Data Aggregation and Cross-layer Design in WSNs

Rochester Institute of Technology

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2005

Data Aggregation and Cross-layer Design in WSNs

Carter May

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Recommended Citation

Data Aggregation and Cross-layer Design in WSNs

by

Carter May

A Thesis Submitted

m

Partial Fulfillment of the

Requirements for the Degree of

Advisor:

Fei

Hu

Master of Science

m

Computer Engineering

---Dr. Fei Bu, Assistant Professor

Committee Member:

---

Marcin Lukowiak

Dr. Marcin Lukowiak, Assistant Professor

Committee Member:

_S_h_a_n_c_h_ie_h_J_aL-y_Y_a

n~g",--____________________ __

Dr. Shanchieh Jay Yang, Assistant Professor

Department of Computer Engineering

Kate Gleason College of Engineering

Rochester Institute of Technology

Rochester, NY

Release Permission Form

Rochester Institute of Technology

Data Aggregation and Cross-layer Design in WSNs

I, Carter May, hereby grant pennission to the Wallace Library of the Rochester Institute of

Technology to reproduce this thesis, in whole or in part, for non-commercial and non-profit

purposes only.

Carter May

Carter May

Table

of

Contents

TableofContents 1

ListofFigures 3

Chapter 1 Introduction ₅

1 Abstract ₅

2 Data Aggregation 6

3 Cross-Layer Optimized Design 6

4 Research

Methodology

₆

5 Thesis Organization 7

Chapter 2 BackgroundandRelated Research 8

1 Wireless Sensor Networks 8

1.1 Common Network Topologies ₈

1.2

Clustering

8

1.3 Trees 10

1.4 Ad Hoc Peer Networkvs.WSN 10

2 Data Aggregation 12

2.1 Data Aggregation

Timing

14

3 Cross-Layer Design 17

3.1 Traditional Network Model

-OSI Stack 17

3.2 MAC-layer design in WSNs 19

4

Summary

20

Chapter 3 A Novel

Timing

Control for Optimal Data Aggregation 21

1 Problem Statement 21

2 Assumptions 23

-2.1 Cluster Size 24

3 Proposed Time Synchronization Algorithm 27

3.2 Control Parameter Refresh Rate 30

3.3 Finite State Machine ImplementationofAlgorithm 31

3.4 LevelofAggregationChosen

by

Sink 34

3.5 Height-based Differential Analysis 35

4

Energy

AnalysisofProposed

Timing

Synchronization Algorithm 38

5 ApplicationsandExamples 46

Chapter 4 Cross-layer Design 49

1 Introduction 49

1.1 Traditional Network Model 49

1.2

Existing

WorkonCross-Layer Design 50

1.3 WSNMAC layer design 51

2 Our Proposed

Routing

Optimization

Using

MAC Layer Information 53 3 Our Proposed MAC Layer Optimization

Using Routing

Layer Information 55

3.2 Optimized

Sleep

Schedule 57

3.3 Proposed Route-aware MAC Optimization: FSM Control 60

3.4 Proposed Route-aware MAC Optimization: Protocol 61

4 Mathematical Analysis 62

4.1 Formulae 62

4.2 Visualization 62

5

Summary

63

Chapter 5 OPNET SimulationofData Aggregation

Timing

Control 64

1 Simulation Design Requirements 64

2 Locationfor

Aggregating

Data 65

2.1 Between MACand

Routing

Layer 65

2.2 Inthe

Routing

Layer 65

2.3 Betweenthe

Routing

andApplication Layer 65

3 Simulation Implementation Overview 66

3.1 Intelligent Optimization BasedonLate Packets 69

4 SimulationDetails 69

4.1 Packet Format 69

4.2 Node FSM 70

4.3_ Sink FSM 71

5 OPNET Simulator Implementation 72

5.1 Sensor Node Simulation Model 72

5.2 Sink FSM 73

5.3

Routing

Layer 76

6 SimulationResults 78

6.1 ProofofConcept 79

6.2 PerformanceComparison 82

6.3 ComparisonResults 84

7

Summary

89

Chapter 6 ConclusionandFutureWork 91

1 DataAggregation

Timing

Control 91

2 Cross-Layer Optimized Design 91

3 Future Work 92

[image:6.546.82.466.83.589.2]

List

of

Figures

Figure 1: SimpleTree Network 9

Figure 2: TypicalWSN

Using

a

Clustering Topology

9

Figure3: Simple Network

Query

Response,

No Aggregation 13

Figure4: Unbalanced NetworkwithPotential

Timing

Problem 14

Figure5: Response Times for Several Aggregation Schemes 16

Figure 6: Simple NetworkwithPotential Aggregation

Timing

Problem 22

Figure 7: Simple TreeNetwork 24

Figure 8: TreeSizevs.Cluster

Complexity

25

Figure9:

Setup

vs.DataCollection Phases 28

Figure 10: FSM#1 33

Figure 11: FSM #2 33

Figure 12: FSM #3 34

Figure 13: Unbalanced

Tree,

Aggregation Example 35

Figure 14: NetworkwithUnbalanced Tree 36

Figure 15: Chain Plus Child Near Leaf 37

Figure 16: Chain Plus Child Near Sink 37

Figure 17:

Energy

Savingsvs.PercentageofAggregation 42

Figure 18: NumberofMessages Receivedvs.Aggregation Period 43

Figure 19: Simplified NumberofMessages Receivedvs.AggregationPeriod 43

Figure 20: Convergence Timefor FSM #1 44

Figure 21: Convergence Time for FSM #2 45

Figure22: Convergence Time for FSM #3 46

Figure 23: HotspotCreated

by Routing

Algorithm 54

Figure24:

Topology

with

Likely

Channel Contention Problem 55

Figure 25:

Energy

Savingswith

Variably Participating

Nodes 57

Figure 26: Example Route 58

Figure 27: Original

Sleep

Schedule 59

Figure 28: Optimized

Sleep

Schedule 59

Figure29: Finite State Machine 60

Figure 30: Idle

Listening Energy

vs.

Duty

CycleandActive Route 63

Figure 31: Idle

Listening Energy

vs.

Connectivity

andActive Route 63

Figure 32: Node FSM 70

Figure 33: Exampleof

Query

Propagationfrom SinktoLeaves 71

Figure34: OPNET ImplementationofNode FSM 73

Figure 35: Simulated NetworkLayout 74

Figure 36: Data Sink Node Model 74

Figure 37:OPNET ImplementationofData Sink 75

Figure38: OPNET Implementationof

Routing

Layer 78

Figure 39: NumberofResponses Reduced

Dynamically

79

Figure40: Timeout Period Reduced

Dynamically

80

Figure41: NumberofResponses Increased

Dynamically

81

Figure42: Timeout Period Increased

Dynamically

81

Figure 44: Timeout

Period,

n_opt=_{10 86}

Figure 45: Timeout

Period,

Time

Average,

n_opt= _{10 87}

Figure 46: Timeout

Period,

nopt=_{8 88}

Figure 47: Numberof

Responses,

Time

Average,

nopt=

Chapter 1 Introduction

1

Abstract

Overthepastfew years,advancesinelectrical _{engineering have}allowed electronicdevices toshrinkin bothsize and cost. It has becomepossibletoincorporateenvironmental sensors into asingledevicewith a microprocessor and_memorytointerpretthedataand wirelesstransceivers tocommunicatethedata. These "sensornodes"

have becomesmall and_cheapenoughthat_they

can be distributed in _verylarge numbers intothe areato bemonitored and can beconsidered

disposable. Once

deployed,

thesesensor nodes shouldbeabletoself-organizethemselvesintoa usable network. These "wirelesssensor

networks,"

or

WSNs,

differ fromother adhocnetworks

mainly inthe_waythat_theyare used. For_example, inadhocnetworks of personal _computers, messages are addressedfromonePCtoanother. Ifa message cannotberouted, thenetworkhas failed. In

WSNs,

dataabouttheenvironmentisrequested

by

the"datasink."

