routing in sensor networks
DESCRIPTION
Routing in Sensor Networks. Prabal Dutta CS 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) - PowerPoint PPT PresentationTRANSCRIPT
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