Routing in Sensor Networks

Prabal DuttaCS 294-11, Oct 25, 2005

Some Communication Abstractions

Collection (MintRoute) Dissemination (Trickle) Point-to-Point (BVR) Aggregation (TAG, Synopsis Diffusion) Neighborhoods (Hood) Data-centric Storage (GEM, PathDCS) Attribute-based Routing (Directed Diffusion)

Slides borrowed from:A Holistic Approach to

Multihop Routing for Sensor Networks

Alec WooDissertation Talk

Computer Science Division, UC Berkeley

with David Culler and Terence Tong

Key TakeawaysPhysical connectivity is not unit disk

What does connectivity look like?How to estimate connectivity?

Often, more neighbors than slots in NBR TBLWhen to insert? Evict?How to avoid thrashing?

Routing algorithms use cost metricsWhat are the right metrics? Hops? Distance?

METX?Collection routing is a very common pattern

Boolean Connectivity Assumption

0

112

2

2

22

A

Physical ConnectivityMeasure

Average link quality among many pairs of nodes at different distances

Communication Range?3 regions, with a large transitional region

Effective Region

Transitional Region

Clear Region

Implications

Deployment: (X-axis) (In-situ analysis)Communication range = effective region

Individual nodes (Y-axis)Discover connectivity = link estimationHear many nodes in transitional regionHow to define a “neighbor”?

Zhao et al., SCALE

Transitional Region

Neighborhood: A Fuzzy Concept Many potential neighbors

Short effective region Short sensing range

Few good ones (blue) Large gray region

Neighbors > Table-size

If not in table, can’t estimate

Don’t rely on density control

Adapts to all cell density

NeighborTable

Get in

Get out

General solution:down-sample to suppress gray nodesmaintain frequent nodes

Average Hop-Count Contour Plot

Derive Connectivity Graph through

Passive Link Estimation Link sequence number snooping Estimate inbound reception quality

Key issue Cannot infer losses until next packet reception

Solution Rely on a network-wide minimum data rate

infer losses based on it

Bi-directional estimation Require outbound transmission quality estimation Exchange reception quality over local broadcast

E.g piggyback on route updates

A Good EstimatorAccurate

+/- 10% error, with a high confidence

Agile yet stableRelative to message opportunities rather than

time

Small memory footprintMany neighbors to estimate!

SimpleThis is a low-level operation

On-Line Table Management Process

Insertion PolicyAdaptive down-sampling hysteresis

Throw a coin, only insert if success

Eviction and Replacement PolicyClassical Cache Replacement Policy

FIFO, LRU (LRH), Clock

Borrow Database TechniquesEstimate most frequent tokens of a data streamFREQUENCY (Manku et al.)

Key ResultsFixed-size table as cell density increases

Freq alwayskeeps 50%or more of thetable entries in maintainingthe good neighbors

# Good neighbors > Table size

40Number of Potential Neighbors

1st 2nd 3rd

Cost FunctionsSP on physical connectivity graphSP with threshold on logical connectivity

graphPath Reliability (Yarvis et al.)

Product of link quality along the entire pathExponential drop: (link success rate)# of hops

Assumes no link retransmissionsMinimum Transmission (MT)

Cost is based on link quality

Cost = E[total number of trans.] ETX (De Couto et al.)

Implicit retransmission assumption

hop reverseforward pp

1Link estimatorprovides

50%

70% 70%

Tree-Building Approach Variant of a distributed distance-vector protocol

Goal: stable and reliable tree (nodes are relatively immobile)Different from discovering paths quickly in mobile computing

Operate over a dynamically changing physical connectivity graph Environmental changesNode failures

Low-rate periodic route messages (low bandwidth)Carry “cost” to tree rootPiggyback link estimations

Hear neighbor’s “cost” and store in tableSelect minimum cost neighbor for routing

Route damping (stability)Periodic vs. asynchronousSwitching threshold for noisy cost

Self-Organizing Networks

Using only simple local rules for highly resource-constrained nodes to self-organize into a globally consistent and robust network

Protocol design considerationBandwidth/energyAmount of states/complexityMemory footprint

One instance: Multihop routing

Overview Problem decomposition into 3 local processes

Connectivity defines relative to link quality estimation Neighbor table management to build weighted logical

connectivity graph Cost functions to exploit such graph

Observe global properties End-to-end success rate Hop distribution Topology Stability

Extensive simulations and empirical experiments

MintRoute, released in TinyOS 1.1

Roadmap

Physical Connectivity in Reality

Connectivity Graph Derivation with Link Estimations

Neighborhood Management

Tree-Based Routing Study

Central Limit Theorem Prediction

For a 10% error with a 95% interval worst case for agility is at least 100 packets

