Ad Hoc Now2008 Probabilistic Query Dissemination

Zinaida Benenson Felix FreilingMarkus Bestehorn Marek Jawurek

Query Dissemination with Predictable

Erik Buchmann

Query Dissemination with PredictableReachability and Energy Usage in SensorNetworksAdHoc-Now 2008, Sophia Antipolis

www.kit.edu

Introduction – Sensor Networks

A sensor network consists multiple of sensor nodes, e.g.IntroductionProblem Desc.IdIdea

ReachabilityDirectI di t

MicaZSun SPOT

Indirect

Topology Information

Sensor NodesBattery-poweredEquipped with sensor hardware

EvaluationSetupSimulationBreak Even Equipped with sensor hardware

Limited computing resourcesWireless communication

Break-EvenDeployment

Conclusion

Q&A

Slide 2Markus Bestehorn

Query Processing in WSN

Generic query processing in sensor networks approach:1. Disseminate query through base station

IntroductionProblem Desc.Id q y g

SELECT MAX(temp) FROM sensors …

2. Measure data using sensing hardware3 Process & route query results back to base station

Idea

ReachabilityDirectI di t 3. Process & route query results back to base station

Optimization Goal: Reduce energy consumption!

Indirect


Sending/Receiving data most expensive!

215°C

17°C

EvaluationSetupSimulationBreak Even 2

3

4 6

17°C21°C

QQQ

QQQ


Conclusion3

1 5 Basestation20°C

19°C

22°C

QQQ

Q22°CQ&A


Challenges for Query Dissemination

Unnecessary rebroadcasts must be avoidedNodes should receive query only once

IntroductionProblem Desc.Id q y yIdea


2 4 6Q Q

?

Existing approaches

Indirect


315Q

Q

Existing approachesTopology-Based: Determine rebroadcasting nodes using accurate local topology information

EvaluationSetupSimulationBreak Even 2-Hop topology information is very costly

Optimal Broadcast Dominating Set Problem NP-completeProbabilistic: Nodes rebroadcast with probability p


ConclusionHigh p high energy consumptionLow p not all nodes reachedHow to set p?

Q&A


Idea & Agenda

General idea:Acquire basic topology information

IntroductionProblem Desc.Id q p gy

does not consume as much energyUse probabilistic approach to disseminate querySet rebroadcast probability based on basic topology information

Idea

ReachabilityDirectI di t Set rebroadcast probability based on basic topology information

Agenda:

Indirect


Prediction frameworkHow to predict reachability for a given rebroadcast probability p?How to set p based on prediction to reach all nodes?

EvaluationSetupSimulationBreak Even How to set p based on prediction to reach all nodes?

Topology DiscoveryPossibilities to aquire required topology information?

Extensive evaluation


ConclusionExtensive evaluation

Simulation and real deployment resultsExplore tradeoff reachability vs. energy consumptionA f th P di ti F k?

Q&A


Accuracy of the Prediction Framework?

Hop Set Modell (1)

Task: Predict the number of reached nodes givenTopology information

IntroductionProblem Desc.Id p gy

Rebroadcast probability pHop Set: Hop Set H[i] contains all nodes that can be reached by the base station via i hops

Idea

ReachabilityDirectI di t reached by the base station via i hopsIndirect

Topology Information H[1]H[2]H[3] H[0]

EvaluationSetupSimulationBreak Even

2 4 6


Conclusion31 5

Q&A


Hop Set Modell (2)

Possibilities to reach a node via broadcastDirect: Message is sent from node in H[i-1] to node in H[i]

IntroductionProblem Desc.Id g [ ] [ ]

Indirect: Message is sent from node in H[i] to node in H[i]Backwards: Node in H[j] with j > i forwards message to node in H[i] Simplification: not considered

Idea

ReachabilityDirectI di t H[i] Simplification: not consideredIndirect


EvaluationSetupSimulationBreak Even 4

H[1]H[2]H[3] H[0]

QQQBreak-EvenDeployment

Conclusion

24

6Q

Q

Q

Q

Q

Q&A 31 5


Reachability Prediction

R(h,p) := number of reached nodes in Hop Set h with rebroadcast probability p

IntroductionProblem Desc.Id

p y pR(0,p) = 1 base station is always „reached“R(1,p) = |H[1]|

base station always broadcasts H[1]