If anyor multiple sensor nodes can return an informativeresponseto this request, thenetworkhassucceeded. A

networkthatisviewedintermsofthedata itcandeliverasopposedtotheindividual devicesthat make_{it up has been}termeda

"data-centric"

network[26]. The individualsensor nodes_{may fail}

to respond to a query, oreven

die,

as

long

as the final result is valid. The network _{is only} considered useless when no usabledatacanbe delivered.

In this_thesis,wefocuson twoaspects. Thefirst is data aggregation with accurate_timing control. Inordertomaintaina certaindegreeof servicequalityand a reasonablesystem

lifetime,

energy needs to be optimized at _every stage of system operation. Because wireless communication consumes a major amount ofthe limited

battery

powerforthese sensornodes,

we proposeto limittheamount ofdatatransmitted

by

_combiningredundantand_{complimentary}

data as much as possible in order to transmit smaller and fewer messages.

By

_using mathematical models and computer _simulations, we will show that our aggregation-focused protocol

does, indeed,

extend system lifetime. _{Our secondary} focus isa _study of cross-layer

design. Wearguethatthe_extremelyspecialized use ofWSNsshould convince us nottoadhere

to thetraditional_{OSI networking}model. Throughourexperiments,we will showthatsignificant

2

Data Aggregation

The first issue we have investigated in this thesis work is related to the issue ofData Aggregation. Themost_{important issue in Wireless Sensor Networks is energy}consumption. To

this end,many networkingschemes attempttominimizetheamount ofdatatransmitted

by

_using dataaggregation. Thistradesoffdatafreshnessand

delay

forsavingsin_energy,becausereports

fromsensor nodesthatarriveat an_aggregatingnode_{may have}tobe heldthereforsome period

oftime before

being

reportedso thatadditional reports_may reach theaggregatorfrom slower1 nodes. Weproposetouse an intelligenttimerand somehigh-level knowledgeofthenetworkto

implementan efficient aggregation _timingcontrol protocol. Ourprotocol aims to

dynamically

changethedataaggregation period_accordingto theaggregation quality. The datasink willissue

requeststhatincludebothadesirednumber of responses and a maximumtimeinwhichitwishes

to receive them. In some situations, the sink _{may only} require responses from a few sensor

nodes out ofthe

field,

butthe timelinessoftheseresponsesisstillimportant.

Aggregating

nodes attempt to provide as _many responses as possible within time constraints, which also allows

maximum aggregation and _energy savings. Responses that cannot be provided in time are

ignored andthe sinkis responsible _{for specifying}more relaxed _timing constraints for itsnext

query.

3

Cross-Layer Optimized Design

Inthisthesis,inadditionto the_timingcontrol scheme addressed _above, we aimtodefinea

customized cross-layer network model for WSNs that reduces overall power consumption

by

making anyseparatelayersaware of usefulinformation fromotherlayers. Weintendtodesigna

network model that integrates routing,

MAC,

and other operations with the goal of overall system_efficiencyand_{low energy}consumption. InformationavailableintheMAC layerwillbe

used tomake more efficient_{routing layer decisions.} Lower-layer information more accurately describesthecondition of_{the network,}so shouldbe_tightlyintegratedwithhigherlayers

4

Research

Methodology

1

Weusethe term"slower"heremerelytodenotethata report of a related eventtakeslongertoreachthe aggregator. _{This may be}caused_bythenetworktopology,theTDMA schedulingpolicy,or_{physically lost}

We intendto_verifyour_{ideas both mathematically}and_usingcomputer simulations. Wewill

usetheOPNETsimulatorto implementa sensor network with ourdataaggregation algorithms

[31]. Our OPNET model will allow _{easy implementation} of various _routing and MAC

algorithms,as well as new aggregation algorithms as_theyare proposed. Wewill also useMatlab toprogram and_verifyour numerical analyses.

5

Thesis Organization

Therest ofthis thesisisorganizedasfollows. In Chapter 2we presentthestate oftheartin

regardto sensor networks andthe twoissues we wishto address: dataaggregation and cross-layerdesign. Chapter 3 is devotedto dataaggregation _timing in sensor networks and howto

intelligently

and

dynamically

update the _timing to _satisfy

latency

and _energy savings requirements. Chapter 4 analyzesthe_energysavings

by

designing

a MACscheme thatmakes use of_{routing information.} Chapter 5 showsdetailedsimulation results ofthedataaggregation

Chapter

2 Background

and

Related

Research

This chapter describes our background research and some related works in the field of

wireless sensor networks. We firstpresenta general introductiontosensor networktopologies,

followed

by

specific backgroundontheresearchissuesofthis_thesis,i.e._timingcontrol in data

aggregation and cross-layerdesign. We describethe current research in theseareas

here,

and thenexplore our_newlyproposedideas in Chapters

3, 4,

and5.

1

Wireless Sensor Networks

1.1

Common Network Topologies

Wireless sensor networks _typically contain thousands or tens ofthousands of nodes, but

spatiallyor

logically

related nodes_maypossess redundantdata [21]. Forthese reasons,manyof thenetworktopologiesusedinsmaller scale ad hocandaddress-centric wired networks are not

appropriatefor WSNs.

Atypicalnetwork_topologyfor"wired"

networksisatree. Therootnodesare_{usually DNSs}

(domain controllers), and messages are routed through intermediate controllers to end user

computers, orleaves. Aproposed_topologyfor WSNs is clustering,whereby 50or 100sensor nodesare grouped together and,forthemostpart, are used as a single entity. Inthis thesiswe proposeto make use ofboth methods(i.e. clusters and trees), so thatgroups of sensor nodes combinetheirreports atthelowest

level,

thenreports_maycontinuetobeaggregated as_theypass

back uptheaggregationtree[20].

1.2

Clustering

Many

WSN topologies make use of_{clustering [17].}

Usually,

a number of regular sensor

nodesare organized underthecontrol of a

"clusterhead."

Withintheclusterthe_topologyvaries.

The nodes _{may be} organizedintoa flat _topology, a tree

hierarchy,

etc. Consider thecase in

whichthereare a numberofclusters,each with a clusterheadthatreportstoa commondatasink.

In this case, theclusterheads will

likely

be organized into a treewith the sink atthe root, as

be multiple levelswithin the tree with several clusterheads _reporting to a singleintermediate

nodethat,intum,transmitsresultsto thesink.

Level_2, Sink/Clusterhead

Level 1,

Clusterhead/Node

Level0,

Cluster/Leaf Node

[image:12.546.84.458.314.549.2]

Figure 1: SimpleTree Network

Figure 2 shows a networkthat_{has been divided up into}clusters[13]. Inthe

figure,

eachof

the three clusters has aclusterhead thatcommunicates

directly

with the command node (data

sink). Theregular sensor nodescommunicate_onlywiththeclusterheads and perhaps one ortwo

intermediate sensorin thisdiagram. In addition, the clusterheads_maycommunicate between

themselves. Theseclusterheads may be especiallypowerful nodes, but often_they are regular

sensor nodesthathave beengiven additional responsibilities on atemporarybasis.

1.3

Trees

Traditional networking schemes make

heavy

use of some sort ofMasters or Controllers.

Thiscentral server would beresponsiblefor

individually

_queryingeach nodeand_aggregatingall

ofthefinal data

[42]

andisanalogousto theend sinkin WSNs.

Often,

thecontrollerisassumed

tohaveinfinitecomputational resources. In

WSNs,

thesemaster nodes

(clusterheads)

are most

likely

regular sensor nodes that have been tasked with greater responsibility. Multiple

clusterheads are organized under a data sink, but these local leaders do not have infinite resources as in the traditional paradigm.

They

arejust as resource-constrained as the other members of their group, so most of the computation and communication should remain

distributed,

with_onlythecore coordinationfunctions

being

givento themaster. In Figure

1,

the

sink_mayor_maynotbe_{energy-constrained,}but intermediateclusterheads (level 1

)

should still

only begiven minimal extratasks.

