kais t distributed cross-layer scheduling for in-network sensor query processing percom 2006 2006....

KAIST

Distributed cross-layer scheduling for Distributed cross-layer scheduling for

In-network sensor query processingIn-network sensor query processing

PERCOM 2006

2006. 11. 23(THU)

Lee Cheol-Ki

Network & Security Lab.

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ContentsContents

Introduction

Related work

System overview

Schedule construction

Schedule execution

Evaluation

Conclusion and future work

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Introduction(1/2)Introduction(1/2)

In-network sensor query processing systemsData acquisitional applications of wireless sensor networks

Inject queries into the network

The networked sensor nodes work together to process the queries

Send back the query results

Critical issue : To reduce power consumption

Major sources of energy waste

Idle listening, over hearing, collision and control packet overhead

Immediate solution

To make the nodes sleep as much as possible

Focus on tree networks (similar to the existing schemes)

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Introduction(2/2)Introduction(2/2)

Process of systemsA node first checks Applicable Transmission Slots

Sends it to parent

The parent sends the assigned transmission timing

The node arranges it about tasks in all layers

Execution the schedules

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Related work(1/2)Related work(1/2)

Scheduling has been applied to different layers

TinyDB in query layer

S-MAC in MAC layer

Centralized approach (Florens)

Sink allocate transmission timing to all other nodes in WSN

Sink know the network topology → difficult, high cost

Destributed approach

Flexible Power Scheduling (FPS)

Distributed on-demand power-management protocol for tree networks

Reducing the collision between siblings

But, not reducing the collision between neighbors that are not siblings

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Related work(2/2)Related work(2/2)

Sichitiu

The source node broadcast a special route setup packet with neighbors

Temporary schedule → permanent schedule (after arriving at sink node)

Frequent collision → dead nodes → inaccurate query result

In this paperConsidering all tasks query injecting, computation, aggregation

Consecutive sleeping and transmission timing to nodes

All layers of a query processing system are involved

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System overviewSystem overview

In-network sensor queryprocessing system

Construct routing tree

Sink inject a query

Nodes construct schedules

Sink broadcasts synch-signalBottom-up report to sink

Run following the schedules

Scheduling moduleSchedule construction

Time synchronization

Schedule execution<Architecture of scheduling module>

<A typical WSN setup>

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Schedule construction(1/4)Schedule construction(1/4)

Schedule construction module

Constraints for schedule construction

<Example of constraint >ⅲ

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ConstraintsFocus on collisions at the parent node of a sender

Complete collision-free scheduling → high overhead, impractical

In practical

D and E sends packet simultaneously Collision occurs in B⇒

But, don’t affect to the query results

<An example of the collision>

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Notation

Variables

CTS (Children Transmission Slots), ATS (Applicable Transmission Slots),TSI (Transmission Slot Information), CATS

Type of time slots

PL/R(processing-listening/receiving), Q/M(Query injection/maintenance),Sleeping slots, transmission slots

Procedures

A node, to determine ATS (DetermineApplicableTransmissionSlots)

Sends the packet to the parent

Parent node, CTS for children (AllocateChildrenTransmissionSlots)

A node receive TSI from the parent node

Sends ACK

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Complete scheduleConstructSchedule arrangesthe time slots for PL/R, Q/M

<Example of complete schedules>

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Schedule executionSchedule execution

Execution involves the timing control of all layersMAC layer checks whether it is time for transmission

Earlier than allocated transmission time

Copy the messages to a memory buffer and wait

Timer setup (automatically transmission)

Waiting time should be shorter than the interval of 2 transmissions

Routing layer

Control the timing of transmission for the route maintenance messages

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Evaluation(1/4)Evaluation(1/4)

Scheme comparison (DCS, FPS, SS)The experimental setup

The same experimental setup as that Sichitiu used

100 nodes in an 80m*80m rectangular area, Trans-range : 25m

Query processing performanceThe experimental setup

10 Crossbow MICA2 motes, optimized TinyDB, original TinyDB

HP-4156 oscilloscope for measuring the power consumption

VMNet simulator

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Scheme comparisonScheduling overhead

Time of constructing a schedule

Schedule comparison

Number of dead node

Non-scheduled node, conflict-scheduled node

AFS (Average frequency of switching between an active slot and a sleeping slot)

<Scheduling overhead>

<Number of dead nodes> <Average frequency of switching>

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Query processing performanceSchedule for in-network aggregation

VMN : Emulated network in VMNet

Query 1

SELECT avg(light) FROM sensors

Sample intervals

2, 10, 60 seconds

Comparison optimized TinyDB with original TinyDB

42%, 67%, 75% at sample interval of 2s, 10s and 60s

<The VMN topology with schedules>

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Power consumption improvement

Difference between measurement in VMNet and that in the real WSN within ±15%

Difference in the topologies

Measurement errors of the oscilloscope

<Power consumption improvement>

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Conclusion and future workConclusion and future work

Propose scheduling scheme for Power efficiencyReduce the number of dead nodes

Reduce Switching frequency

In-efficient to reconstruct a schedule whenA change in the network topology

A new query arrives

kais t distributed cross-layer scheduling for in-network sensor query processing percom 2006 2006....

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