robust messaging minitask report notre dame ohio state parc uc berkeley uc irvine hongwei zhang...

Robust Messaging Minitask Report

Notre DameOhio State

PARCUC Berkeley

UC Irvine

Hongwei Zhang & Vinod Kulathumani, OSU

Dec 2003

2

Scope

• Comparative study of existing messaging protocols for well-understood scenarios (e.g., A Line In The Sand, Pursuer Evader, Red Force Tagging, Shooter Location) reliability delay throughput/goodput scalability

• Comparison at scale of 100 nodes by testbed-based experiments

• Comparison at the scale of 1000 nodes by simulations

3

Issues

• Importance of testing at scale repeatable result: What works for n nodes does not work for 10n nodes ! several observed routing results for 10-20 nodes do not port to 50-100 nodes

• Importance/hardness of validating simulation completeness & precision especially, fidelity of simulation model (e.g., radio transmission, collision) several observed discrepancies between simulations & experiment complexity of building adequate mathematical models due to

large space of dimensions hardness of extract parameters from expt. traces in protocol independent way

• Benefits of standardized API for porting codes between simulation & experimentation for composability (plug & play) for easy comparison of different protocols

4

Contributions

Notre Dame• Robust routing strategy for Red Force Tagging • Partial list of robustness techniques

PARC• Modeling & Simulation Environment for Ad-hoc Routing Applications in

Wireless Sensor Networks

• Baseline Routing Strategies Spanning tree, Flooding

• Meta Adaptive Routing Strategies based on Reinforcement Learning Adaptive tree, constraint-based search, constrained flooding

• Test Case Studies

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Contributions (contd.)

OSU Experiments

compared GridRouting/ReliableComm & MintRoute wrt A Line In The Sand scenario

generated experimental traffic traces for different types of events in the A Line In The Sand scenario

Simulation compared GridRouting/ReliableComm with GridRouting/TDMA in

Prowler wrt A Line In The Sand scenario defined a uniform interface between modules of Prowler

Compiled a list of existing protocols, papers, and studies related to robust-messaging

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Contributions (contd.)

UC Berkeley Midterm demo 7/2003 report: Landmark Routing tree

evader information reaches landmark landmark forwards information via crumb-trail to pursuer

Alec Woo et al Sensys 11/2003 report: MintRoute tree routing distance vector with minimum transmission cost metric link quality estimates used to calculate expected total # of trans.

Jason Hill’s Surge report on robust routing 19 node experiment-based fine grain analysis of a multihop data

collection application using Alec’s routing protocol

UC Irvine TDMA-based routing experiment & simulations in various traffic

pattern scenarios

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Outline

• OSU Experimental study of GridRouting/ReliableComm & MintRoute

• PARC Network & application modeling Strategy learning for wireless ad hoc routing

• UCI Experiment Simulations

Experimental study: GridRouting/ReliableComm vs. MintRoute

OSU

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Overview

• Objective Comparative study of the performance of

GridRouting/ReliableComm & MintRoute/QueuedSend in the A Line In The Sand scenario

For GridRouting/ReliableComm , study the impact of node location, power level, and maximum number of retransmissions on the end-to-end delay as well as reliability

• Metrics: Mean and variance in

Packet delivery ratio (per event basis) End-to-end delay Goodput for a given event

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Software components

RadioCRCPacket

ReliableComm

GridRouting

LITeS

GenericComm-Promiscuous

QueuedSend

MintRoute

LITeS

OSU UCB

Not using beta/CC1000RadioAck due to availability as well as weather constraint

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Network testbed

• 7 * 7 grid of MICA2 motes

0 1 2 3 4 5 60

1

2

3

4

5

6

Base station

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Application traffic

• Car moving across the network from left to right at a speed of 5~15 MPH

• A mote generates a “start” message at the beginning of an event; the mote generates an “end” message at the end of the event

• All the messages are sent to the base station, which performs higher-level detection and classification

