Lecture 9WSNs: Data Dissemination

Reading: • “Wireless Sensor Networks,” Chapter 12, sections 12.2-12.4.• W. Heinzelman, A. Chandrakasan, and H. Balakrishnan, “An Application-

Specific Protocol Architecture for Wireless Microsensor Networks,” IEEE Trans. on Wireless Communications, Vol. 1, No. 4, Oct. 2002, pp. 660-670.

• C. Intanagonwiwat et al., “Directed Diffusion: A Scalable and Robust Communication Paradigm for Sensor Networks,” Proc. MobiCom '00.

• W. Heinzelman, J. Kulik, and H. Balakrishnan, “Adaptive Protocols for Information Dissemination in Wireless Sensor Networks,” Proc. MobiCom ’99.

• D. Braginsky and D. Estrin, “Rumor Routing Algorithm for Sensor Networks,” Proc. WSNA, Sept. 2002, pp. 22-31.

2

Sensor Network RoutingEnergy-efficiency even more important than in MANETs“Resource”-aware, data-centric routing needed

Reduce power consumptionDistribute energy load (maximize network lifetime)Take into account sensors’ importance to application

May be tightly coupled with protocols from different layersTake advantage of data fusion opportunitiesCross-layer architectures

Types of routing neededOne-to-one: data to sinkMany-to-one: all sensors’ data to sinkOne-to-many: sink commands to sensorsMany-to-many: data dissemination, flooding, gossiping

3

Resource-aware RoutingConsider each sensor’s resources for routing decisions

Energy resourcesSensing resourcesOthers?

Energy-aware routingBalance power consumption so nodes fail uniformlyNode costs and link costs considered with different protocols

Fidelity-aware routingConsider importance of node to sensing applicationRoute around important “sensors”Takes into account redundant neighborsRequires local communication so nodes learn relative positioninginformation

4

Data-Centric RoutingAggregate data or information from data important

Individual data items not important

Sensor nodes themselves less important than dataQueries posed for specific data rather than data from a particular sensorRouting exploits the requirement for aggregate data rather than individual dataExample protocols

SPINDirected DiffusionRumor Routing

5

Network-wide Data Dissemination

A

B C D

F EGH

A,G,H

B,C,F B,C,D,E C,E,D

B,E,F,G

C,D,

E,FA,G,HA,G,H

A,B,F-H

A-H B-H B-G

A-H B-GA-C,E-H

A,B,F-H

A-H

A-H A-H A-H

A-H A-HA-H

A-H

Problem: information dissemination

6

Conventional Approach

B

D E

FG

CA

FloodingSend to all neighborsE.g., routing table updates

7

Resource Inefficiencies

Implosion

A

B C

D

(a)

(a)

(a)

(a)

A B

C (r,s)(q,r)

q sr

Data overlap

Resource blindness

8

What is the optimum protocol?

B

D E

FG

CA

“Ideal”Shortest-path routesAvoids overlapMinimum energyNeed global topology information

9

SPIN Family

Sensor Protocol for Information via Negotiation

Data negotiationMeta-data (data naming)Application-level controlModel “ideal” data paths

SPIN messagesADV- advertise dataREQ- request specific dataDATA- requested data

Resource management

ADVA B

AREQ

B

ADATA

B

10

SPIN Example

B

AADVREQDAT

A

ADV

ADVADV

ADVADV ADV

REQREQ

REQ

REQ

REQ

DATAD

ATA

DATA

DATA

DATA

11

SPIN Example 2

ADVE

DREQ

D

EDATA E

DC

ADV

E

DC

BA

A Nodes with dataA Nodes without data

Nodes waiting to transmit REQA

12

Simulation Results

-- SPIN-- Ideal-- Flooding

SPINConverges quicker than floodingReduces energy by 50% compared with floodingMeta-data negotiation successful in broadcast

13

Directed DiffusionAbstraction that tries to describe communication patterns underlying many localized algorithmsData named with attribute-value pairsNodes that want data express interests based on the predefined attributesInterests disseminated throughout networkInterests diffuse to correct area

Intermediate nodes propagate interests based on the contents of the interestE.g., if interest is for data from location (x,y), interest will be propagated towards (x,y)

14

Directed DiffusionInterests may be propagated to multiple neighbors for robustnessGradients set up that draw events of interest back to originating node

Strength of gradient depends on quality of the routing pathApplication-specific meaning to a gradient

Interests/data propagated along routes with strong gradientsGood routes inherently reinforcedCreates low-energy routing of data

Data aggregation and caching performed within the networkFurther reduces node energy dissipation

15

ExampleSensor nodes monitoring area for animalsInterested in receiving data about all 4-legged creaturesSpecify desired data rateQuery/interest

