december 3, 2009 yu (jason) gu @ rtss ‘09 spatiotemporal delay control for low-duty-cycle sensor...

December 3, 2009 Yu (Jason) Gu @ RTSS ‘09

Spatiotemporal Delay Control for Low-Duty-Cycle Sensor Networks

Yu (Jason) Gu1, Tian He1, Mingen Lin2 and Jinhui Xu2

Department of Computer Science and Engineering

1University of Minnesota, Twin Cities

2State University of New York at Buffalo


22

Motivation

TargetTracking

BorderControl

InfrastructureProtection

TrafficControl

AssistedLiving Disaster

Response

Real-time data delivery

Long-term operation (Low-Duty-Cycle)

+How to achieve delay requirements in low-power networks ?


33

Design Objectives

• Real-time guarantee of communication delay for long-term low-duty-cycle sensor network applications– Can be applied to generic low-duty-cycle

network model– Minimum energy/system cost


44

Related Works• Real-Time Communication

– Traffic Regulation• Vasudevan et al., SenSys’03; He et

al. (AIDA), TECS’04, Karenos et al., RTSS’06

– Feedback-based • Lu et al. (RAP), RTAS’02; He et al.

(SPEED), ICDCS’03; Felemban et al. (MMSPEED), INFOCOM’05

– Traffic Scheduling• Carley et al., RTSS’03; Li et al.,

RTAS’05

– Analysis Method• Mohan et al., RTSS’04; Abdelzaher

et al., RTSS’04

• Low-Duty-Cycle Networking– Scheduling

• Yang et al.(PTW), RTAS’04; Lu et al. (DESS), INFOCOM’05; Gu et al. (ESC), ICNP’09

– Unicast • Gu et al (DSF), SenSys’07; Su et al.,

ICNP’08

– Multicast and flooding• Guo et al. MobiCom’09; Wang et

al. , INFOCOM’09; Su et al., ICNP’09; Sun et al. (ADB), SenSys’09

We are the first to address real-time issue in low-duty-cycle Networks


55

What is a Low-Duty-Cycle Network• A low-duty-cycle network is formed by nodes that listen

briefly and shut down their radios most of the time (e.g., 95% or more).

• To communicate, a wakeup schedule must be shared among neighboring nodes.

2 3 83

active

84

Period = 100

active

Node Working Schedule : { 2, 83 }

Node Duty Cycle : 2 / 100 = 2%

An Active Instance


66

Delay in Low-Duty-Cycle Networks

AA BB CC DD

Packet Arrival Time :

{41} {71} {91}

41 71 91

{1}

1

End-to-end communication delay is 90

B C DA Sleep Latency = 40

Usually packet can be successfully delivered from a sender to a receiver within an active instance. TOS packet size 47 bytes, 20ms active instance duration, 13 tx by using CC2420. Above 30% link quality ensures 99% delivery ratio.


77

Agenda

• Motivations and Design ObjectiveMotivations and Design Objective

• Network ModelNetwork Model

• Delay Control– Temporal Delay Control– Spatial Delay Control– Hybrid Design

• Evaluation

• Conclusion


88

How to Temporally Reduce Delay?

AA BB CC

{41} {3,79}{1}

41 791


{2,41}

2 31


B CA

B CA

Original

New

Active Instance Augmentation Scheme


99

Optimization Goal

AA

BB

CC

EE

DD

SS

{41}

{38}{73}

{1}

{92}

{15}

Sink Node

How to augment a minimum number of active instances into the network, such that E2E delays from data source nodes to the sink node are all below delay bound ?


1010

Where to Augment Active Instance?The augmented active instance should always reduce sleep latency to 1

AA BB CC

{41} {25,79}{1} {2,41}

Waiting in the network can never reduce E2E delay!

E2E delay = 24

{24,41} E2E delay = 24

2 251

B CA

24 251

B CA


1111

How to Find Optimal Active Instance Augmentation ?

• Dynamic programming– Intermediate State Lij(m,h): The

minimal delay a packet arrives at node j after traversing at most m edges from node i. Among m edges, the sleep latencies of h edges are reduced to 1 by augmenting h active instances along the path.

AA BB CC

LAB(1,0) : Minimal delay from node A to B through edge AB without any active instance Augmentation LAC(2,1) : Minimal delay from node A to C through edge AB and BC by reducing the edge length of either AB or BC to 1

• i: source• j: destination• m: edges traversed• h: number of active instance augmented


1212

Example Walkthrough: Initial States

AA

BB

DD

{41}

{25,97}{1}

• Initial States:– LAB(1,0) = 40, LAC(1,0) = 14

– LAB(1,1) = 1 , LAC(1,1) = 1

– LAD(2,2) = 2

Lij(m,h)• i: source• j: destination• m: edges traversed• h: number of active instance augmented

{2,41}

{3,25,97}

CC

{15}{2,15}

Lij(m,h) = { dij m=1,h=0

m m = h


1313

Recursive Computation

ii pp jj

{ Lip(m-1,h-1) + 1

Lij(m,h) = min

• Case 1: From i to p (possibly multiple hops), then to j through one single hop without any active instance augmentation

• Case 2: From i to p (possibly multiple hops), then to j through one single hop by reducing sleep latency between p and j to 1

Lip(m-1,h) + dpj


1414

How to Optimally Bound Pair-wise E2E Delay?

