Infrastructureless Wireless networks

Infrastructure-lessWireless NetworksGwendal SimonDepartment of Computer ScienceInstitut Telecom2009

Literature

Books include:“Algorithms for sensor and ad hoc networks”,D. Wagner and R. Wattenhofer“Wireless sensor networks: an informationprocessing approach”, F. Zhao and L. Guibas

and journal/conferences include:ACM SigMobile (MobiHoc, SenSys, etc.)IEEE MASS and WCNCElsevier Ad-Hoc Network, Wireless Networks

2 / 41 Gwendal Simon Infrastructure-less Wireless Networks

Motivations

Current wireless net. require an infrastructure:cellular network: interconnected base stationswifi Internet: an access point and Internet

Same flaws than centralized architectures:costscalabilityprivacydependability


Motivations

Current wireless net. require an infrastructure:cellular network: interconnected base stationswifi Internet: an access point and Internet

Same flaws than centralized architectures:costscalabilityprivacydependability


Motivations

Sometimes, there is no infrastructuretransient meetingdisaster areasmilitary interventionsalter-communication

Sometimes not every station hear every other stationlimited wireless transmission rangelarge-scale area


Motivations

Sometimes, there is no infrastructuretransient meetingdisaster areasmilitary interventionsalter-communication

Sometimes not every station hear every other stationlimited wireless transmission rangelarge-scale area


Multi-hop Wireless Networks

Nodes: portable wireless devicestransmission ranges do not cover the areadensity ensures network connectivity

Links: wireless characteristicstransmission model: local broadcastingenergy consumption: transmission is costly

Behavior: devices emit, receive and forward data


A Taxonomy ofApplications


Ad-Hoc vs. Sensor Networks

Ad-Hoc Networks Sensor Networksnodes powerful wifi devices tiny zigbee nodes

algorithms all-to-all routing echo to sinkmobility human or car motions failures

performance criteria quality of service energy consumption


Ad-Hoc Applications

Delay-Tolerant Network (social media application)assumption: no connectivity, but high mobilityobjective: ensuring eventual message delivery

Mesh Networks (rural wireless coverage)assumption: some nodes have Internet accessobjective: maintaining path to these nodes

Vehicular Ad-Hoc Networksassumption: a particular mobility modelobjective: mostly services related to car safety


Ad-Hoc Applications





Ad-Hoc Applications





Sensor Network Applications

Sink-Based Networks (monitoring of natural areas)assumption: one sink retrieves all sensed dataobjective: increasing life-time

Mobile Object Tracking (area surveillance)assumption: sensors know their locationobjective: determining hostile position

Multi-Sink Networks (intervention teams)assumptions: mobile sinks and fixed sensorobjectives: increasing sink coverage












Short Introductionto Popular Models


Network as a Graph

Unit-Disk Graph:

→ node position

→ circular transmission

→ boolean connections00

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Network as a Graph

Unit-Disk Graph:

→ node position


→ boolean connections

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Network as a Graph

Unit-Disk Graph:

→ node position


→ boolean connections

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Interferences

Signal-to-noise-plus-interference (SINR) ratioPu

d(u,v)α

N +∑

w∈V \{u}Pw

d(w ,v)α≥ β

Pu: power level of sender ud(u, v): distance between u and vα: path-loss exponentN : noiseβ: minimum ratio


A Tour of the MostStudied Issues


Broadcasting I: Stormy Effect

Broadcast:a simple basic problem :

a source emits a messageall nodes within the network eventually receive themessage

a simple and efficient solution:upon first reception of message, forward it.

Limits of flooding in wireless networks:redundant messagesinterferences


Broadcasting II: Proposals

Probabilistic flooding:idea: forward the message with some probability pdrawbacks: no guarantee of deliveringrefinements: adjust p to node density

Constrained flooding:idea: only some nodes forward the messageimplementation: build the MinimumConnected Dominating Setdrawbacks: maintaining cost in dynamic systems


Broadcasting II: Proposals

Probabilistic flooding:idea: forward the message with some probability pdrawbacks: no guarantee of deliveringrefinements: adjust p to node density

Constrained flooding:idea: only some nodes forward the messageimplementation: build the MinimumConnected Dominating Setdrawbacks: maintaining cost in dynamic systems


Mobility Models I

Few theoretical proof, few real implementations⇒ generate realistic node motions for simulations

The simplest model: Random Waypoint1. each node picks a random position uniformly2. it travels toward this destination with a speed v3. once it reaches it, it stops during few seconds4. back to 1


Mobility Models I

Few theoretical proof, few real implementations⇒ generate realistic node motions for simulations

The simplest model: Random Waypoint1. each node picks a random position uniformly2. it travels toward this destination with a speed v3. once it reaches it, it stops during few seconds4. back to 1


Mobility Models II: Improvements

Basic Structural Flaws:non-uniform distribution of node location:

higher node distribution in the centeraverage speed decay:

low speed nodes spend more time to travel

Realistic Mobility Models:group movementarea popularityurban modelscommunity-based

the most realistic one : using real traces!