1. Type=four-legged animal2. Interval=20ms (event data rate)3. Duration=10 seconds (time to cache)4. Rect=[-100, 100, 200, 400]

Reply1. Type=four-legged animal2. Instance = elephant3. Location = [125, 220]4. Intensity = 0.65. Confidence = 0.856. Timestamp = 01:20:40

Attribute-Value pairs, no advanced naming scheme

16

Directed Diffusion ProtocolSinks broadcast interest to neighborsInterests are cached by neighborsGradients are set up pointing back to where interests came from at low data rateOnce a source receives an interest, it routes measurements along gradientsGradients from source to sink initially small

Increased during reinforcement

17

Directed Diffusion (cont.)

18

Design Considerations

19

Simulation Results

20

Rumor RoutingIn query-based networks, different techniques for routing data and queries

Query floodingExpensive for large query/event ratioAllows for optimal reverse path setupGossiping scheme can be use to reduce overhead

Event FloodingExpensive for low query/event ratio

NoteBoth of them provide shortest delay paths

21

Rumor RoutingAlternative: Rumor Routing

Designed for query/event ratios between query and event floodingMotivation

Sometimes a non-optimal route is satisfactoryAdvantages

Tunable best effort deliveryTunable for a range of query/event ratios

DisadvantagesOptimal parameters depend heavily on topology (but can be adaptively tuned)Does not guarantee delivery

22

Rumor Routing (cont.)

23

Basis for AlgorithmObservation

Two lines in a bounded rectangle have a 69% chance of intersecting

IdeaCreate set of straight line gradients from eventSend query along a random straight line from sink

What if this line is not really straight?

Event

Sink

24

Creating PathsNodes with data send out agents

Agents leave routing info to the event as state in intermediate nodes

Agents attempt to travel in a straight lineIf an agent crosses a path to another event, it begins to build the path to bothAgents also optimize paths if they find shorter ones

25

Algorithm BasicsAll nodes maintain a neighbor listNodes also maintain event table

When it observes an event, event added with distance 0Agents

Packets that carry local event information across networkAggregate events as they go

26

Agent PathAgent tries to travel in a “somewhat” straight path

Maintains a list of recently seen nodesWhen it arrives at a node, adds the node’s neighbors to the listFor the next hop, tries to find a node not in the recently seen listAvoids loopsImportant to find a path regardless of “quality”

27

Following PathsA query originates from sink and is forwarded along until it reaches its TTLForwarding Rules

If a node has seen the query before, it is sent to a random neighborIf a node has a route to the event, forward to neighbor along the routeOtherwise, forward to random neighbor using straightening algorithm

28

Agents

29

Simulation Results10 Events, 4000 Nodes

0

5

10

15

20

25

30

35

40

45

50

0 10 20 30 40

Number of Queries

Num

ber o

f Tra

nsm

issi

ons

(thou

sand

s)Query Flooding

A=28, La=500, Lq=1000

A=52, La=100, Lq=2000

Event Flooding

30

ObservationsWide range of parameters allow for energy saving over simple alternativesOptimal parameters depend on

Network topologyQuery/event distribution and frequency

Algorithm very sensitive to event distributionFault tolerance

After agents propagated paths to events, some nodes were disabledDelivery probability degraded linearly up to 20% node failure, then dropped sharplyBoth random and clustered failure were simulated with similar results

31

Geographic RoutingSensors often know locationUse location information to get data/queries to particular part of networkGeographic forwarding reduces

Routing overheadMemory utilization

Example protocolsGPSRTBF

32

Greedy Perimeter Stateless Routing (GPSR)

Greedy geographic forwarding algorithm

Forward data to node’s neighbor that makes most progress towards destinationMust keep track of neighbors’ locations

Obstacles can cause problems

Use “right hand rule” routing around holes

33

Trajectory Based Forwarding (TBF)

Similar to GPSR but allow source-specified trajectories for routesEnables

Multipath routing for added resilienceSpoke broadcastingBroadcast to remote subregion

34

ClusteringTo scale, hierarchical approach beneficialForm local clusters managed by cluster head

Fixed or adaptive cluster maintenance

Clustering providesFramework for resource managementSupport for intra-cluster channel access and power controlSupport for inter-cluster routing and channel separationDistributes management responsibility from sink to cluster headsProvides framework for data fusion, local decision making, localcontrol and energy savings

Example protocolsLEACHHEED

35

µAMPS Networks

Relevant parameters:System lifetime (energy efficiency)QualityLatency

Assumptions:Base station away from nodesAll nodes energy-constrainedLocally, data correlated

A

B D

E

C

FG Base station

f(A-G)