• What we have known?– The minimum E2E delay between a source

node and a destination node by augmenting h active instances

• Given a Delay Bound– Find the minimum h value that yields the delay

smaller than the bound and augment those active instances into the network


1515

Many-to-Many Communication Bound

• NP-Hard and inapproximable

• Greedy Solution– Each active instance augmentation reduces

maximal sum of E2E delays among all source nodes and all destination nodes.


1616

Agenda

• Motivations and Design ObjectiveMotivations and Design Objective• Network ModelNetwork Model• Delay Control

– TemporalTemporal Delay Control Delay Control– Spatial Delay Control– Hybrid Design

• Evaluation• Conclusion


1717

How to Spatially Reduce Delay ?

AA

BB

CC

EE

DD

FFZZYY

How to select a minimum number of nodes as sink nodes such that E2E delay from any source node to a sink is within delay bound


1818

How to Find Optimal Sink Nodes?

AA

DD

CC

BB

EE

Assume Delay Bound is 100:

SA={A,C,D} SB={B,C}, SC={A,B,C}, SD={A,D,E}, SE={D,E}

The problem transforms to set cover problem

AA BB CC DD EE

AA

BB

CC

DD

EE

0 185 73 16 124

0 66 290 247102

99 0 155 11820

153 201 39 477

101 144 83 0172


1919

Solving the Set Cover Problem

– Repeatedly choose the set that contains the largest number of uncovered nodes

– Best-possible polynomial time approximation under plausible complexity assumptions.

AA

DD

CC

BB

EE

SA={A,C,D}, SB={B,C}, SC={A,B,C}, SD={A,D,E}, SE={D,E}


2020

Agenda

• Motivations and Design ObjectiveMotivations and Design Objective

• Network ModelNetwork Model

• Delay Control– TemporalTemporal Delay Control Delay Control– Spatial Delay ControlSpatial Delay Control– Hybrid Design

• Evaluation

• Conclusion


2121

Drawbacks of Temporal Delay Control

Not effective when delay bound is very small !


2222

Drawbacks of Spatial Delay Control

Inefficient for augmenting last a few sink nodes!


2323

Hybrid Design Tradeoff• Temporal Delay Control

– Pros: Little human intervention– Cons: Increase single node energy consumption

• Spatial Delay Control– Pros: Bound E2E delays for a large number of

nodes; No change on working schedule– Cons: Additional hardware cost and human

intervention

• We need to find a balanced configuration to achieve efficient power and cost management!


2424

Hybrid Design

• Cost Ratio:– Augmenting a sink node over augmenting an active instance– Based on hardware cost, lifetime of sink and sensor nodes,

human intervention cost, …

AA

DD

CC

BB

EE

SA={A,C,D}, SB={B,C}, SC={A,B,C}, SD={A,D,E}, SE={D,E}

Number of active instance augmentation for Node A,D,E

• Cost(Sink) >Cost(Active Inst. Aug.)– Augment Active Instances for Node A, D, E

• Cost(Sink) <Cost(Active Inst. Aug.)– Augment Sink Node D


2525

Evaluation• Large-Scale Simulation

– Up to 5000 nodes, 100 repeated experiments for each data point

– Baseline: Streamlined Wake-up in IPSN’05

• Test-bed Implementation– Linear Network, 5-hop network– 838 bytes of code memory, 12 bytes of data

memory on top of a sensing application


2626

Energy Efficiency of Temporal Delay Control

Consume half amount of energy than the baseline


2727

Deadline Miss Ratio vs. Augmented Sink

Larger delay bounds lead to smaller miss ratios


2828

Hybrid Performance

Hybrid is able to achieve the minimum system cost


2929

Testbed Performance

We are able to bound E2E delays on real system


3030

Conclusion• Delay Control in Low-Duty-Cycle networks is

challenging!• Three schemes for delay control

– Temporal solution by augmenting active instances• Energy optimal for bounding pair-wise communication

– Spatial solution by augmenting sink nodes – Hybrid solution

• Demonstrated effectiveness through large-scale simulation and test-bed experiments


3131

http://mess.cs.umn.edu

december 3, 2009 yu (jason) gu @ rtss ‘09 spatiotemporal delay control for low-duty-cycle sensor...

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