the bus)

Model

Manhattan49th St, 6th Ave.

Bus station

Public transportation model

• Participants– Vehicle

• Gathers traffic information• Carries messages from other vehicles to

upload them at stop

– Station • Infrastructure (AP), content delivery

service• Media streaming, traffic information

– Passengers• Email, web-based service (web-

searching)

• Messages– Uploading

• Messages to the infrastructure (at station)

– Downloading • Message from the infrastructure (at

station)

Internet

Motivation

• The increase of information to be transmitted during opportunity (contact)– Content distribution service at stations

• Media streaming

– Proliferation of usage of mobile devices• Web-searching, email-delivering, downloading applications, etc

• What if is throughput not good enough to transmit all messages during opportunity?

– Need to measure actual throughput, contact time, raw data rate.

• Can we maximally utilize the limited resources?– Limited storage, limited bandwidth, limited contact time

Measurement #1

• Measurement of bus dwell time (stop time) and travel time in Manhattan– 2:30 PM – 3:30 PM, Jan, 2010 – 116st, Broadway – 42st, 1 Ave

• Results– Average bus dwell time is 26 sec; average bus travel time is 65.4 sec

Measurement #2

• Measurement of goodput via IEEE 802.11g while on a bus– One-way (upload)– Two-way (upload and download)

• Measurement settings– 2 – 4 PM, Feb, 2010– 106th ST, Broadway – Use two laptops

• Bus stop– ThinkPad 11a/b/g/n Wireless LAN Mini PCI Express Adapter– Atheros AR5418/AR5008 chipset

• Bus– Intel PRO/Wireless 3945ABG Mini-PCI Express Adapter– Intel WM3945AG chipset

TCP goodput via IEEE 802.11g

• TCP-upload only• Total network connection time: 25 sec• Bus dwell time: 11 sec

• TCP-two-way (upload and download)• Total network connection time: 46 sec• Bus dwell time: 26.7 sec

Total throughput is smaller than that of TCP-upload because of network contention

Network contention

• There are several users– Passengers, bus, bus stop

• Problem– As number of users increases,

network contention increases• Numerical analysis results of

bandwidth estimation – Scenario: users upload messages to

an AP

(a) Number of users = 1 (b) Number of users = 10

CR: Channel Rate of an AP and usersRbusy: channel busy ratio

S. Seo et. al, “Achievable throughput-based MAC layer handoff In IEEE 802.11 wireless local area networks”, EURASIP Journal onWireless Communications and Networking, 2009.

Almost 10 times

Measurement #3

• Measurement of raw data rate (channel rate) and signal strength– Uploading messages

• Measurement settings– 2 – 4 PM, Feb, 2010– 106th ST, Broadway – Use two laptops

• Same wireless network card to make symmetric link– Orinoco 11a/b/g ComboCard PCMCIA wireless card– Atheros chipset

Raw data rate and signal strength

• Average signal strength: -65.6 dBm• Raw data rate is low

bit/sec

1 Mbps 2 Mbps 1 Mbps 2 MbpsRaw data rate (Mb/s)

Retry

Signal strength (dBm)

Findings of measurements

• Network connection time– Average bus dwell time: 26 seconds– Average network connection time: 42 seconds

• Network contention– As number of users increases, network contention increases

• Raw data rate (channel rate) is low– 1, 2, or sometimes 5.5 Mb/s

• There are unexpected behavior of buses– Sometimes, a bus stops a bit far from a bus station.– Sometimes, a bus does not stop at a bus station.

• Antenna and wireless network card affect throughput– Goodput at measurement #2 is 8 Mb/s while a bus is stopping– Goodput at measurement #3 is 1 Mb/s while a bus is stopping

Solution space

• To overcome the limitation of resources in a public transportation model, we need to:

– Maximize the availability of network resource utilization– Minimize the usage of network resources during opportunity – Schedule messages

Solutions for network resource limitation problem

Maximize the availability of network resource utilization

Minimize the usage of network resources during opportunity

Reduce network contention

Reduce repetition of transmission

of same contents

Load balancing in the temporal domain

Message scheduling

Centralized Distributed

Centralized Distributed

• vehicle

Client-side proxy-based system

• cluster head among users

• Caching • P2P exchange

• During running time: cache hit and P2P exchange• During stopping time: Internet connection (access)

• User behavior (boarding duration)• Deadline for content transmission is the disembarking time of users• incomplete-first, popular content-first, round-robin, random selection

Optimizing network resource utilization

Load balancing in a temporal domain

No network connection to bus stop

Network connection to bus stop

No network connection to bus stop

Proxy

Server

User A

time

User B

Data transmission

Signaling for suppression

Signaling for allowance

Users

System architecture

Database

Message Scheduler Proxy

Incoming Outgoing

P2P exchange Proxy service

Packet flow Configuration

Cached files

Cache component Proxy component

Query handler

Response handler

forwarding handler

Traffic management

• We will install compact, low-power, low-cost, advanced communication computers at bus/train stations and buses/trains

• We will test our system and evaluate performance

Long-term future work

Soekris net5501-70 500 Mhz CPU, 512 Mbyte DDR-SDRAM, 4 Ethernet, 2 Serial, USB connector, CF socket, 44 pins IDE connector, SATA connector, 1 Mini-PCI socket, 3.3V PCI connector.

bus/train stationbus/train

Conclusion

• Message delivery– Problems

• Congestion, burst• Limited contact time• Low raw data rate

– We are developing a system that:• Maximizes the availability of network resource utilization• Minimizes the usage of network resource during opportunity (contact)• Schedules messages

backup

Destination/delivery mode

Multicast AnycastUnicast

Interest-driven

Location-drivenPerson Location-

driven

Any node that meets conditionse.g., any AP or infostation to upload Messages•7DS message delivery

•Geographic routing•GeOpps

•Community-based routing•Interest-aware communication

•Geographic routing•GeOpps•GeoDTN+Nav•Oracle-based

•EBR•MaxProp•Prophet•Spray and wait•BUBBLE•SimBet

Depth and breadth

Two-hops / Source routing

More than two hops /Per-hop routing

Single copy Multiple copies

One-hop

•Direct deliverybetween a sender and a receiver Single link Multiple

links Flooding

•Epidemic routing,•MaxProp

•Shortest path•Oracle-based

•Several possible paths•Oracle-based

•GeOpps•GeoDTN+Nav•Prophet•SimBet

•Spray and wait•EBR•BUBBLE

Knowledge for message delivery

Zero knowledge

Deterministic information

Temporal information

Spatial information

Route/destinatio

n-invariant

Mobility pattern

•randomized routing•Epidemic routing•Spray and wait•7DS message delivery

•Bus, train•Oracle-based

Probabilistic information

Popularity/centrality

Time-varying, dynamics are

known

Time-invariant

Route-varying,

Destination- invariant

•Satellite•Oracle-based

•Satellite•GeOpps•GeoDTN+Nav•Oracle-based

Personal relationship

•Route/destination location varying•Prophet•MobySpace

•EBR•BUBBLE•SimBet

•Navigation system•GeoDTN+Nav

•MaxProp•Prophet