50 :Caset Wosr 100

)1( %)10(

4

Dist. Binomialfor Sigma )1(

Interval Confidence 95% %10]2

[

2

.pn

ppn

pp

n

Estimator Study Study 7 different estimators

EWMA, Flip-Flop EWMA, MA, Time-weighted MA, Packet Loss/Success Interval, WMEWMA

Compared by tuning each to the same objectives Verify with empirical traces See details in thesis

Results WMEWMA(T, ) Estimator

Stable, simple, constant memory footprintCompute success rate over non-overlapping window (T)Average over an EWMA()

Key Implication 10% |error| requires at least 100 packets to settle Limits rate of adaptation

Roadmap

Physical Connectivity in Reality

Connectivity Graph Derivation with Link Estimations

Neighborhood Management

Tree-Based Routing Study

Details Insert

Set prob. such that insertion rate < reinforcement rate Down-sample prob. min(1,Table Size / # Neighbors Est.) Estimate # neighbors based on periodic route beacons

Reinforce if in table Cache hit (FIFO, LRH, Clock) Node’s Counter++ (Freq)

bypass down-sampling for reinforcement

Evict Cache policies

evict for each insertion Freq: Counter--,

Counter == 0 becomes replaceable If all Counters > 0, drop insertion

Implications Non-threshold based neighborhood selection

No estimation required

One-hop neighbor Based on competitiveness relative to the goodness metric

Other goodness metric that augment neighborhood selection Control in/out degree on the logical connectivity graph

Higher-level changes on cell density will not affect system functionality Connectivity graph adapts with its best using limited resources

New neighborhood interface and abstraction

Holistic Approach to Routing

Now, the connectivity graph is built

Neighbor management using

FREQUENCY

A

Select Good RoutesBased on ?

A

Link Estimation using WMEWMA

A

Many-to-One Data Collection

A common routing service for data collectionSimple form of directed-diffusion

Tree rooted at the sink node where data is collected

10m

Single hop weatherSingle hop burrowMulti hop weatherMulti hop burrow

Evaluation Roadmap Key observations:

Hop distribution, end-to-end success, stability

Graph analysis 80x80 grid

SP, SP(%), MTRule out SP because of poor reliability

Packet-level simulation 10x10 grid, (max 2 retrans./hop)

Broadcast and DSDV (periodic route selection)Neighbor table management

Freq + Routing Goodness -> MTTM

Empirical (Mica/Mica2 Motes) 5x10 grid and 30-node random placement, smote SP(%), MT with large enough table max 2 retrans./hop, deliberate congestion

Large

Small

HighLevel

LowLevel

Graph Analysis Key Results

Hop-Distribution and Reliability to BS

Hop-Count DistributionEnd-to-end Success vs. Distance

Simulation Key ResultsStability

Empirical Study

Restudy connectivity vs. distancePut nodes at end of effective region (~ worst

case)8 feet

Study SP(70%), SP(40%), MT

Key observations:SP(70%) fails

SP(40%) failsHard threshold fails under congestion

Link qualitydrops under traffic

Empirical Key ResultsHop-Count DistributionEnd-to-end Success vs. Distance

Effective Region is 8 feet

Differentfrom simulations!

Congestion and Stability

LinkEstimation

TopologyStability

30-node network

Time (s)

%

# R

oute

Changes

Per

5 R

oute

Mess

ages

Possible Congestion/Rate Control: Woo et al. (Mobicom ’01)

Mitigate Instability

Subtle overflow bug in link estimation

Confidence-interval filtering on link estimation

Link estimation to tree root can affect stability on the entire tree

Switching threshold helps stability, but sacrifices end-to-end success rate

Cross-layer Interactions

3.02

2.49

0.52

0.100.14

Ave.# ofParent ChangesPerRouteUpdate

Induced Interference

0.30

0.10

Ave.# ofParent ChangesPerRouteUpdate

Node Failure

Current StatusUsed by GDI ’03, TinyDB, TASK (Intel)

TinyOS 1.1 Release

Surge as a Network Analysis ToolCrossbow: www.xbow.com

Incorporated with low-power listening

~97% success rate on mica2

Source: Crossbow

Related Work Summary Connectivity Study

Choi et al., Zhao et al., Cerpa et al., Ganesan et al. Link estimation

IGRP, EIGRP, De Couto (Mobicom ’03), Kim et al. (Mobicom ’99) Neighborhood Management

Limiting Logical Neighborhood Size (Miller et al., Simulation of computer networks ’ 87)

Random Selection (Shacham et al., ICC ’88) Routing Metrics

De Couto (Mobicom ’03) Draves et al. (Microsoft Research TR-2004-18 March ’04) LIR, least gain routing opt. for spatial reuse (SRNTN ’88) LRR, link cost = physical-level interference, (Tactical Communication

Conference ’90) Sensor Network Routing

Real experiment running DSDV + Path Reliability Metric (Yarvis et al. IWAHN ’02)

Future Work

Reverse Tree Routing Support any-to-any routing

Co-design of query processing and networkingQuery-informed routingSee June Communication of the ACM ‘04

Thank you!

