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Alaeddine EL-FAWAL. Thesis director : Jean-Yves Le Boudec. Data Dissemination for Spot Applications in Ad-Hoc Networks. Private Defense, 3 April 2009, EPFL. Overview on the PhD Work. Supported by 2 projects: Haggle: European project in Situated and Autonomic Communications

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Alaeddine EL-FAWAL

Thesis director :

Jean-Yves Le Boudec

Data Dissemination for Spot Applications in Ad-Hoc Networks

Private Defense, 3 April 2009, EPFL


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Overview on the PhD Work

  • Supported by 2 projects:

  • Haggle: European project in Situated and Autonomic Communications

  • NCCR MICS: National Center of Competence in Research – Mobile Information and Communication Systems

  • 2 parts in my PhD work:

  • Data dissemination for spot applications over WIFI technology (Haggle)

  • Cross-layer optimization for UWB systems (MICS)

Dealing with different facets of uncoordinatedad-hoc wireless

networks and deals with challenges at all networking layers


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OUTLINE

Introduction

SLEF: Our Data Dissemination Middleware

Performance Validation

Prototyping and Testbed

Conclusions

Achievements


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Open-Ended Environment

  • Characteristics:

  • Involves from few to thousands of nodes.

  • Challenging circumstances:

    • Highly dynamic

    • Unpredictable

    • Uncoordinated

    • Short contact time

    • Quickly changing from dense to sparse, non-congested to congested

Caused by:

Dramatic expansion of WIFI interfaces: laptops, PDAs, mobile phones, video games and even with peripherals and vehicles.


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Spot Applications

Description

  • Destination: all nodes within the spot (multi-hop).

  • The spot might be the entire network (campus).

  • Example: ad applications, traffic info, support routing, resource discovery, bootstrapping phases for application layer.


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Spot Applications

Requirements:

  • Efficient and reliable data dissemination service

  • Trade-off: spread-application rate

    • Spread: number of nodes within the application spot

      (number of nodes that receive a packet).

    • The spot size is variable according to the network conditions.

  • Delivery ratio can not be used… instead, we talk about spread.


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Open-Ended Envir. : Variation and Diversity of Scenarios

Relay

Transmission range

Source / Relay

  • Density: average

  • Few sources: little new injected traffic


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Open-Ended Envir. : Variation and Diversity of Scenarios

Relay

Transmission range

Source / Relay

  • Density: average

  • almost all are sources: a lot of new injected traffic


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Open-Ended Envir. : Variation and Diversity of Scenarios

Relay

Source / Relay

  • Very high density (traffic jam)

  • almost all are sources: a huge amount of new injected traffic

  • One hop: + 200 neighbors


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Open-Ended Envir. : Variation and Diversity of Scenarios

Relay

Source / Relay

  • Density: very sparse.

  • No communication without mobility (opportunistic communication)

An autonomic mechanism for data dissemination that adapts to

this diversity in scenarios is a must, otherwise

network failure


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OUTLINE

Introduction

SLEF: Our Data Dissemination Middleware

Performance Validation

Prototyping and Testbed

Conclusions

Achievements


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SLEF: Self Limiting Epidemic Forwarding

  • We propose the SLEF middleware.

    • Delivers an efficient and reliable data dissemination service to the spot applications

    • Deals with the tradeoff: spread-application rate

    • The network conditions define the TTL limit (spread control).

    • Functional between the application and the transmission sockets (UDP or raw sockets).

Data dissemination through limited epidemic forwarding :

  • A source transmits packets in broadcast mode.

  • Nodes forward each packet they receive Forwarding Factor times.

  • Packets are forwarded within a limited hop count.


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SLEF: Self Limiting Epidemic Forwarding

  • SLEF Features:

  • Autonomic: Adapts itself to any change in the network.

    • Density increases  forwarding factor decreases

    • Traffic load increases  TTL limit decreases

  • Complete design of a middleware

  • Does not need / exchange any topology information. Uses only local information to the node (very short contact time).

SLEF is designed to hold in all scenarios, in particular

in very dense and very sparse ones


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Essential Functions for Multi-Hop Broadcast

SLEF implements 6 essential functions needed for a sustainable service

1. Congestion control: first mechanism proposed for broadcast in ad hoc networks

  • Efficient use of MAC broadcast:

    • 802.11 broadcast does not implement any exclusion mechanism (RTS/CTS) and it performs poorly (similar to Aloha). We replace it by a new scheme that we call pseudo-broadcast.

    • 802.11 broadcast does not implement Ack. Pkts might be transmitted in the vacuum. We implement a presence indicator that does not need any message exchange.

Scheduler / fairness: A scheduler is needed to decide which packet to serve. It is based on Source ID to ensure some level of fairness.