Ifatreeistobeused asthenetwork_topology,itcanbecreated

by

some_existingalgorithms [49]. For

instance,

abreadth-first-searchcan create an_{optimally balanced}tree. Thiswillgreatly aid network management and data aggregation, however the complexity ofthis algorithm is

Otime(diameter)

and0meSsage(nxdiameter+|E|). This_{is rarely}usedinsensor networksdueto its

lackof_{scalability [42].} Eventhecommon minimum_spanningtree

(MST)

_{has complexity 0(n}

)

[13]. Inaddition,MSTalgorithmsdonottakeintoaccountthebroadcastnatureofWSNs. For

example,atransmissiontoadistantnode_{may be}overheard

by

closernodes,butcommunication

to eachindividual node is modeled

by

a weighted edge [32]. Thechoice ofthe tree-building

structureis

important,

butoutsidethescope ofthis thesis. Severalschemeshave beensuggested for

building

tree networks appropriate for sensor networks [49].

Specifically,

our data aggregation protocolsare compatible with_anytypeoftree topology.

1.4 Ad

Hoc Peer Network

vs.

WSN

Wirelesssensor networksare,ineffect,specialized adhocnetworks.

However,

_theydiffer in several ways. Whenonethinksof a

"traditional"

adhoc_network,one envisions several PCsor perhaps wireless devices communicatingwithin alocal area. Communicationprotocols reflect

this typeof network

by

_stressingperformance metrics such as_reliability, bandwidth_utilization,

energy-constrained. Whena node's

battery

powerisexhaustedit isnolongerusefulinthenetwork and thenetwork suffers_{exponentially}as nodes continuetodie.

Individualnodes_may

frequently

becomeunreachableduetodynamicenvironmental

factors,

malfunctions, and _{energy depletion.} The whole network must be able to recover from these failures as _efficiently as possible and would _preferably operate _{transparently} to them. In addition,WSNsare subjecttothe

following

listofdifferencesfromtraditionaladhocnetworks [1]:

Thenumberof sensor nodesina sensor network canbeseveral orders of magnitude higherthanthenodesinan adhocnetwork.

Sensornodes are

densely

deployed. Sensornodesare pronetofailures.

The_topologyof a sensor network changesvery frequently.

Sensornodes_mainlyusebroadcastcommunication paradigm whereas mostadhoc networks arebasedon point-to-point communications.

Sensornodes arelimitedinpower,computationalcapacities,and memory.

Sensornodes _may not haveglobal identification

(ID)

because ofthe large amount of overhead andlargenumber of sensors.

AllofthesefeaturescombinetomakeWSNsa unique area ofresearch. _{Previous networking}

designs areserviceable inmost _cases, but_they perform _{sub-optimally} when_{energy-efficiency} and systemlifetime becomethe_topconcerns.

WSNs differfrom"wired"

and_manyother wireless networksinthat_theyare adhocand self-configuring. There isno central controllerthat is aware of eachindividual sensornode;nodes must communicate with direct neighbors to ensure overall network connectivity. Though cellular networks communicate wirelessly, the distribution of_controlling entities

(towers)

is well-planned andtheyhavesufficient_processingpowertocontrolthedeviceswithin theircell. Internet protocols can

dynamically

discover routes between any two

hosts,

but intermediate routersbetween a source and a destination are much more stable than in a wireless network. Messagesare_generallyrouted_upa

hierarchy

ofdedicated hardwaretoan establishedbackbone. WSNsmust transmitsenseddata from distributedsources through theirpeersback to a single sink. Sensornodes have high failureratesand _may movearound a sensor

field,

breaking

old links and _creatingnew ones. A route evaluated as efficient

during

one communication round

thenetworklayer. Abovethis,any numberof applicationsmayrun. Sensornetworks are_very

application specificinthatan entire networkisconfiguredanddeployed fora single purpose.

Wirelesssensor networks canbecomparedtocommonBluetoothnetworks. Like

WSNs,

a

Bluetooth network must be able to self-configure within a reasonable amount oftime after

deployment (ad

hoc),

and uses a_potentially

lossy

communication medium. Bluetoothnetworks

formthemselvesintoa star_topologymade_upof a single master and_upto seven slaves. These

mini-networks are termed a

"piconet."

Within _{this piconet,} the master is responsible for

assigning both a TDMA schedule and a

frequency-hopping

schedule.

Physically,

Bluetooth

devices usually transmit at around 20 dBm

[1],

and a piconet might cover 10 or 12 meters.

Nodes inthesenetworks_{may be}

battery

powered,butarelargeenough andfewenoughthat_they

canbe easily_{collected, recharged,}and maintained.

On the other

hand,

WSNs are made_up of_many more nodes with shorter communication ranges. This has led to the idea of_clustering,where a cluster_{may be} thoughtof as a large

piconet,butnumerous clusters mustinteracttoserve as a usable network

[2]

[10]. Thescale of thenetwork exacerbatestheproblemofMAC-Iayercontrol considerably. Thenetwork_topology must be maintained under _{extremely dynamic} conditions, due to _{mobility but} also the high failureand error rate ofthesensor nodehardware.

The

following

section provides the state-of-the-art in the area ofData Aggregation

()

in

wireless sensornetworks,whichisone ofthefocusesofthisthesis.

2

Data Aggregation

Somecommunication schemes usethenaive approach of

having

_everynode communicateits datato_everyother node. As discussed in

[1],

thisis inappropriate forsensor networks.

Only

a powerful master or controller node could even addressthe potential hundreds ofthousands of sensor nodes. Insensor networksthisis not evennecessary,as _onlythesinkcan make use of collecteddata.

Dataaggregationis a mechanism

by

which_onlya portion ofthedatareceived

by

a host is

retransmitted. Thisis done

intelligently

asthehost

(or,

ina wireless sensor_{network, the}sensor

node) uses some criteria to _{decide specifically} what data need not be retransmitted. Most

able to transmitfewermessage

headers,

thoughall original datamust stillbetransmitted. Also easilycalculatedbutcapable of_showingmore_{dramatic energy}savings arefunctionssuch as min and max. _{For example,}a chain of_{k nodes,}each with an8-bitvaluetoreport can communicate

the minimum or maximum value from one end of the chain to the other with _{only -1} transmissionsandk-\ mathematical comparisons. Two 8-bitvalues are compared at each node and_onlyoneisretransmitted

by

each. This isaformof_compressingrepresentativedata intoa singlefinalaggregated result.

Figure 3shows a simplified network with an establishedhierarchy. Weconsider a network

tobeorganizedinatree

topology

forreasonsdiscussed later inthis thesis. Itcanbeseen_that,if

noaggregation_{is performed,}therewillbeatotalof18transmissions_assumingallleafnodes and onlytheleafnodes respondto thequery. Shouldtheintermediatenodesalsohave datato report, therewillbe 21 transmissions. Ifresponsesfromtheleafnodes couldbecombined intoa single response at eachintermediatenodetherewould_{only be 12}transmissions torespondto thesame query.

Non-aggregated responses, 9 transmissions

si\A\A*

Individual responses, 9 transmissions

Figure 3: SimpleNetwork_{Query Response,}No Aggregation

Though ithas beenproventhatdataaggregation can save_{energy in}wireless sensornetworks,

research issues such as where and when to perform the aggregation still exist [5]. Network designersrequire,

first,

an algorithmthatcan choose an optimallocation inthenetwork_topology

toperformdataaggregation.

Some aggregation schemes that have been proposed are shortest path tree

(SPT),

center

nearestsource

(CNS),

andgreedy incrementaltree

(GIT) [26]

[43]. SPT isthesimplest ofthese.