Backup Slides

A Connectivity Cell144-node, 12x12 grid network with Rene

Motes

Joint work with Ganesan et al.

2-feet spacing

Low transmit power

Open tennis court

RSSI & Link Quality

Can we use RSSI to predict link quality?

Low packet loss => good RSSIBut not vice versaInterference from traffic

Similar findingsZhao et al. (RFM sensor networks)De Couto et al. (802.11 networks)

Approximate Connectivity Variations

Approximate time variations

Time-Varying Connectivity

Link quality varies over time

over an 8-hour periodover a 5-hour period

Routing Architecture

Table Management

Timer

Parent Selection

Cycle Detection

Estimator

Route message• save information

All message• sniff and estimate

Data message

Cycle detected• choose other parent

Run parent selectionand send route message periodically

ApplicationSend originated data message

All Messages• discard non data packet• discard duplicate packet

Filter

Forward Queue

NeighborTable

Originating Queue

Forwardingmessage

Send route update message

Topology over Time

70-100%

Est. Link Quality

40-70%

0- 40%

Tree Depth

1

2

3

7

7

14

21

28

35

42

49

14 21 28 35 42 49 56 630Feet

Fee

t

Channel Utilization Contour

Routing Cost: Actual vs. Est.

Pursuer and Evader Application

The Berkeley NEST team

“Design and Implementation of a Sensor Network System for Vehicle Tracking and Autonomous Interception”, Submitted to OSDI 2004

Hops and Cost Metrics

Shortest Path vs. Shortest Path with threshold

Hop over distance is a relative concept.

Highlights of Other Work Query Processing and Networking Co-design

CACM June 04, with Ramesh Godvidan and Sam Madden Shadowing Phenomenon

UCB Tech 04, with Kamin Whitehouse, Joe Polastre, Fred Jiang Ranging and Localization

Acoustic, Ultrasound Infrastructure and Ad hoc Submitted to SenSys 04, with Kamin Whitehouse, Fred Jiang,

Chris Karlof, and David Culler Mica Sensorboard

Sold as Crossbow MTS300/310 MAC and Transmission Rate Control for Fairness

Mobicom 2001, with David Culler TinyOS

ASPLOS 2000with Jason Hill, Robert Szewczyk, Seth Hollar, David Culler, and Kris

Pister

2004: a year of the mote?

May be?

What can you really do with it?

“I think there is a world market for maybe five computers.”- IBM Chairman Thomas Watson, 1943

It’s time to innovate! Let’s talk!

Why such a Holistic Approach?

The underlying issues matter!

Expose and embrace these issues Not assume over them

Articulate the 3 core system components Understand how they interact and affect each

otherIndependent improvementReusability

Distributed Tree-Building Process

Candidate Non-Bayesian Link Estimators

A Derived Connectivity Graph

Neighbor management keep the good ones build a logical connectivity graph

Select Good RoutesOver Logical Conn

Graph

Wireless Networking

Packet Radio NetworksWi-Fi Mobile

ComputingSensor Networks

Rooftop/Metropolitan Networks

Radio

Traffic

Applications

Bandwidth

Routing Any-to-any

Individual User

Pairs of indep.flows(end-to-end)

Co-op, correlated, in-network processing

Many-to-one(few)

High

Mobility

Single-band

Resources

Mobile Static

Spread spectrum

Low

Phy Layer

Not a concern Limited

Transport End-to-End ?? Custody/Best Effort

Network as a whole

local

global

ChallengesProgramming a large network of highly

resource-constrained nodes to self-organize into some global consistent and robust behavior using only simple local rules over a noisy and dynamically changing environment

Think small and big

Take a probabilistic view to describe lossy link quality and follows such apporach all the way up to the routing layer

Bandwidth/energy, amount of states/complexity, memory footprint, reliability over unreliable channel

2004: a year of the mote?

May be?

I think there is a world market for maybe five computers (sensor networks?). - IBM Chairman Thomas Watson, 1943

There is no reason anyone would want a computer (sensor network?) in their home. -Ken Olson, president of Digital Equipment Corp.

1977