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GridRouting/ReliableComm vs. MintRoute

Power level = 9Power level = 9

Metrics

GridRouting & ReliableComm MintRoutewithout

ACKno ACKACK w/ max. 1 retransmit

ACK w/ max. 2 retransmit

Packet delivery ratio (%)

Mean 46.72 33.69 54.41 33.72

Variance 5.53 4.7 5.41 4.21

Delay(seconds)

Mean 7.4513 8.8889 18.7303 0.1102

Variance 0.3093 0.1772 1.3616 0.0071

Goodput(packets/sec)

Mean 3.6701 2.2978 3.5734 2.7645

Variance 1.3782 1.1103 1.3479 0.8948

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Per-node packet delivery ratio: GridRouting/ReliableComm

Base station

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Per-node packet delivery ratio: MintRoute

Base station

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Summary: GridRouting vs. MintRoute

• GridRouting provides better packet delivery ratio & goodput

• The packet delivery ratio for each individual mote is distributed more evenly in GridRouting

• End-to-end delay is shorter in MintRoute

• To do: Compare GridRouting/ReliableComm with MintRoute/RadioACK

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Outline

• OSU Experimental study of GridRouting/ReliableComm and MintRoute

• PARC Network & application modeling Strategy learning for wireless ad hoc routing

• UCI Experiment Simulation

Network and Application Modeling and Strategy Learning for Wireless Ad-hoc Routing

PARC

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Outline

• One Modeling and Simulation Environment for Ad-hoc Routing Applications in Wireless Sensor Networks

• Two Baseline Routing Strategies

• Three Meta Adaptive Routing Strategies based on Reinforcement Learning

• Four Test Case Studies

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RMASE: Routing Modeling & Application Simulation Environment

• Motivation: Comparing Routing Algorithms in a Systematic Way

• Functions: Network Models:

Generate Network Topologies Radio and Fault Models:

Set Transmission Parameters and Fault/Alive Probabilities Application Models:

Generate Application Scenarios Performance Metrics:

Calculate Performance Metrics for Simulated Runs Layered Routing Architecture

• Developed on Prowler with Application Name ‘generator’

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Network Topology Models (I)

• Default Regular Grid• Parameter Settings

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Network Topology Models (II)

• Small and Large Random Offsets

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Network Topology Models (III)

• Grid Shifts

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Network Topology Models (IV)

• Distance and Density

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Network Topology Models (V)

• Fixed and Random Holes

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Radio and Fault Models

• Prowler’s Radio Model Signal Fading Formula

Asymmetric Link Dynamic Link Random Error

Collision

• Energy Use Model One unit for every transmission

• Faulty/Alive Model If fault, become alive with probability p If alive, become fault with probability q

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Application Models

• Source and Destination Specifications

• Source Rate: r packages per second

• Initialization Time• Source Amount: n

total packages per source• Source/Destination Distance• Source Trace

given by a trace file

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Performance Metrics

• Latency (s): Tarrived – Tsent• Throughput (p/s): N/T

N: the total number of packets received T: the duration of simulation

• Loss Rate: n/N n: the number of packets missing N: the total number of packets received

• Energy Use: Σipi The total number of packets sent in the network

MinimizeMaximize

Minimize

Minimize

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Layered Routing Architecture

Stats

App

MAC

Router

generator_application

Init_ApplicationPacket_SentPacket_ReceivedClock_Tick

Send_Packet

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Baseline Routing Strategies

Stats

App

MAC

Flood

Stats

App

MAC

SpanTree

Ignore_Duplicate

Unconstrained Flood Routing Spanning Tree Routing

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Meta Routing Strategies based on Reinforcement Learning

• Meta-Routing Strategies: destination specification: constraints on attributes cost function: function on attributes meta-strategies: independent to destination and cost specification