Each messageis routed to the sink viatheshortest path. Data is aggregated _{opportunistically}

aggregationrequires the most overhead toset_upout ofthese threeaggregation schemes [26]. SimilartoDjikstra's_{link-state routingalgorithm,}aggregation pointsare chosen

iteratively

before

aggregationbegins. Thisrequiresknowledgeoftheentire network andlinkcosts. Though it is not_necessarilya priori

knowledge,

itcouldbeconsidered_such,sincetheaggregationtreemust becreated

independently

fromthe_{routingtopology}[22].

2.1 Data Aggregation

Timing

Most researchers agree that data aggregation is a useful technique _{for reducing energy}

consumption in wireless sensor networks

[26]

[41].

However,

how to determine appropriate aggregation

timing

characteristics remains a

largely

unexploredfield.

Inalargescale

WSN,

there_{may be}a significant

delay

betweenwhen an eventissensed(ora

query is answered) and when thedata reachesthe sink. For maximum _{energysavings,} each aggregating sensor should be able to transmit all data received from all ofits children at one time. Thiscouldleadtoan even larger

delay

betweenwhen thedataoriginates and when itis

finally

reported, as each _aggregating node _{may have} to pause before sending a report to its parent

Depending

onthe_priorityof_energy savings vs. maximum

latency,

it may make more senseforan aggregatortowaitforall, some,or_onlythefirstofitschildrentorespond.

For_example, Figure 4 shows a network with a potential _timingproblem. In the case in

which leaf nodes and _{only leaf} nodes respond to a _query, if Node B wishes to aggregate responses from itschildren, it will be facedwith adecision of whethertowaitfortheslower responsefrom Node Corto transmit theresponsefrom Node D firstand sendC'sresponselater. Thisassumesthateachtransmissionover a wirelesslinktakes_exactlythesame amount oftime. Ifthisisnotthe case, thetimingproblembecomesmore complicated andless deterministic.

Level 3 B39 Sink

Level2

Level 1

Level 0

Figure4: Unbalanced NetworkwithPotential_TimingProblem

aggregation, but stopshort of_specifyingdetails forany specific implementation. Others have

proposeddetailed aggregation schemes intendedtobeused withDirected

Diffusion,

including

"Greedy

Aggregation"

[41]

and CLUDDA [7].

However,

none ofthese address the issue of

timing

controlbetween differentlevelsof sensors. Mostaggregation schemesfocuson

finding

an optimal node at which to aggregate

data,

_{simply mentioning}that aggregationis performed

thereand often_{assuming in}theircalculationsthatall responses received are aggregated before

being

propagated.

Based onthe

timing

modelsdescribed

by

SolisandObraczka in

[41],

existingperiodicdata

aggregation protocols canbeclassified into three categories,namely:periodicsimple,periodic

per-hop,and periodic_per-hopadjusted. Periodicsimpleaggregation works

by having

each node wait a pre-defined period of_time,aggregate alldata itemsreceived,and send out a single packet

containing the result. Aggregation mechanisms in theperiodic_{per-hop category have} nodes

sendtheaggregated packet as soon as

they

hearfromalltheirchildren.

Excessively

latereports are dropped as inperiodic simple.

Finally,

periodic_per-hop adjusted schemes use the same

basicprinciple ofperiodic_{per-hop but}schedule a node's timeoutbased on itsposition in the

distribution tree (rooted at the information sink and _spanning all _reporting and intermediate

nodes). The aggregation scheme proposed

by

this thesis fallswithin this category, and, when

comparedto other_existingperiodic_per-hopadjusted_algorithms,presentsbenefitssuch as not

requiringclock synchronization _amongnodes and_minimizingthe timeout_schedulingoverhead.

Thedetailswillbe discussed laterinthe thesis.

WereferagaintoFigure3. Nodesa,

b,

and c are child nodes.

They

transmittheirresponses

atthe end oftheirrespectivetimeouts, whichare not_necessarilysynchronized.

Theoretically,

since_theyare allleafnodes,theycouldtransmit earlier,butthisexampleholdsfornodesinternal

to the treeas well. Node d isan aggregator and the parent oftheotherthree nodes. It will

ideally

receive responsesfromall ofitschildren,butwilltimeout after somelengthoftime.

Figure 5 shows the response schedule forrepresentative periodic _simple, periodic_per-hop,

and periodic_per-hopadjustedschemes as_theywould_applyto the treeshownin Figure 3. Two

examplesare givenforeachscheme;child nodesrespond atthesametimeinallthreeschemesin

each example. Theshaded sectionsindicatetimebefore theaggregator'stimeout that itwould

nothavetowaitbecause itschildrenhaveall responded. Inthesecond_example,not all children

Nodea

II

II

Periodic b

II

II

Simple _c

II

II

d

1

1

1

'

1

Nodea

~r

I

I- ~T~

Periodic b

l

r

I

Per-hop c T~

~i

I

T~

d

i

ii

i

i; .

Nodea

~Tl

~n

Periodic b

~i

n

Per-hop c T~

n

Adjusted _$ 1i.^^i

r

U

Figure 5: Response Times for Several Aggregation Schemes

Periodicsimpleisthesimplestto

implement,

butexhibitsthemaximum

latency

inall cases.

Periodic per-hopreducesthe

latency

in thefirstexample, as expected. Inthesecondexample,

both oftheseschemes must

drop

nodeb's response(or wait until thenext_reporting period).

Finally,

periodic_per-hopadjusted performs

ideally

in bothexamples. Becausethechildren are

configured with a shortertimeout, this scheme can aggregate all responses before its timeout

eveninthesecond example.

Directed Diffusion's communication paradigm is basedon information sinks

broadcasting

requests, or_interests,forrelevantdata. Nodeswhohaverelevant informationrespond to these

requests anddatapathsareformed alongthereturn route. "Better"routes aredetermined

by

the

numberand _qualityof responses that are sent _{along it.} Data is aggregated _{opportunistically;}

wheneveridenticalresponses orqueries meet at a node_onlyoneisretransmitted. _{Though every}

node can_potentiallyperform_aggregation,nodesintheshortest pathfrom informationsourcesto

thesinks are responsibleformostofthe_energysavings[22].

In periodic simple aggregation _protocols, all nodes wait a pre-defined amount of time,

aggregate all thedatareceived within_{that period,} and send out a single packet[41]. Directed

Diffusionfalls intothis _categoryonlywhen all nodeshaverelevantdatato send. In _{this case,}

reinforcementqueriesfromthesink_specifythedesiredresponse rate. Notethat nodesare not

necessarilysynchronized when

"clocking

out"

data. Thoughseveral_closelygrouped nodes_may

respond at an identical rateof once per_{minute, these} individualresponses _may not go out at

TAG

[28],

or

Tiny

AGreggation,

is a good example of a periodic _per-hop adjusted

aggregation mechanism(see Figure 5). TAG uses aggregationas queries are processed within thenetwork. Somequeries in TAGrequest reportstobesentfromsensors periodically. Inthis

case,TAG

intelligently

subdividesthedatacollection

"epoch"

intosmallerslots. Eachslotisthe

epoch length divided

by

D,

the depth ofthe tree.

Following

_per-hop adjusted aggregation

operation, slots are assigned to nodes in

decreasing

order,

D, D-l, D-2,

..., as the query propagatesthrough thenetwork. Thisscheme requiresknowledgeofthenetwork_topologyand

time synchronizationbetween nodes,but allows nodes topowerdown when notscheduled to

transmit or receive. Our proposed aggregation scheme is not affected

by

the potential _sleep

schedule and ourMAC layerscheme,discussed

later,

takesfulladvantage ofit.

In additiontoTAG

[28],

another well-received aggregation schemeis AIDA [15]. Though

both ofthese schemes suggest several _{potentially energy-saving}

ideas,

_they focus on disjoint

aspects of aggregation. TAG'smainfocus ison an efficient_{querying language}thatisconducive

to aggregation,but is mainlyan application-level optimization.