36

LEACH Protocol Architecture

Cluster-head

Base station

Low-Energy Adaptive Clustering HierarchyAdaptive, self-configuring cluster formationLocalized control for data transfersLow-energy medium accessApplication-specific data aggregation

37

Dynamic Clusters

Cluster-head rotation to evenly distribute energy loadAdaptive clusters

Clusters formed during set-upScheduled data transfers during steady-state

Time•••

START START START

Set-up Frame RoundSteady-state

Cluster-heads = •

38

Distributed Cluster Formation

Using Pi(t)

Choose CH with “loudest” announcement

Cluster-headNodes

Non-CHNodes

Node icluster-head ?Yes No

Wait forcluster-head

announcements

Send Join-Requestmessage to chosen

cluster-head

Announcecluster-head status

Wait forJoin-Request

messages

Steady-stateoperation for

t=Tround seconds

Autonomous decisions lead to global behavior

• No global control• Flexible, fault-tolerant

39

Distributed Cluster FormationAssume nodes begin with equal energyDesign for k clusters per round

Want to evenly distribute energy load

ktN

i=∗= ∑

=

1)(P CH] E[#1

i

k = system param.(Analytical optimum)

Each node CH once in N/k rounds

−=0

)/mod(* )(Pi kNrkNk

t 0 )(Ci =t

1 )(Ci =tCi(t) = 1 if node i a CH in last r rounds

Can determine Pi(t) with unequal node energy

40

LEACH Steady-StateSet-up Steady-state

Clusters formedSlot for node i

Slot for node i •••

TimeFrame

Cluster-head coordinates transmissionsTime Division Multiple Access (TDMA) scheduleNode i transmits once per frame

Cluster-head broadcasts TDMA schedule Low-energy approach

No collisionsMaximum sleep timePower control

41

Inter-Cluster Interference

BA

C

Cluster BCluster C

Transmission in different clusters can collideNodes minimize transmission powerEach cluster has unique spreading code Distributed solution to minimize interference

42

LEACH Medium Access

ADV Join-REQ SCH

ADV• CSMA• Large power, small messages

Time

Set-up Steady-state

CH Non-CH CH

Join-REQ• CSMA• Large power, small messages

SCH• DS-SS code• Power to reach all members

Data Transfer to Cluster-Head• TDMA slot (with DS-SS)• Power to reach CH, large messages

43

Data Aggregation

Energy for aggregation (J/bit/signal)

Tota

l ene

rgy

(J)

No DataAggregation

Local Data Aggregation

**

Clusters exhibit spatial localityLocal data aggregation

Computation vs. communication tradeoffDepends on cost of computation and communicationSignal processing within the network

44

Base Station Cluster Formation

Get optimal clusters for comparisonLEACH-C

Requires communication with base stationNodes send base station current positionBase station runs optimization algorithm to determine best clusters

Need GPS or other location-tracking method

45

Simulation Framework

Extensions to ns network simulatorComputation and communication energy models

StrongARM processor data aggregation Radio energy

LEACH, LEACH-C, MTE routing, static clustering

Transceiver Tx Amplifier Transceiver

Eelec* kk bit packet εamp* k * dn Eelec* kd

Transmitter Receiver

k bit packet

46

Simulation Parameters

Base Station

100

met

ers

100 meters

100 nodes

500 bytesData size

5 nJ/bit/signalAggregation cost

100 pJ/bit/m2Transmit amplifier

50 nJ/bitRadio electronics

100 kbpsBit rate

50 µsProcessing delay75 meters

47

Optimum Number of Clusters

Ave

rage

ene

rgy

per r

ound

(J)

Number of clusters (k)

k=7

k=9

k=5k=3

k=1

k=11

Too few clusters cluster-head nodes far from sensors

Too many clusters not enough local signal processing

48

Analytical Optimumk clusters N/k nodes/cluster:

dtoBS

dtoCH α 1/k

βα +=kNECH

δγ +=− kE CHnon

1

N=100M=10075 < dtoBS < 185

2 < kopt < 62toBS

opt dMNk ρ=

Simulation agrees with theory

49

Data per Unit Energy

Am

ount

of d

ata

rece

ived

at B

S

Energy Dissipation (J)

Static-Clustering

LEACH-C

LEACH

MTE

LEACH achieves order of magnitude more data per unit energy

2 hops v. 10 hops averageData aggregation successful

Nodes begin withlimited energy

50

Network Lifetime

Num

ber o

f nod

es a

live

Amount of data received at BS

Static-Clustering

LEACH-C

LEACHMTE

LEACH delivers over 10 times amount of data for any number of node deathsRotating cluster-head effective

51

Discussion