Mobility Models II: Improvements

Basic Structural Flaws:non-uniform distribution of node location:

higher node distribution in the centeraverage speed decay:

low speed nodes spend more time to travel

Realistic Mobility Models:group movementarea popularityurban modelscommunity-basedthe most realistic one : using real traces!


Localized Data Gathering

Basic idea: query data from sensors within an areatwo rounds:

query diffusionretrieve data from sensors

main objectives:minimize energy consumptionminimize the delay

A problem related with broadcasting except:only sensors from the queried area are reached:complex queries are possible (average, max, etc.)


Time Synchronization I

Different time on nodes:different oscillator frequency ⇒ frequency errorabsolute difference between clocks ⇒ phase error

The need of a common clocklocalization protocolssome MAC protocolsdata fusion in sensor network


Time Synchronization II

Broadcasting standard time via GPS system:√precision, simple implementation

× expensive devices× limited usage (outdoor environment)

Achieve a common time distributively:√(almost) no special devices required√more tolerant to the environment

× special protocols× message overhead, multi-hop delays


A Focus onRouting Protocols


Routing Protocols

Objective:select a path between a source and a destination

Main design challenges:unstable network topologylow-cost devices (energy, computing. . . )

Main routing mechanisms:neighbor discoveringroute setuproute maintenance


Proactive routing vs. On demand routing

Proactive ReactiveSetup all-to-all on demand

Maintenance regularly during utilizationAdvantages no setup delay no unused routes

Disadvantages fixed overhead long setup delayMain examples OLSR AODV


AODV Route Discovery

D

S B

EA

FG

H

BroadcastingRREQ Mes-sage.Setting upreverse path.

ReplyingRREP tosource.Forward pathsetup.



D

S B

EA

FG

H

BroadcastingRREQ Mes-sage.

Setting upreverse path.




D

S B

EA

FG

H






D

S B

EA

FG

H






D

S B

EA

FG

H






D

S B

EA

FG

H






D

S B

EA

FG

H






D

S B

EA

FG

H


ReplyingRREP tosource.

Forward pathsetup.



D

S B

EA

FG

H


ReplyingRREP tosource.

Forward pathsetup.


AODV Route Maintenance

D

S B

EA

FG

H

Link breaksbetween Band D.

SendingRERR mes-sage.

Restartingroute discov-ery.New routediscovered.



D

S B

EA

FG

H



Restartingroute discov-ery.New routediscovered.



D

S B

EA

FG

H



Restartingroute discov-ery.

New routediscovered.



D

S B

EA

FG

H



Restartingroute discov-ery.

New routediscovered.


Some Tricks

Intelligent flooding (detect close destination)idea: init TTL at 1, then 2, then 3. . .idea: flood slowly and send message to stop it

Route caching (use past flooding)idea: during flood, answer for a distant nodedrawback : contradict reactive routing philosophy

Local maintenance (almost unchanged route)idea: instead of NAK s, look for d by yourselfdrawback : sometimes it does not work


Some Tricks





Some Tricks





A Proactive Routing Protocol: OLSR

Objective: make use of Multi-Point Relay (MPR)acting as super-peerseasing topology discoveryhandling most of the traffic

OLSR message types:HELLO: discover 1-hop and 2-hop neighborstopology discovery through MPR


Neighbor sensing

D

S B

E A

F GH

BroadcastingHELLO Message.