AIDA,

ontheother

hand,

inserts

a newlayer intotheprotocol stackthatinterprets and repackagesdataneartheMAC

layer,

but doesnot considerdynamic_timingparametersbased on application-level requirements. Bothof

theseframeworksare usefulforreducing energyconsumptionin WSNsand are compatible with

our_{timingprotocol,}but donotadequatelyaddresstheissuesthatwehopetosolve. Theconcept of

"cascading

timeouts,"

where nodes would waitfora period oftime

directly

related to their depth in the aggregation _tree, was _recently proposed in

[41] by

Solis and Obraczka. Though this is a _potentially useful _{optimization,} its main _{shortcoming is} that it

requires significantadditionaldatatobetransferred

during

the_setupperiod. Our_timingcontrol

scheme requires minimal overhead, but is flexible enough to allow expansion for later optimizations.

Wenextdiscussthestate-of-the-artinthearea of cross-layer

design;

thesecondfocusofthis thesis.

3

Cross-Layer Design

3.

1

Traditional

Network Model

-OSI Stack

The

following

isabriefoverview of what we considerthe traditionalnetwork_programming

model, as defined

by

the International Standards Organization. A moredetailed description is

Table 1

Layer Name Function

Layer 7 Application User interface

Layer 6 Presentation Formatconversionandinterfaceto theapplicationlayer Layer 5 Session Maintainsmultiplelow-levelconnections asa single_entity

for logicalorganizationintheapplicationlayer. Layer 4 Transport End-to-end_reliability

Layer 3 Network

Routing

Layer 2 Data-link

(MAC)

Neighbor-to-neighborcommunication

Layer 1 Physical Communicationacrossthephysical medium

(wire,

fiber optic, radio,etc.)

Ofthese, layers 5 and6are_rarelyconsidered_{separately in}sensor networks. Theoperation oftheapplicationlayer is dictated

by

thepurposeofthesensornetwork. Acommon view ofthe

sensor networkisas adatabase. Inthis case, theapplicationbecomesthe_{query interpreter}and little isrequired ofthepresentation or session

layers,

so_{theymay be practically}eliminated.

Thetransport layer is responsible_{mainly for} end-to-end reliability. This is a specialized requirementin sensor networksbecausetheirusagedeviates froman Internet-type host-to-host communication paradigm. Queriesmust reach_most,ifnotall, targetsensors and responses must be _returned, perhaps_{anonymously, to the} sink.

Assuring

that each query and each response

definitively

reachesits destination is infeasibleonthisscale. Becauseofthe_redundancyofdata andlargenumber of_{sensors, the}

functionality

ofthe transport_{layer is effectively}accomplished

by

the_{routing layer.} Queriesand responses shouldberouted with some_reliability, which_may wellbe lessthan100%. Lostmessages and messages with errors_{may be ignored.}

There have been a number of proposed optimizationsfor boththe_routingandMAC

layers,

howevermost all oftheresearchconsiders them_separately

[17] [21]

[48].

Only

_{recently have} researchers begun _{to classify the} problem of_{officially combining functions from} these two layers. So

far,

research indicates that significant _energy can besaved

by

_eliminating at least

some oftheboundaries inthetraditionalmodel[36].

Thisisnot_simply a matterof computation and encapsulationthat must occurbetweentwo layers.

Obviously,

fewer interfaces betweenlayers will reduce code complexity. In_addition, information _{traditionally} not available to the MAC or

Routing

_{layer may} allow them to

MAC and _{routing layers is} of particular interest. We will analyze the benefits ofrelating

protocolsfromthese twolayersinChapter4.

3.2

Optimized MAC-layer

design based

on

routing information

Inregardto traditional_{MAC layer protocols, TDMA}can make use ofinactivetimeslotsto

put some sensors to _sleep and reduce _energy use.

However,

were the _{sleep scheduling}

mechanism apprised of whether or notthe sensor wastobeusedto route messages

during

the

nexttime slot, itcould opt notto turnon thesensorforthat slot and

thereby

save even more

energy. This isa major_{issue for low data}rate sensor_networks,where nodesmay berequiredto

awake _{from sleep} mode farmore often than _they are _actuallyused to route data queries and

replies.

There exist several customizedMAC _{protocols, specifically designed for}sensornetworks,

such asSensor MAC

(S-MAC)

[48]. Though itshows a reduction_{in energyconsumption,}since it is still_{route-oblivious, it}wastes_energy since _sleepperiods are not coordinated with_routing

functions.

Even customized MAC protocols such as S-MAC cannot _completely account for the

additional_energyconsumeddueto thefactthatthese_{highly-optimized routing}protocolsdonot

consider the operation ofthe MAC layer. As shown in

[36]

and

[50],

_{energy-efficiency}of

networks _using these _routing protocols can be improved

by

_using a "route-aware" MAC

protocol. Thisreducestheseparationbetweentheoperation oftheMAC layerand_{routing layer.} We holdthat thisisa usefultechnique_{for reducing energy}consumption and examinetheissue in

detail.

Ourprotocol will take thisconcept one_{step further}

by

_combiningrelevantfunctionsofthree

majortraditional_networking

layers;

theMAC

layer,

the_routing

layer,

andtheapplication layer. The authors of

[50]

analyze the benefits of different layers'

common optimizations as

implemented in wireless sensornetworks. Theirresults are discussed in detail in Chapter 4.

They

arguethat cross-layerdesign isa good

idea,

_{specifically because}theoptimizations inone layer may counteract those in another. This is shown

by

the simple example of a _routing

algorithm that discovers the shortest route from sensors that sense an event to the data sink. When an event occurs, perhaps after a period of complete

inactivity

on the wireless _channel,

these multiple sensors all _{simultaneously} report the event, causing a sudden escalation of

Zhang

and

Cheng

proposetheirownMACschemeaimedtoavoid contentioninthepresence

of

bursty

data transmissionsand routes pronetohotspotsthatare commonin WSNs. Few details

aregiven,butthemaindrawbackisthat_theyspecify theuse of out-of-band signaling. S-MAC avoidsthisrequirementandthe twoideasshouldbe

functionally

compatible.

In

[33],

the authors analyze the idea ofusing MAC-layer information to create optimized

clusters, usually the _{responsibility} ofthe _{routing layer.} An interrogator in the MAC layer realizesitsrole and usesthiscriteriontobecomea clusterheadin the_{routing layer.} Simulations showthat this isafeasibleapproach_{for creating} clusters. All packet_processingoccurs inthe

MAC

layer,

which _essentially combines the _routing and MAC layers. This allows nodes to

dynamically

decide uponthenumber of neighborstocommunicate with(an importantcriterion in their_{system) based} on overheard MAC layer frames.

Logically,

one expectsthat creating

clustersbasedintheMAC layerismore efficientthan_creatinga_completelyconnectednetwork, then_eliminatingsome oftheselinks basedon a_routingprotocol. Thisassumesthattheclusters formed are comparable_{in efficiency}to thosethatwouldbe formed

by

usinga_{dedicated routing} layer clusteringalgorithm. Performanceoftheproposed protocolisshown,buttheauthorsstop

shortof_{any direct}comparisons.

4

Summary

Wireless sensor networks are a _relatively new area of research. As the _technology has

developed,

many issues have arisen that are unique to these networks. This _{is prompting}

research on efficient algorithms to organize the networks, how to spend the least amount of

energy to communicate

interesting

data,

and thedifferences ofWSNs that _may requirea new networkprogrammingmodel.

After studyingthestate-of-the-artinthesetwo areas,we estimatedthatwecould supplement

and extendthework of others with our newideas on

Timing

Control in Data Aggregationand Cross-layer design. Weproposeto leveragethecurrentresearchand analyze several potential

optimizationsinthese areas. In the

following

chapters we will examine adetailedexample of

Chapter

3

A

Novel

Timing

Control for

Optimal

Data

Aggregation

We now present the first focus of this thesis. We will analyze the issue of _timing

requirements when _{aggregating data in WSNs} and propose a protocol thatprovides increased lifetime and performance. We then propose several new _timing control algorithms that are

compatible with our protocol and attempt to

dynamically

update data aggregation _timing parametersto extend system lifetime. These performance ofthe algorithms described inthis

chapter are evaluatedlaterinthis thesis.