Idea

ReachabilityDirectI di t base station always broadcasts

Hop Set H[1] always reached

Nodes in s bseq ent Hop Sets are reached

H[1]Indirect


Nodes in subsequent Hop Sets are reachedDirectly Direct(h,p)Example: Direct(2,p)=4Indirectly Indirect(h p) H[1]

EvaluationSetupSimulationBreak Even Indirectly Indirect(h,p)

Example: Indirect(2,p)=2H[1]

H[2]Break-EvenDeployment

ConclusionR(h,p) := Direct(h,p) + Indirect(h,p) with h > 1Q&A


Direct Reachability Prediction

Basic Idea to compute Direct(h,p)Possible rebroadcasters |H[h-1]| nodes

H[i]H[i-1]IntroductionProblem Desc.Id | [ ]|

Potential Rebroadcasters R(h-1,p) nodesRebroadcasters R(h-1,p)·p nodes

|H[h-1]|R(h 1 )

Idea

ReachabilityDirectI di t |H[h 1]|R(h-1,p)

R(h-1,p) ·p

Indirect


P(„Node in H[h] directly reached“) can be computed

EvaluationSetupSimulationBreak Even Avg. Number of connections from

H[i] to H[i-1] Connectivity[h]Detailed description in the paper


Conclusion p p p

Direct(h,p) = P(reached directly)·s[h]

pRebroadcast

Probability

H[i]Nodes reached

in i Hops

Q&A


p

Indirect Rechability Prediction

Idea to compute Indirect(h,p):Potential Rebroadcasters Direct(h,p)

IntroductionProblem Desc.Id ( ,p)

Rebroadcasters Direct(h,p)·pAverage Number of connections within a Hop set

Interconnectivity[h]

Idea

ReachabilityDirectI di t Interconnectivity[h]

Indirect(h,p)=Direct(h,p)·p·Interconnectivity[h]Indirect

Topology Information H[1]H[2] H[0]Evaluation

SetupSimulationBreak Even

4 6

Implicit Assumption:


Conclusion3 5

Implicit Assumption:Reached nodes distributed evenly within hop sets

Q&A pRebroadcast

Probability

H[i]Nodes reached

in i Hops


p

Reachability Prediction (3)

R(h,p) computes reached nodes in Hop Set h with rebroadcast probability p


p y pComputing total reachability for given p:

( )( )][min)( hHphRpR ∑=Idea


Minimum required because Direct(h p) + Indirect(h p) > H[h]

( )( )][,,min)( hHphRpRh∑=Indirect


Minimum required because Direct(h,p) + Indirect(h,p) > H[h]possibleEvaluation

SetupSimulationBreak Even

Also available:Number of sent messages / rebroadcasting nodes


ConclusionNumber of sent messages / rebroadcasting nodesNumber of received messagesAllows estimation of energy consumption!

Q&A



Required Topology Information for Reachability PredictionSet Size: Number of Nodes in each Hop Set H[h]

IntroductionProblem Desc.Id p [ ]

Connectivity: Avg. Number of connections a node in H[h] has to nodes in H[h-1]Interconnectivity: Avg Number of connections a node in H[h]

Idea

ReachabilityDirectI di t Interconnectivity: Avg. Number of connections a node in H[h]

has to other nodes in H[h]Example:

Indirect


H[i]H[i-1] Set size Connectivity

EvaluationSetupSimulationBreak Even … i-1 i …

… 2 3 …… i-1 i …… 1.5 2 …

I t ti it


ConclusionInterconnectivity

… i-1 i …… 0 4/3 …

Q&A


… 0 4/3 …

Acquiring Topology Information

Several options to get required topology information:Echo Algorithm

IntroductionProblem Desc.Id g

Expansion Wave: Explore network by initiating a flooding at the base stationContraction Wave: Aggregate topology information towards base

Idea


gg g p gystation

Drawback: Energy consumption, ScalabilityGossiping: Nodes attach routing information to messages

Indirect

Topology Information Gossiping: Nodes attach routing information to messages

Advantage: No extra messagesDrawback: Routing information disperses slowly

Routing Protocol Extraction: Extract topology information

EvaluationSetupSimulationBreak Even Routing Protocol Extraction: Extract topology information

from data structures of routing protocolDrawback: Only possible for some protocols (AODV)

N t


ConclusionNote:

Even for Echo Algorithm Prediction pays off after a few query disseminations!