Nb:{S},2hopNb:{}

Nb:{S},2hopNb:{}

Nb:{S},2hopNb:{}

Nb:{E},2hopNb:{}

Nb:{E},2hopNb:{S}

Nb:{S,E},2hopNb:{}

Nb:{E,F},2hop Nb:{}

Nb:{S,E,F},2hop Nb:{}

Nb:{F},2hop Nb:{S}

Nb:{E,F,B},2hopNb:{G,A}

Nb:{S,A,B},2hopNb:{F,G,D}

Nb:{S,B,G},2hopNb:{E,A,H}

Nb:{S,E,F,A,G},2hopNb:{D,H}

Nb:{F,B,H},2hopNb:{S,E,A,D}

Nb:{E,B,D},2hopNb:{S,F,G,H}

Nb:{A,H},2hopNb:{B,E,G}

Nb:{G,D},2hopNb:{B,F,A}


Neighbor sensing

D

S B

E A

F GH


Nb:{S},2hopNb:{}

Nb:{S},2hopNb:{}

Nb:{S},2hopNb:{}

Nb:{E},2hopNb:{}

Nb:{E},2hopNb:{S}

Nb:{S,E},2hopNb:{}

Nb:{E,F},2hop Nb:{}


Nb:{F},2hop Nb:{S}










Neighbor sensing

D

S B

E A

F GH


Nb:{S},2hopNb:{}

Nb:{S},2hopNb:{}

Nb:{S},2hopNb:{}

Nb:{E},2hopNb:{}

Nb:{E},2hopNb:{S}

Nb:{S,E},2hopNb:{}

Nb:{E,F},2hop Nb:{}


Nb:{F},2hop Nb:{S}










Neighbor sensing

D

S B

E A

F GH


Nb:{S},2hopNb:{}

Nb:{S},2hopNb:{}

Nb:{S},2hopNb:{}

Nb:{E},2hopNb:{}

Nb:{E},2hopNb:{S}

Nb:{S,E},2hopNb:{}

Nb:{E,F},2hop Nb:{}


Nb:{F},2hop Nb:{S}










Neighbor sensing

D

S B

E A

F GH


Nb:{S},2hopNb:{}

Nb:{S},2hopNb:{}

Nb:{S},2hopNb:{}

Nb:{E},2hopNb:{}

Nb:{E},2hopNb:{S}

Nb:{S,E},2hopNb:{}

Nb:{E,F},2hop Nb:{}


Nb:{F},2hop Nb:{S}










MPR selection

D

S B

E A

F GH





B

HELLO messageindicating B asMPR of S and Bnote S as its MPRselector.

MPR Selector:{}

MPR Selector:{}

MPR Selector:{}

MPR Selector:{S,G,E,F,A}

MPR Selector:{B,F,H}

MPR Selector:{B,E,D}

MPR Selector:{A,H}

MPR Selector:{G,D}


MPR selection

D

S B

E A

F GH





B


MPR Selector:{}

MPR Selector:{}

MPR Selector:{}




MPR Selector:{A,H}

MPR Selector:{G,D}


MPR selection

D

S B

E A

F GH





B


MPR Selector:{}

MPR Selector:{}

MPR Selector:{}




MPR Selector:{A,H}

MPR Selector:{G,D}


Topology Table

Each node maintains a Topology Tablecontaining all possible destinationsnotifying a MPR to reach them

Structure of Topology Table (on S for example):Dest Addr Last Hop Seq Holding Time

G B 1 10A B 4 20D A 6 10H G 5 15. . . . . . . . . . . .


Building the Topology Table

D

S B

E A

F GHMPR Selector:

{B,F,H}

BTopology Table

Des Lhop Seq HtimeF G 2 30H G 2 30


MPR Selector:{G,D}S

Topology TableDes Lhop Seq HtimeF G 2 30H G 2 30B G 2 30


MPR Selector:{A,H}

Broadcasting contin-ues. . .

D

B

A

GH

MPR Selector:{}

MPR Selector:{}

MPR Selector:{}




MPR Selector:{A,H}

MPR Selector:{G,D}



D

S B

E A

F GH


BTopology Table



MPR Selector:{G,D}S



MPR Selector:{A,H}


D

B

A

GH

MPR Selector:{}

MPR Selector:{}

MPR Selector:{}




MPR Selector:{A,H}

MPR Selector:{G,D}



D

S B

E A

F GH


BTopology Table



MPR Selector:{G,D}

S



MPR Selector:{A,H}


D

B

A

GH

MPR Selector:{}

MPR Selector:{}

MPR Selector:{}




MPR Selector:{A,H}

MPR Selector:{G,D}



D

S B

E A

F GH


BTopology Table



MPR Selector:{G,D}

S



MPR Selector:{A,H}


D

B

A

GH

MPR Selector:{}

MPR Selector:{}

MPR Selector:{}




MPR Selector:{A,H}

MPR Selector:{G,D}



D

S B

E A

F GH


BTopology Table



MPR Selector:{G,D}S



MPR Selector:{A,H}


D

B

A

GH

MPR Selector:{}

MPR Selector:{}

MPR Selector:{}




MPR Selector:{A,H}

MPR Selector:{G,D}



D

S B

E A

F GH


BTopology Table



MPR Selector:{G,D}S



MPR Selector:{A,H}


D

B

A

GH

MPR Selector:{}

MPR Selector:{}

MPR Selector:{}




MPR Selector:{A,H}

MPR Selector:{G,D}


Building the Routing Table

Topology Table on SDes Lhop Seq HtimeF G 2 30H G 2 30B G 2 30F B 3 30A B 3 30E B 3 30G B 3 30B A 6 30E A 6 30D A 6 30A D 7 30H D 7 30D H 8 30G H 8 30

Neighbor Table on SNb:{E,F,B},2hopNb:{G,A}

Routing Table on SDes Nhop HopsE E 1F F 1B B 1






Routing Table on SDes Nhop HopsE E 1F F 1B B 1A B 2G B 2






Routing Table on SDes Nhop HopsE E 1F F 1B B 1A B 2G B 2H B 3D B 3








































































































Any Hybrid Approach ?