4. Spread control (adaptive TTL)

5. Forwarding factor control

6. Buffer management


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Spread Control: Adaptive TTL

Trade-Off Spread vs. Application Rate

Spread: number of nodes that receive a packet.

How:

  • We use an Aging mechanism

  • Adaptive TTL: Aging adapts locally the TTL to the different network setting, based on the send/receive events.

  • The idea is as follows:

With fixed TTL

  • Density => Spread Rate

TTL limit= 2

With SLEF

  • Density =>TTL : In a traffic jam: TTL = 1

  • Density => TTL : In a very sparse network: TTL = 10

N : Spread

FF: Forwarding Factor

R0: Nominal Rate of MAC Layer

λ : Application Rate


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Aging

Age

adaptive Age

Real time Age

hop count

Send/receive the same pkt

Age = Age +K0

receive any pkt

Age = Age + K1

Constant increase

by time: 8h -> 255

Age > 255

Drop packet

  • New created pkt: Age = 0

  • Pkt received for the first time: Age = 255 – TTL

  • Age manipulated locally.

  • when transmitting: TTL = 255 -Age

  • Hop count: plays the role of a fixed TTL (=255/K0) if the network is not congested.

  • Adaptive Age: adapts the TTL to the network activity, which reflects the density and the traffic load

  • Real time Age: A pkt lives at most 8 hours (work cycle).


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Spread-Rate Balance

Adjusting the spread-rate balance according to the application needs

Default values for K0 and K1 are computed in the thesis

  • SLEF maintains a spread-rate balance

  • 2 parameters to adjust: K0, K1

  • Once Adjusted, they work well with all settings


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Forwarding Factor Control

One send/receive event

Two send/receive events:

The green nodes are inhibited:smaller forwarding factor

Transmission would be redundant

Why:To Minimize redundancy (save resources)

How:

  • Inhibit nodes from transmitting over sent/received pkts.

  • We compute a virtual rate for each packet based on the send/receive events.

  • Adaptive:thevirtual rateAdapts locally the Forwarding Factor to the different network settings


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Virtual Rate

Virtual Rate: The max rate a packet is transmitted with

For each send / receive event on a given pkt:

1- The virtual rate is computed as follows:

R0 : nominal MAC rate [pkts/s]

RcvCount : number of times the pkt is received

SendCount : number of times the pkt is sent

aandb : are coefficient less than 1: a=0.1, b=0.01

3- The pkt is allowed to be transmitted only after: current time +

(similar to a back-off system)

Unlike other mechanisms, the virtual rate based forwarding factor control allows transmitting the pkt multiple times if needed.

2- vRate decreases exponentially with send/receive events.

The smaller the vRate is, the longer the back-off time is:

The pkt might be dropped before being transmitted


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Buffer Management

Highest age

Lived the highest number of send/receive events

  • Cleans the buffer in order to keep space for new incoming packets.

  • Based on Aging.

  • Drops packets with the highest age


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OUTLINE

Introduction

SLEF: Our Data Dissemination Middleware

Performance Validation

Prototyping and Testbed

Conclusions

Achievements


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Setting

  • Scenarios:

    • Vehicular networks.

    • Different network settings: node density, traffic load…

Network Simulator: JIST-SWANS, A JAVA simulator for Ad Hoc networks

Vehicular Mobility Simulator: STRAW, an extension of JIST-SWANS. It provides a mobility model based on the operation of the real vehicular traffic.

Topo : 2-lanes road, speed limit 80Km/h

MAC : 802.11/b

Channel : Fading

K0 = 25

K1 = 0.1

Range : 300 m in average


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Adaptation of the Spread to the Rate

Spread

Rate

Density: 12 vehicles/Km

Adaptive TTL


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Adaptation of the Forwarding Factor to the Density

Very sparse

(Death Valley)

Very dense (Traffic jam)

+200 neighbors


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Importance of the Pseudo-Broadcast

Very dense (Traffic jam): +200 neighbors

Idea: implement a mutual exclusion mechanism for broadcast in order to avoid collision


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OUTLINE

Introduction

SLEF: Our Data Dissemination Middleware

Performance Validation

Prototyping and Testbed

Conclusions

Achievements


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SLEF Prototyping

2 architectures:

  • It is independent of the IP address

  • Practical in the absence of a centralized coordination where assigning IP @ is challenging

  • Complex with Windows

  • IP networking needs to be initialized

  • Straightforward with all platforms

Spot application

SLEF

UDP sockets

Spot application

UDP

SLEF

IP

Raw sockets

MAC

MAC


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SLEF Prototyping

4 platforms:

Resource-constrained devices:

  • Smartphone HTC S620:

    • Windows Mobile

    • 64MB RAM, 128 ROM

    • 201 MHz

  • ASUS WL-500 GP wireless router

    • OpenWrt

    • 32MB RAM, 8MB Flash

    • 266MHz

  • Windows XP: J2SE, C++

  • Windows Mobile: J2ME, C++

  • Linux: J2SE, C++

  • OpenWrt (Linux-like firmware for embedded system): C++

SLEF is practical and performs well on very resource-limited devices


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Testbed

Stress test:

Testbed features:

Performance evaluation of SLEF through measurements

  • Wireless router: ASUS WL-500GP

  • Technical specifications: 8MB Flash, 32MB RAM, 266MHz, 2 USB 2.0 ports

  • Firmware: OpenWrt

  • Configurable wireless interface: using Atheros card with MadWIFI driver (setting RTS/CTS Th, Tx power, promiscuous mode, monitor mode, Tx queue length…).

  • Mobility: using plumb batteries, +4 hrs lifetime with full power transmission at full rate

  • Robustness.

  • More than 50 devices communicating with each other for long time.


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Measurement Design

Nodes are distributed over 12 buildings in EPFL


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Measurement Design

  • Application: injects packets at fixed rate.

  • Application rate: can be reduced by the congestion control mechanism.

  • Comparison: SLEF vs. fixed-TTL

  • Fixed-TTL:

    • Implements all functions of SLEF, otherwise it is not functional.

    • Spread control is replaced by TTL (decremented by one for each hop)

    • TTL limit is fixed

    • Buffer management is based on the TTL and not on the age: packets with the smallest TTL are dropped first when the buffer is full

    • Fixed-TTL limits the spread through 2 parameters: TTL limit and the buffer size.

  • Buffer size of fixed-TTL:

    • Small buffer size: We ran SLEF and we use the average buffer occupation obtained (620 packets).

    • Large buffer size: 10 000 packets.


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Measurement Results

Higher redundancy

Lower rate

Smaller spread

  • With fixed TTL:

  • Setting the buffer size per scenario is needed:

    • Large buffer size  radundancy and low rate

    • Small buffer size  small spread even in non-congested network

  • TTL based buffer management does not perform well.


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Measurement Conclusions

Fixed-TTL

SLEF

  • 2 parameters to adjust: K0, K1

  • Once adjusted, they work well with all settings

  • Aging based buffer management performs well

  • Max Buffer size is 255/K1 (little formula)

  • 2 parameters to adjust: MaxTTL and buffer size

  • Needs to adjust whenever the network setting (density, traffic load, mobility,…) changes

  • TTL based buffer management performs poorly.


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OUTLINE

Introduction

SLEF: Our Data Dissemination Middleware

Performance Validation

Prototyping and Testbed

Conclusions

Achievements


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Conclusions

We propose SLEF: data dissemination middleware for spot applications

Prototyping SLEF for 4 platforms

Building a testbed for wireless networks protocols

SLEF performs better than fixed-TTL:

  • Fixed-TTL is not adaptive

  • TTL-based bufer management performs poorly

  • Autonomic: Adapts itself to any change in the network.

  • Complete middleware

  • Does not need/exchange any topology information. Uses only local information to the node.

  • Works even in extreme scenarios (very dense/sparse…)

  • Performs well on resource constrained devices


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Perspectives

Performance evaluation of SLEF:

  • Factorial analysis applied on measurement results.

  • Measurements will consider varying scenarios: mobility, intermittent connectivity, different traffic load and density.

  • Comparison with different variants of data dissemination protocols

Collaboration with LCA3: Prof. Patrick Thiran and Adel Aziz

  • Extensive series of measurements for wireless network protocols


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OUTLINE

Introduction

SLEF: Our Data Dissemination Middleware

Performance Validation

Prototyping and Testbed

Conclusions

Achievements


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Achievements

Cross-Layer Optimization for UWB IR Networks (MICS)

  • Robust Signal Acquisition in UWB Ad Hoc Networks:

    • One journal paper, one conference paper and one patent (adopted by MICS).

  • Sleeping Mode for UWB IR Ad-hoc Networks:

    • One journal paper.

Data Dissemination for Spot Applications (Haggle)

  • Multi-Hop Broadcast Middleware (SLEF):

    • One conference paper.

  • Vulnerabilities in Epidemic Forwarding:

    • One conference paper.

  • SLEF Prototyping and Experimental Testbed.


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Robust Signal Acquisition in UWB Ad Hoc Networks:

Problem: Conventional detection method

Near-far scenario

S1

D1

  • assume power control, otherwise it fails.

  • Power control impractical in the absence of a centralized coordination (CDMA).

D2

S2

Solution: Power independent detection method.