Chapter organization: In this _chapter, we first describe the problem to be addressed in

Section

1,

andthenin Section 2we enumerate some assumptionsnecessary forour_networking

topology setup. In Section 3 we provide detailed descriptions of multiple versions of our

aggregation _timing control scheme and in Section 4 we perform a theoretical analysis ofits potential_energysavings.

Finally,

in Section

5,

welistseveral example applications andhowour

aggregation scheme canbeappliedinnetworks with

differing

priorities.

1

Problem Statement

In orderto minimize_{energyconsumption, many networking} schemes attemptto minimize

the amount ofdatatransmitted

by

_using someform ofdata aggregation. This tradesoffdata

freshness forsavingsinenergy,becausereportsfromsensor nodesthatarrive at an_aggregating

node_{may have}tobe heldthereforsome period oftimebefore

being

reportedsothatadditional

reports_mayreachtheaggregatorfromslower nodes. This isa separateissue fromthe_processing

timeneededtoaggregatedata frommultiplesources.

For

instance,

in Figure

6,

nodeBwillreceivedatafromnodeDbefore itreceivesdata from

nodeC becausenodeCmust waittoreceivedata from bothnodesEand

F,

_assumingthatallleaf nodes reportdataand aperfectMAC layer. Inthis example,should nodeBwaittohear from nodeCorpromptly forward D'smessageassoonasit isreceived?

There are a numberofissues that affect this

decision,

such as whetherB is aware ofthe

willhavetowait_{for exactly}the

delay

incurred

by

a single

hop

_{transmission, it may}opttowait for C's response(based on knowledgeofthemaximum response latency).

Otherwise,

it may havetowait until atimerexpires. Alsonotethatifthenetwork were_any

larger,

forexampleif

nodeE hadchildrenGand

H,

we would encounter a similar_{timingproblem,}butwith adifferent solution sinceE isfartheraway fromthesink.

Sink

Level 1

Level 0

Figure6: Simple NetworkwithPotentialAggregation_TimingProblem

Inthis_thesis,we proposetouse a novel intelligenttimerand somehigh-level knowledgeof

the networktoimplementan efficient aggregation _timingcontrol scheme. Basedon a node's

position inthe network,itwillknow how

long

itcan waitforreportsfrom itschildren without

exceedingthemaximum

latency

for itsown reporttoitsparent node. Itmustbepossibleforthe sinktoindicatethroughthenetworkthehighestacceptable

latency

andthenodesthroughout the network musthavesomeideaofthenetworktopology.

We assume a _{reasonably linear relationship between}thenumber of messages and the time

period. It has beenshown

by

Yuan, Krishnamurthy,

andTripathi in

[49]

that,toapoint, thisis true. This is discussed later in this chapter. With this assumption and the knowledge listed

above, the sink and sensors will be able to calculate maximum timeouts that _satisfy the application-level_timingrequirements.

As discussed in Chapter

2,

_timing models can beclassified into _{three categories,} namely:

periodic_simple,periodicper-hop,and periodic _per-hopadjusted. Forthepurposes of_timingin

our aggregation model, we propose an efficient periodic _per-hop adjusted scheme _whereby a

node,

being

aware of its distance in hops from the sink, can reduce the timeout period proportionallybefore retransmittingtherequest.

It is not _necessary for a node to know the number oflevels below it in the tree as the

calculations arehandled

by

thesink. Thisis incontrasttoschemes wherethesink mustdiscover the network_topology, thenpropagate this informationtoall nodesin thenetwork. Each node

specifies too short of anaggregationperiod aggregators somewhere above the leafnodes will

timeout and return alimitednumber of responses.

Only

localsynchronizationisrequired,asthe

wireless propagationtime isassumedto benegligible andis accountedfor

by

the

dynamically

updated globaltimeout.

Thescheme proposedinthischapter canbeachieved_{solely in}the_{routing layer}anddoesnot

interfere with _any potential _sleepschedule enforced

by

theMAC

layer,

which is discussed in

Chapter 4. In thenext section we listanddiscuss some assumptions relevanttotourproposed

aggregation

timing

mechanisms.

2

Assumptions

2.1

Topology

assumption:

Cluster-Tree

architecture

IntermsofWSNtopologiesforthepurpose of optimaldata_aggregation,we proposetomake

use ofbothmethods(i.e.atree_consistingofcluster-heads)

[20]

sothatgroups of sensor nodes

will combinetheirreports atthelowest

level,

thenreports_maycontinuetobeaggregated as_they

pass_upthe aggregationtree[17]. Wearguethat thisisapromising routing implementation in

termsof_scalabilityand overall _energyefficiency. Sinceeach cluster-headfirstperforms local

aggregationin itsclusterbefore

forwarding

datatothenextcluster-head, this thesiswillfocuson

the data aggregation issue in the entire tree(i.e. between cluster-heads instead ofinside each

cluster).

Consider Figure 7. In this

figure,

each circle can represent a sensor node. In the

figure,

groups oftwoorthreesensor nodes atlevel iare groupedtogetherunderthecontrol of another

node atleveli+\. Nodesatlevel/areleaves inthe treeandfunction onlyas sensors. Thevast

majorityof nodesinthenetworkfall intothiscategory. Nodesatlevelj+1 and

higher,

uptothe

root ofthetree,wouldbeconsidered clusterheads. Though_theyare still sensor nodes and_may

have

individually

senseddatatoreturn_{to the sink,}

they

arefewenoughinnumberthatour main

concern with them is how much data _they forward.

Alternatively,

thecircles at level 0could

easily beentireclusters. Membersoftheseclusters communicateto therest ofthenetwork via

the organization will be the same. In

fact,

a

hierarchy

of some sort_{is practically} guaranteed

when_{clustering[13].}

In this _thesis, we usethe relative

terminology

interchangeably;

_{each circle in thediagram}

may bethoughtof as a_node,a_cluster,or a clusterhead andthecircle atthehighest levelwillbe

referredtoas eithertheclusterhead orthe_sink,

depending

onthecontext.

Level_2,

Sink/Ctusterhead

Level 1, Clusterhead/Node

Level0, Ouster/Leaf Node

Figure7: SimpleTreeNetwork

OnthedeterminationofCluster Size:

Givenafieldof size m

by

nand

disregarding

theeffect of cluster_overlap,onehasa choice as

tohow largetomaketheclusters. Weconsidertheradius of a clustertober,ineitherdistance

orthenumber of_{hops2. For simplicity,}rshould_exactlydividemandn.

Thetotalarea covered

by

theclustersisthesum oftheareas covered

by

each oftheclusters.

Thetotalnumber of clustersisthenumber of clustersthatfitintothefield

horizontally

times the

number of clusters that fit into the field vertically. These qualities are expressed

by

the

following

formula:

x r r cov erage=

which simplifiesto

cov erage=

(mx

n)n,

showingthatrdoesnotaffectthe totalradio coverage ofthenetwork3.

Though mathematicallythecluster radiusdoesnot affect coverage ofthe

field,

thisdoesnot

representthe_{energy efficiency}ofthenetwork. Withalargercluster

diameter,

clusterheads will have to transmit fartherto reach each other, but the treeof clusterheads will be simpler. A

2

Inan_evenlydistributed fieldofsensors, thesewillbeproportionalunits ofmeasure. 3

Thisapproximationholds ifsensors'

simplertree leads to more energy-efficient_topology maintenance at this level. Also with an

increasing

r,maintenance and communication costsforeach cluster willbe increasing.

There isabalancetobestruckbetweencluster size andtreesize. Thelargertheclustersare, the smallerthe tree of clusterheads _{may be} and vice versa. Theradius of a cluster, r, is the

independent variable in this case. The dependent variable is the _efficiency ofthe network topology. Thiscanbevisualized

by

the

following

figure:

Cluster Sizevs.Network_Complexity

| *

TreeComplexity

--ClusterComplexity|

Figure8: Tree Sizevs.ClusterComplexity

Theoptimum_{balance may be} calculated offline and isnotpartof our protocol.

