Routing in Sensor Networks

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Routing in Sensor Networks

• Prabal Dutta
• CS 294-11, Oct 25, 2005

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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)

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Slides borrowed fromA Holistic Approach to
Multihop Routing for Sensor Networks

• Alec Woo
• Dissertation Talk
• Computer Science Division, UC Berkeley

with David Culler and Terence Tong
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Key Takeaways

• Physical connectivity is not unit disk
• What does connectivity look like?
• How to estimate connectivity?
• Often, more neighbors than slots in NBR TBL
• When to insert? Evict?
• How to avoid thrashing?
• Routing algorithms use cost metrics
• What are the right metrics? Hops? Distance? METX?
• Collection routing is a very common pattern

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Boolean Connectivity Assumption
0
A
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Physical Connectivity

• Measure
• 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
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Implications
Transitional Region

• Deployment (X-axis) (In-situ analysis)
• Communication range effective region
• Individual nodes (Y-axis)
• Discover connectivity link estimation
• Hear many nodes in transitional region
• How to define a neighbor?
• Zhao et al., SCALE

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Neighborhood A Fuzzy Concept

• Many potential neighbors
• Short effective region
• Short sensing range
• Few good ones (blue)
• Large gray region
• Neighbors gt Table-size
• If not in table,
• cant estimate
• Dont rely on density
• control
• Adapts to all cell density

Get in
Get out
Neighbor Table

• General solution
• down-sample to suppress
• gray nodes
• maintain frequent nodes

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Average Hop-Count Contour Plot
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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

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A Good Estimator

• Accurate
• /- 10 error, with a high confidence
• Agile yet stable
• Relative to message opportunities rather than
  time
• Small memory footprint
• Many neighbors to estimate!
• Simple
• This is a low-level operation

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On-Line Table Management Process

• Insertion Policy
• Adaptive down-sampling hysteresis
• Throw a coin, only insert if success
• Eviction and Replacement Policy
• Classical Cache Replacement Policy
• FIFO, LRU (LRH), Clock
• Borrow Database Techniques
• Estimate most frequent tokens of a data stream
• FREQUENCY (Manku et al.)

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Key Results

• Fixed-size table as cell density increases

Good neighbors gt Table size
1st
2nd
3rd
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Number of Potential Neighbors
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Cost Functions

• SP on physical connectivity graph
• SP with threshold on logical connectivity graph
• Path Reliability (Yarvis et al.)
• Product of link quality along the entire path
• Exponential drop (link success rate) of hops
• Assumes no link retransmissions
• Minimum Transmission (MT)
• Cost is based on link quality
• Cost Etotal number of trans.
• ETX (De Couto et al.)
• Implicit retransmission assumption

70
70
50
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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 changes
• Node failures
• Low-rate periodic route messages (low bandwidth)
• Carry cost to tree root
• Piggyback link estimations
• Hear neighbors cost and store in table
• Select minimum cost neighbor for routing
• Route damping (stability)
• Periodic vs. asynchronous
• Switching threshold for noisy cost

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(No Transcript)
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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 consideration
• Bandwidth/energy
• Amount of states/complexity
• Memory footprint
• One instance Multihop routing

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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

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Roadmap

• Physical Connectivity in Reality
• Connectivity Graph Derivation with Link
  Estimations
• Neighborhood Management
• Tree-Based Routing Study

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Central Limit Theorem Prediction

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

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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 footprint
• Compute 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

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Roadmap

• Physical Connectivity in Reality
• Connectivity Graph Derivation with Link
  Estimations
• Neighborhood Management
• Tree-Based Routing Study

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Details

• Insert
• Set prob. such that insertion rate lt
  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)
• Nodes Counter (Freq)
• bypass down-sampling for reinforcement
• Evict
• Cache policies
• evict for each insertion
• Freq Counter--,
• Counter 0 becomes replaceable
• If all Counters gt 0, drop insertion

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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

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Holistic Approach to Routing

• Now, the connectivity graph is built

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Many-to-One Data Collection

• A common routing service for data collection
• Simple form of directed-diffusion
• Tree rooted at the sink node where data is
  collected