Merging advantages from both approaches:build a routing table at 4 ∼ 5 hopslaunch a reactive process if d is not

Applicative concerns:OLSR is attractive because networks often smallAODV scales well but no all-to-all routing


Research Activity:Multi-Sinks QueryRange


Multi-sink Multi-hop WSN

0 50 100 150 200 250 3000

50

100

150

200 Target application: “fireman application”Many sensors (small, blue) and somefiremen (large, green)

Firemen talk directly with the sensors

Gather only local information

On demand, fixed rate data gathering

Hop based query, constrained flooding

Simple to deploy and scalable

Networking assumptions:

IEEE 802.15.4 MAC layer, ZigBee tree routing

No in-network data aggregation, compression

Static sensors and sinks (may extend to mobile sinks)



0 50 100 150 200 250 3000

50

100

150













0 50 100 150 200 250 3000

50

100

150













0 50 100 150 200 250 3000

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0 50 100 150 200 250 3000

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Simple to deploy and scalableNetworking assumptions:



Static sensors and sinks (may extend to mobile sinks)35 / 41 Gwendal Simon Infrastructure-less Wireless Networks

Network Sharing Without Congestions

0 50 100 150 200 250 3000

50

100

150

200Capacity of sensors c = 5Each flow consumes r = 1Nodes within u hops generate traffic

S1

S2u1 = 5

u2 = 1

Configurations Feasible?(5, 1) yes

u1 = 4

u2 = 2

Configurations Feasible?(5, 1) yes(4, 2) no

u1 = 3

u2 = 2

Configurations Feasible?(5, 1) yes(4, 2) no(3, 2) yesu1 = 2

u2 = 3

Configurations Feasible?(5, 1) yes(4, 2) no(3, 2) yes(2, 3) yes

62 configurations



0 50 100 150 200 250 3000

50

100

150


S1

S2

u1 = 5

u2 = 1


u1 = 4

u2 = 2


u1 = 3

u2 = 2

Configurations Feasible?(5, 1) yes(4, 2) no(3, 2) yesu1 = 2

u2 = 3


62 configurations



0 50 100 150 200 250 3000

50

100

150


S1

S2

u1 = 5

u2 = 1


u1 = 4

u2 = 2


u1 = 3

u2 = 2

Configurations Feasible?(5, 1) yes(4, 2) no(3, 2) yes

u1 = 2

u2 = 3


62 configurations



0 50 100 150 200 250 3000

50

100

150


S1

S2

u1 = 5

u2 = 1


u1 = 4

u2 = 2


u1 = 3

u2 = 2


u1 = 2

u2 = 3


62 configurations



0 50 100 150 200 250 3000

50

100

150


S1

S2

u1 = 5

u2 = 1


u1 = 4

u2 = 2


u1 = 3

u2 = 2


u1 = 2

u2 = 3


62 configurations


Which Configuration is Better?

0 50 100 150 200 250 3000

50

100

150

200Basic considerations:(4, 2): not feasible, (1, 1): inefficient

Optimality criteria:Maximum Impact Range

(5, 1): Sum up to 6Max-Min Fairness

(2, 3) = (3, 2) � (5, 1)

1

2 34

(?, ?, ?, ?)


Which Configuration is Better?

0 50 100 150 200 250 3000

50

100

150

200Basic considerations:(4, 2): not feasible, (1, 1): inefficient

Optimality criteria:Maximum Impact Range

(5, 1): Sum up to 6Max-Min Fairness

(2, 3) = (3, 2) � (5, 1)

1

2 34

(?, ?, ?, ?)


Problem Formulation

Multi-Dimensional Multiple Choice Knapsack Problema NP-complete problem

Toward a distributed heuristic algorithmonly local views of the networkonly local optimal solutions


Problem Formulation

Multi-Dimensional Multiple Choice Knapsack Problema NP-complete problem

Toward a distributed heuristic algorithmonly local views of the networkonly local optimal solutions


Protocol

0 50 100 150 200 250 3000

50

100

150

200

A 12

3

4

At each sink:enlarge requirement periodicallyreceive notification from sensorsadjust requirement if it is smaller

At each sensor:measure the trafficdetect congestionsolve the local problemnotify related sinks


Conclusion


Personal Thoughts

Great theoretical importance:a lot of new and scientifically exciting problemsa multi-disciplinary field (network, algorithms,computational geometry, probabilities)

Unsure applicative importance:no killer application yetcellular networks just do what we want


Personal Thoughts

Great theoretical importance:a lot of new and scientifically exciting problemsa multi-disciplinary field (network, algorithms,computational geometry, probabilities)

Unsure applicative importance:no killer application yetcellular networks just do what we want


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Infrastructureless Wireless networks

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