However,

thisplotdoesshowthat thereexists some optimal point. Asuboptimal choiceforrwill resultin

some amount of

inefficiency,

soit isnot enoughtousesolelyatreetopologyor a single cluster.

Asmentioned, we consider anetwork_consistingofa treeofclusters. Witha given number

ofnodes, largerclusters allow asimplertreeandvice versa.

Assuming

clusters arecircular, the

numberof nodesina cluster andthe complexityof eachincreasesexponentiallywithr. Thetree

[image:28.546.104.471.181.411.2]

showthe_complexityofcommunicationwithinthe tree to change

linearly

with r[13]. This isa

simplified_{example, but}showsthat thereissomebalanceof organizational_{efficiency between}a

network with a single cluster and a network inwhich each sensor nodeis a nodeinatree. The

optimumbalancemay becalculated offline andisnot part of our protocol. Asuboptimal choice

forrwill resultinsome amount of

inefficiency,

soit isnot enoughtouse_solelyatree_topology

or a single cluster.

2.2 Other Assumptions:

Animportantnote aboutWSNs ingeneralisthat_theyaredata-centric [16]. Thismeansthat

dataisrequested and returnedto thesinkbasedonitsrelevance_{to the query,}ratherthanbecause

it was requested from a specific node. For our aggregation scheme we assume queries are

definitive fornameddata. This isa minor_assumption,and our protocol_reallyonlyrequires that

one_querycanbe differentiated fromanother,whichisa reasonable requirement.

Thismeans thatend-to-end _addressingwill notbe_{used, though}

hop-to-hop

_addressingwill

be. Even in WSNsthatuse_{clustering, thereis usually}a concept of_{local addressing (within}the

cluster)

[25]

[26]. Messagesarebroadcasttoall other nodes within radio communication range.

The receiving node mustbe able to recognizeat least the typeof message in orderto decide

whethertointerpretthedata_within,forwardthe_message,orignorethemessage.

A minor distinction to be made is the communication model. Commensurate with the

wireless nature of

WSNs,

messagesare assumedtobe broadcasttoall nodes withinradio range.

Thisnecessitatestheuse of either

hop-to-hop

addressingor messageIDstoavoidloops. Despite

thephysical

implementations,

_conceptually messages are unicast from node to node in that a

parent can send a messagetoitschild and viceversa,with other nodesthatoverhearthemessage

discarding

it. Some WSNschemes_relyon _gossiping topropagate messages[16]. This is not

requiredforoperation of_anyoftheaggregationschemesdiscussed inthis thesis.

The final assumption, which is necessary to this

discussion,

is that meaningful data

aggregation mustbeabletobeperformed. That

is,

datagenerated

by

thesensornodes mustbe

able tobe combined in some _waythat shortens the total message,while _retainingthe original

information. The specific aggregation function is beyond the scope ofthis _thesis, but this

A query that asks forthe maximum value sensed is an ideal candidate for aggregation

becauseanynumber of messages can_{be easily}combinedintoa single value attheaggregator. A

query fortheaverage over a_{field is slightly}more complicated inthat thenumberof responses

mustbe communicatedtocalculate_{the average,} butthefinal message will stillbeshorterthan

multipleindividualresponses.

Finally,

a_{query for}all values sensed wouldnotbea candidatefor

aggregationbecauseeach value mustbeappendedtothemessage andthefinalmessage willbe

proportionalinlengthto thenumber of sensorsresponding.

3

Proposed

Time

Synchronization

Algorithm

An important research topic _necessary to the idea ofdata aggregation is _timing control.

Figure 7 represents a simple network organizedintoa tree topology. Ina realistic WSN there

would be many more nodes and _potentially a much deepertree. With deeper_{trees, timing}is

even moreimportant [26].

Aggregationtradesoff

latency

_{for energy}savings. _{Because aggregating}sensorshavetowait

for data fromtheirchildrento_{arrive, there}will_{necessarily be}someincrease inthe timeittakes

them to respondto queries. As illustrated in Figure

7,

a node atlevel 2 would need towait

longerthannodes atlevel 1 inordertosend aggregateddatatoitsparent node. There isadesign

tradeoffin the maximum amount oftime to wait. Ifan _aggregating node waits too

long,

the

resultsofthe_{query may}notbetime-relevant. _{This is especially}trueinsituations inwhichthe

datasink_maywantinitialresultsimmediately.

Latency

tradeoffsare_{application-specific,}sowe

assumethat theapplication

(running

onthe_sink)can choosetheoptimaltargetlatency.

Inmost aggregation_schemes, latereports are_{simply discarded}

[26]

[49]. Wefeel that this

decisionshould be upto theapplication.

Depending

ontherequirements oftheapplication, it

maymake more sensetoacceptlateresponses. Notethat thisisa separate_{issue from using}the

number oflateresponses tocalculatetheaggregation period. _{The ability for}theapplicationto

dictatetheaggregation action canbeadded

by

a single

flag

_{accompanying data}queries. This is

discussed in detail in Section 3.4.

Amajorconclusiondrawn

by

theauthorsof

[49]

isthat theaggregationtree_{(not necessarily}

thecommunication_tree)plays thelargestrole in the_efficiencyoftheaggregation. While it is

true that theaggregationtopologyis

important,

it isnot enoughtodependon anidealtree. Steps

to performance. Ourprotocol aims to do this

by informing

interested nodes ofthe

(limited)

network

topology

inthemostenergy-efficientmanner.

Using

this

information,

thedatasink can

makeintelligent decisionsaboutthe tradeoffsbetweendata

latency

andpotential_energysavings.

Our proposed aggregation

timing

control protocol, as _{do many} others, makes use of a

separate_setupphasetodistribute parameters_{necessary for}aggregation. This istypical _of, and

compatiblewith,mostdynamic _routingandMACprotocols. _{The setup}phasetakessomefinite

amount oftime and is followed

by

a much longer data collection period. These phases are

scheduledtooccur periodically. The

frequency

with which _theyoccuris discussed in Section

3.2. The

following

figureshowsthegeneral scaleinwhich our protocol will operate.

Control Parameter SetupPhase

3=

]

Repeat

DataCollection Phase

Figure 9: _Setupvs.DataCollectionPhases

3.1.1

Setup

phase

1. Sink broadcasts "depth

request."

Therequestispropagateddownthrough thenetwork

similarlytoa simpledataquery.

2. Messagereachesbottomandisreturnedbackup. Eachnode returns withits

hop

count,

starting from leavesatlevel 0. Theparents ofthesenodes realizethat_theyare atlevel 1and

passthis_{information back up} thetree. Thiscontinues untilthesink retrievesinformation

abouttheentiretree. Eachnode propagatesthedepthrequesttoitschildren and each node

returns an answertoitsparent,sothisisaccomplished with_complexity<9(depth).

Inordertosave_energy

during

this_setupphase,somelevelof aggregation_{may be}performed

atthisstep. Possibilitiesvaryfrom

transmitting

informationthat_{completely describes}the

networkto_{communicating only}themaximumdepthofthe tree. Theseoptions arediscussed

more

fully

in Section 3.4.

3.

Finally,

thesinkwillcalculate andtransmitan appropriate valuefor T basedonthedepthof

the_tree,themaximum

latency,

andtheoptimal number ofresponses,whichisassumedtobe

After performing this_exchange,each_aggregatingnode,

including

the_sink, should haveall

knowledge_{necessary for}ittocalculate an appropriatetimeoutperiod.

Depending

onthelevelof

aggregation performed_{in step}

2,

nodes_{may be}awareofhow balancedor unbalancedthetree

is,

or parent nodes_{may be}aware oftheentire aggregationtreebelowtheirpositions.