Performance Evaluation:

10 users, LOS Indoor Office Channel model by IEEE P802.15.4a

Total Error: Et=PMD + PFA

Proba of Misdetection: PMD

PMD


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Issues Addressed for the First Time

  • Spot Applications (introduced for the first time).

  • Trade-off: Spread – Application rate.

  • Spread control.

  • Congestion control in ad-hoc networks in broadcast mode.

  • Fairness with epidemic forwarding.

  • Identifying vulnerabilities that are specific to epidemic forwarding.

  • First prototype of network coding for ad-hoc networks.

  • Power independent signal acquisition for UWB in uncoordinated wireless ad-hoc networks (problem identification and solution).

  • Identifying key design elements for power saving with UWB IR systems.


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Used Expertise

  • Queuing theory

  • Performance evaluation tools

  • Wireless networks

  • Security

  • All layers of TCP/IP stack, mainly:

    • MAC (Medium Access Control)

    • Physical Layer (UWB IR and 802.11)

  • Vehicular networks

  • Network coding

  • Different channel models

  • Signal processing.

  • Simulation

    • ns-2, Jist-Swans, Straw (vehicular traffic simulator), Matlab.

  • System programming

    • Windows XP, Windows Mobile, Linux, OpenWrt

    • C++, J2SE, J2ME, raw sockets


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Demo: Ad-Hoc Ventes Flash

  • Spot application

  • Needs SLEF

  • Windows platform: XP and Mobile

  • C++

Client

Sender (shop)

  • Injects ads:

  • Shop name

  • Product features:

    • Name

    • Category

    • Price

  • Receive ads:

  • Filtering at the application level:

    • Shop name

    • Category

  • Vote


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Demo: Ad-Hoc Ventes Flash

X

X

X

X

X

X

Scenario: Persistent broadcast in presence of intermittent connectivity

Shop

Client 1

Client 2

Application on

Application on

Application off

Application on

Application off


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List of Publications at EPFL

Journal Articles:

Proceedings:

Pending Patent:

Demo

El Fawal, Alaeddine ; Le Boudec, Jean-Yves: A Robust Signal Detection Method for Ultra Wide Band (UWB) Networks with Uncontrolled Interference. In: IEEE Transactions on Microwave Theory and Techniques (MTT,) 2006.

El Fawal, Alaeddine ; Le Boudec, Jean-Yves et al. Tradeoff Analysis of PHY-aware MAC in Low-Rate, Low-Power UWB networks.In: IEEE Communications Magazine, vol. 43, num. 12, 2005, p. 147.

Raya, Maxim ; Aad, Imad ; Hubaux, Jean-Pierre ; El Fawal, AlaeddineDOMINO: Detecting MAC layer greedy behavior in IEEE 802.11 hotspots. In: IEEE Transactions on Mobile Computing, December 2006

El Fawal, Alaeddine ; Le Boudec, Jean-Yves ; Salamatian, KaveMulti-hop Broadcast from Theory to Reality: Practical Design for Ad Hoc Networks. In: First International Conference on Autonomic Computing and Communication Systems, October 2007.

El Fawal, Alaeddine ; Le Boudec, Jean-Yves ; Salamatian, KaveVulnerabilities in Epidemic ForwardingIn: The First IEEE WoWMoM Workshop on Autonomic and Opportunistic Communications (AOC2007), 2007

Merz, Ruben ; El Fawal, Alaeddine ; Le Boudec, Jean-Yves ; Radunovic, Bozidar et al. The Optimal MAC Layer for Low-Power UWB is Non-Coordinated.In: IEEE International Symposium on Circuits and Systems (ISCAS 2006), 2006

El Fawal, Alaeddine ; Le Boudec, Jean-YvesA Power Independent Detection Method for UltraWide Band (UWB) Impulse Radio Networks. In: IEEE International Conference on Ultra-Wideband (ICU 2005), 2005

El Fawal, Alaeddine ; Le Boudec, Jean-Yves

Synchronizing Method for Impulse Radio Networks, Date: 2005

El Fawal, Alaeddine ; Salamatian, Kave et al. A framework for network coding in challenged wireless network.  Presented at: MobiSys 2006, Uppsala - Sweden, June 19-22, 2006.


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The End

MERCI DE VOTRE ATTENTION


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Testbed

Testbed features:

Wireless router: ASUS WL-500GP

Technical specifications: 8MB Flash, 32MB RAM, 266MHz, 2 USB 2.0 ports

Firmware: OpenWrt

Configurable wireless interface: using Atheros card with MadWIFI driver (setting RTS/CTS Th, Tx power, Tx queue length…).

Mobility: using plumb batteries, +4 hrs lifetime with full power transmission at full rate

Robustness.