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Evaluation Roadmap

• Key observations
• Hop distribution, end-to-end success, stability
• Graph analysis
• 80x80 grid
• SP, SP(), MT
• Rule 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 -gt 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

High Level
Large
Low Level
Small
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Graph Analysis Key Results

• Hop-Distribution and Reliability to BS

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Simulation Key Results
Hop-Count Distribution
End-to-end Success vs. Distance

• Stability

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Empirical Study

• Restudy connectivity vs. distance
• Put nodes at end of effective region ( worst
  case)
• 8 feet
• Study SP(70), SP(40), MT
• Key observations
• SP(70) fails
• SP(40) fails
• Hard threshold fails
• under congestion

Link quality drops under traffic
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Empirical Key Results
Different from simulations!
Effective Region is 8 feet
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Congestion and Stability
30-node network
Topology Stability
Route Changes Per 5 Route Messages
Link Estimation

Time (s)
Possible Congestion/Rate Control Woo et al.
(Mobicom 01)
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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

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Cross-layer Interactions
Ave. of Parent Changes Per Route Update
3.02
2.49
0.52
0.14
0.10
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Induced Interference
Ave. of Parent Changes Per Route Update
0.30
0.10
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Node Failure
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Current Status

• Used by GDI 03, TinyDB, TASK (Intel)
• TinyOS 1.1 Release
• Surge as a Network Analysis Tool
• Crossbow www.xbow.com
• Incorporated with
• low-power listening
• 97 success rate on mica2

Source Crossbow
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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)

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Future Work

• Reverse Tree Routing Support
• any-to-any routing
• Co-design of query processing and networking
• Query-informed routing
• See June Communication of the ACM 04

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Thank you!
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Backup Slides
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A Connectivity Cell

• 144-node, 12x12 grid network with Rene Motes

• Joint work with
• Ganesan et al.
• 2-feet spacing
• Low transmit
• power
• Open tennis
• court

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RSSI Link Quality

• Can we use RSSI to predict link quality?
• Low packet loss gt good RSSI
• But not vice versa
• Interference from traffic
• Similar findings
• Zhao et al. (RFM sensor networks)
• De Couto et al. (802.11 networks)

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Approximate Connectivity Variations

• Approximate time variations

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Time-Varying Connectivity

• Link quality varies over time

over an 8-hour period
over a 5-hour period
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Routing Architecture
Timer
Send originated data message
Application
Send route update message
Run parent selection and send route message
periodically
Parent Selection

• Cycle detected
• choose other parent

Originating Queue
Forward Queue
Table Management
Cycle Detection
Estimator
Neighbor Table
Forwarding message

• Route message
• save information

• All message
• sniff and
• estimate

Data message
Filter
All Messages

• discard non data packet
• discard duplicate packet

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Topology over Time
Est. Link Quality
70-100
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0- 40
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Tree Depth
Feet
28
1
21
2
14
3
7
7
14
21
28
35
42
49
56
63
0
Feet
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Channel Utilization Contour
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Routing Cost Actual vs. Est.
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Pursuer and Evader Application

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

The Berkeley NEST team
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Hops and Cost Metrics

• Shortest Path vs. Shortest Path with threshold

Hop over distance is a relative concept.
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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 2000
• with Jason Hill, Robert Szewczyk, Seth Hollar,
  David Culler, and Kris Pister

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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
• Its time to innovate! Lets talk!

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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
  other
• Independent improvement
• Reusability

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Distributed Tree-Building Process
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Candidate Non-Bayesian Link Estimators
Select Good Routes Over Logical Conn Graph

• Neighbor management
• keep the good ones
• build a logical
• connectivity graph

A Derived Connectivity Graph
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Wireless Networking
Rooftop/Metropolitan Networks
Packet Radio Networks
Wi-Fi Mobile Computing
Sensor Networks
Applications
Individual User
Network as a whole
Co-op, correlated, in-network processing
Pairs of indep. flows(end-to-end)
Traffic
global
Transport
End-to-End
?? Custody/Best Effort
Routing
Any-to-any
Many-to-one(few)
local
Mobility
Mobile
Static
Resources
Not a concern
Limited
Radio
High
Low
Bandwidth
Single-band
Spread spectrum
Phy Layer
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Challenges

• Programming 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

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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