Q&A


q y

Evaluation - Setup

Network: 125 to 425 nodesNode Degree: 4 – 16

IntroductionProblem Desc.Id g

Different Topology Types used, e.g.Uniform: Nodes are placed uniformly around basestationG i G i di ib i f d d b i

Idea

ReachabilityDirectI di t Gaussian: Gaussian distribution of nodes around basestation

100 topologies per topology type, 40 queries per topologyEnergy prediction based values measured on MicaZ

Indirect

Topology Information Energy prediction based values measured on MicaZ

Criteria for success:

EvaluationSetupSimulationBreak Even Accurate Prediction for Reachability and Energy

Optimization of probabilistic rebroadcast parameter pto reach ALL nodes with query


Conclusionto reach ALL nodes with querywithout rebroadcasting at each node

Exploration of rebroadcast probability – reachability tradeoff

Q&A


Evaluation – Simulation Results

Result for node degree 16, 425 nodesIntroductionProblem Desc.Id Uniform GaussianIdea


p0

Indirect



Findings:


Conclusion

Reachability & energy prediction accurateFor most experiments, there exists a p0<1: Increasing p beyond p0 does not pay off regarding reachability!

Q&A


p0 p y g g yenergy savings without reducing reachability

Break Even Point

Exemplary computation:Uniform topology

IntroductionProblem Desc.Id p gy

425 nodes, node degree 16Assuming

T l di i h E h Al i h

Idea

ReachabilityDirectI di t Topology discovery using the Echo Algorithm

Energy consumption values measured on MicaZIndirect


Topology Discovery consumes 722 mAsQuery dissemination with simple flooding (p=1) consumes 370 A

EvaluationSetupSimulationBreak Even 370 mAs

Using prediction framework for 99% reachabilityp=0.6 220 mAs


Conclusion pResult:Topology Discovery pays off after 5 queries!

Q&A


Evaluation – SPOT Deployment

17 SPOTs + Basestation deployed10 Queries were disseminated into the

IntroductionProblem Desc.Id 10 Queries were disseminated into the

network using Simple flooding (p=1)P b bili ti fl di

Idea

ReachabilityDirectI di t Probabilistic flooding

Prediction algorithm was used to reachAll nodes

Indirect


At lowest possible rebroadcast prob. pResult:

Broadcast Reached Sent Msg Received

EvaluationSetupSimulationBreak Even Broadcast

AlgorithmReached

NodesSent Msg. Received

Msg.Simple 16.3 16.3 63.8Probabilistic 15 4 10 2 34


Conclusion

Probabilistic Rebroadcast Optimization~30% less sent messages

Probabilistic 15.4 10.2 34Q&A


almost 50% less received messages

Summary

Explored relations betweenReachability

IntroductionProblem Desc.Id y

Energy consumption for query disseminationEnergy spent to acquire topology information

I t d d l ti l f k

Idea

ReachabilityDirectI di t Introduced analytical framework

Determines p0<1 for probabilistic broadcasting to reach all nodes

Indirect


Allows predictions regarding sent / received messages Energy consumption

EvaluationSetupSimulationBreak Even gy p

Energy spent for topology information pays off after a few (5) query disseminations

Even if echo algorithm is used!


ConclusionEven if echo algorithm is used!

Evaluation using Simulation & real Sensor networkQ&A


Outlook

Integrate „backwards“ reachability intoframework


More topology information required?Payoff?

Relation between query dissemination and query result

Idea

ReachabilityDirectI di t Relation between query dissemination and query result

accuracyIndirect


p0

~100% reachability100% accuracy

~100% reachability100% accuracy

<100% reachability? accuracy

<100% reachability? accuracy


Dynamic usage of different broadcast algorithmsProbabilistic approach good for dense networks


ConclusionProbabilistic approach good for dense networksSwitch to other broadcast algorithms in less populated areas of the network?

Q&A


Thank you for your attention!


Questions?Idea

ReachabilityDirectI di tIndirect


EvaluationSetupSimulationBreak EvenBreak-EvenDeployment

Conclusion

Q&A


Documents

Ad Hoc Now2008 Probabilistic Query Dissemination