3.1.2

Data

collection phase

1. Thesinktransmitsa new

querywith an updatedtimeoutperiod. This doesnot require_any

timesynchronizationbecausethesensors and aggregatorsjustmaintaintheprevious value

until notified. Aggregatorsthatreceivethe_queryreduceituniformlybefore propagatingthe

queryasdescribed inSection3.5.

2. Eachaggregator replies withthenumber of replies received

(average,

per_query)andits

depth inthe tree. Notethatan aggregatorthatistheparentof anotheraggregatingnode will

sumitstotalnumber received

(Nrec)

withitschild's report. _{Aggregators may optionally}

reportthenumber and

timing

oflatereports. Weterm these

Niate

and

Tiate

anddiscussthem

inthe

following

section.

3. Thesink will send out an updated

Tn+i

toallaggregators,basedontheFSM. cisa parameter

chosen

by

theapplicationinthesinkthatrelates

Nop,

toTopt.

3.1.3 Optimization

In order to get the most accurate view ofthe network, aggregators _may also report the

number and/ortimeoflatereports. Thiswouldallowthe sink,ifpowerfulenough,tochoose an

appropriate aggregation period even moreintelligently.

For example, the "maximum latency" ofthe application _{may be} provided _along with a

flexibility

factor,

flex and its associated weight,flexing*. If the sink does not receive a

satisfactory number of responses, it may calculate the benefit of

increasing

the aggregation

period.

Disregarding

network

fluctuations,

thiscanbe done

by

simply countingthenumber of

nodes whoseadditionallatenesseswere reported tobe lessthantheminimum incrementofthe

aggregationperiod,T.

This number can_{easily be}weighted and compared to our application-level_parameters,flex

3.2

Control

Parameter

Refresh Rate

There isa concept of adata-collectionround. Thisroundmay includemultiple requests for

similar ordissimilar

data,

but uses thesame parameters fortiming, etc.forthedurationofthe

round. At the end of a _{round, the} performance experienced is evaluated and parameters are

chosenforthenext round.

As mentioned, the Masternodes, aggregators, or"clusterheads" are

likely

_ordinary sensor

nodes, tasked with additional responsibilities. For_{this reason,}theMaster_{(or clusterhead)} must

berotated_periodicallytoprovide a_reasonablyeven loadon each sensor node

[17]

[40]. This

may be done after each _round, or after a specific numberof rounds. This leads to another

tradeoff _{in energy} efficiency. If the network is reconfigured

frequently,

it will lead to

unnecessaryoverheadintheformof control messages and calculations. Attheother_extreme,if

the network

topology

is chosen once and remains static, then a single node will bear

responsibility foran unfair amount oftimeand will

likely

die before itscluster members. This

alsodecreasestheoverall usefulness ofthe network,since whenthisclusterhead

dies,

_{it may}take

irretrievable datawithit.

It iscommon practicetouse a_setupphase where we communicate allthe_necessarycontrol

packetsforthecreation ofthenetwork

[17]

[39]. Atthispoint we couldconceivably informthe

aggregating nodes oftheirdepth inthe tree. This is the_only parameter_{necessary for}abasic

aggregation_timingscheme. Anotheroptionistoembed certain control informationwithin each

datapackettransferred. Thisprovides more_timelyfeedbackonthestate ofthe system,butadds

overheadintheformofadditionaldata foreachtransmission.

Wefeelthatbothofthesemethods shouldbecombined. As_{mentioned,it is necessary}touse

some sort of_setupphasetocommunicatethe_topologyoftheaggregation network. Thisis

likely

part ofthenetworksetupphase_anyway,andfor simplicitythisoverhead will notbeconsidered

in detail inthisthesis.

The onlyadditional informationthat_mayneedtobepassedback upthetree

during

thedata

collection phaseisoptionalinformationsuch asthenumber of messages missed

by

an aggregator

duetoa shortaggregationperiod,or perhapsjustwhether or not_anymessages were receivedlate

fromthe lastquery. Thisrequires_onlyafewadditionalbitsas part ofthedatamessagepackets,

3.3

Finite

State

Machine

Implementation

of

Algorithm

Several finite state machines were developed forevaluation. Each aimed to maintain the

number of messages received as close as possible to the optimal number (determined

by

the

application). _{In addition, it}wasdesiredtoreachthispointas_quicklyas possible. The

following

are variables usedinthesestate machines.

N0[),

Optimalnumber of_{responses; determined}

by

theapplication.

Nrec

Numberof responsesreceived; tallied

by

theaggregators and reportedto thesink

T Maximumaggregation_period,distributed

by

thesink L+

Maximum

latency

(application

level)

Tj

Maximumaggregation period(fora node atlevel

i)

n Theround;T iscalculated onceforeachround,whichisassumedtobe

long

enoughto

create aheuristicwithout

becoming

stale.

Topt

Aggregationperiodthatsatisfies

Nrec

=

Nopt

c Aparameter chosen

by

theapplicationinthesinkthatrelatesNopttoTopt.

Nia,c

Numberoflatepackets received

by

aggregators

TiaK

Timeunits while_{waiting for late}packets

D Depthofthe tree

K Levelof nodeiinthe tree.

? Differencebetween Tateachlevelofthe tree

If D is

known,

the sink can derivea maximum

T;

for each level inthe _tree, based onthe

maximum

latency (L+)

oftheapplication:

7)

=V-KA

Equation1

Since the nodes know their own level in the _{tree, they} can

individually

calculate their

aggregation periods, using the above formulaand ? ifavailable. Our protocol is capable of

distributing

this deltaas partofthecontrol message. Itwasfound

during

simulationsthat this

parametercouldbeeliminated. Detailscanbe found in Chapter 5. Thoughthere_{may be}some

advantage_usingavariable

?,

thereisatradeoffintheamount of controldatatransmitted. This

issue isanalyzedinmoredetail in Section 3.5. Theempirical performance advantages of_usinga

deltaareleft for futureexperimentation.

Thesimplestfinitestate machineshownbelowwas

initially

derived fromtheapproximation

that the number of responses received

by

the sink is

directly

proportional to the aggregation

period. Thisisa reasonableinitialassumption_since,

logically,

alongerperiod will allow more

for _choosing an aggregation _period, and the amount of control data necessary.

Minimizing

complexityshouldbetheaim of_{any algorithm, especially}when_energyconsumptionisa major

concern.

This initial assumption _may prove to be naive,

depending

on MAC and physical layer

performance and even the traffic model, which is network- _{and application-specific. Ifthisis}

found tobethecase

(experimentally),

our protocol can be easily modified to allow optimized

performance. Evenwith anexampletrafficmodel as shownin

[38],

our protocol should perform

well without modification as it still

dynamically

changes the aggregation period. The most

notable_{shortcoming is}thattheperiod willbereduced

linearly

oncetheoptimal pointisreached.

Thiscanbeeasilymodifiedifthe traffic ismeasuredas shownin

[38],

insteadofthemodel we

basedour protocol on.

Thestate machinein Figure 10 isthesimplest. Ateach evaluation oftheaggregation_period,

theoptimal number of messages received iscomparedto theactual number received. Ifthere

weretoofewmessagesreceived, theaggregation periodisincreased

by

one atomic unit. Ifmore

messages were received than were needed, the aggregation period is

linearly

decreased in a

similar manner. The amount of increase and decrease can be varied _statically to suit the

application and network characteristics.

Thestate machinein Figure 1 1 requires amore complicatedformulatocalculateTn+i. Inthe

figure,

there are additional parameters used to calculate T. This figure also introduces the

concept of an acceptabledeviation. Thiscanlimitthenumberof_unnecessarychangesin Tand

theattendantbroadcasts.

Thefinalstatemachine,shownin Figure

12,

doesnot make use ofthisacceptabledeviation

parameter, but abstracts the multiplicand responsible for

increasing

and

decreasing

the

aggregation periodinordertoreducethetimeneededtoprovideNopl.

Thetheoreticalperformance ofthesevarious state machi