StructuredSource-Destination Path

Spanning Tree

Adaptive Spanning Tree

ConnectionlessReal-time Search

Flooding

Constraint-based SearchConstrained Flooding

Reinforcement Learning

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Application Studies

• Case I (OSU): A Line in the Sand (LIS) Network: 10x10, offset 0.1, hole <6.5, 4.5, 2, 9, 1> Source: dynamic, given by trace Destination: static, fixed 300 sec, 3 runs

• Case II (ND): Red Force Tagging (RFT) Network: 5x10, offset 0.1 Source: mobile, fixed, unique Destination: static, fixed, unique 30 sec, 4p/s, 5 runs

• Case III (UCB): Pursuer Evader Game (PEG) Network: 7x7, offset 0.1 Source: dynamic, fixed, unique Destination: mobile, fixed, unique 15 sec, 4p/s, 5 runs

• Case IV (Vanderbilt): Shooter Locator (SL) Source: dynamic, random, not unique Destination: static, fixed, unique Future work

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Routing Strategies Comparisons

• Five Strategies Flood Spanning tree Adaptive tree Constraint-based search Constrained flooding

• Four metrics Latency Throughput Loss rate Energy

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A Line in the Sand

Flood

Span Tree

Adaptive Tree

Constraint-based Search

Constrained Broadcast

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Red Force Tagging

Flood

Span Tree

Adaptive Tree

Constraint-based Search

Constrained Broadcast

36

Pursuer Evader Game

Flood

Span Tree

Adaptive Tree

Constraint-based Search

Constrained Broadcast

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Take Away Points

• Rmase Provides a virtual experimental platform for studying routing

strategies• None of the routing strategy is superior to others;

performance depends on the network and application types metrics the application cares about

• The relationship between simulation and hardware Simulation makes assumptions Hardware verifies assumptions

UCI

TDMA-based Routing Experiments & Simulations

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Routing Tree

• Motes 24 Mica2 motes

• Topology 6x4 grid with 4 ft. spacing,

outdoors PowerNode at upper left corner to

test the longest routing paths• Communication settings

Size of TDMA slot = 48 msec 12 TDMA slots per cycle Packet transmission frequency: 1.736Hz

(one per TDMA cycle) Radio transmission power: 3 Total number of packets: 36,840 Data contents of msgs: 3 ~ 24 bytes (variable sizes)

• Metric: Response Time = Sensing-to-Tracking Time

PNGroup 1 Group 2 Group 3

Group 4 Group 5 Group 6

PowerNode master gate worker

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Experimental Results & Observations

Hop count

End-to-End delivery success

rate

Equivalent

one-hop reliability

End-to-End Response Time (msec)

Max Min Avg

1 98.39% 98.39% 32 32 32

3 89.67% 96.40% 992 368 620

5 77.50% 95.03% 1664 848 1280

• Every worker node was programmed to generate sensor data reports once every TDMA round. ==>Multiple simultaneous reports were handled without unnecessary collisions.

• Over 95% of one-hop link reliability is achieved ==> Reflects high performance of the global clock synchronization mechanisms built.

• 18 out of 24 motes reported their environment sensing data. 17 out of 18 motes experienced negligible variances in power node response times ==> Proves highly deterministic nature of the protocol.

0

10

20

30

40

50

60

70

80

90

100

0 5 10 15 20 25

mote ID

succ

ess

rate

(%)

# hops = 1# hops = 3# hops = 5

End-to-End Delivery Success Rate

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Simulation of TDMA & Routing with Prowler

TDMA Scheduler

Prowler with TDMA

Neighborhoodinformation of

each node

TDMA schedule& Routing tree

Response time& Queue length

• Application layer: Describe the motes’ handling of events: Packet_Sent, Packet_Received, Clock_Tick. It also implements the mote initiation and data file storage.

• Radio channel layer: On top of the CSMA layer, a layer which executed TDMA and routing was built. Worker nodes, gate nodes, and master nodes were all simulated.

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Simulation Setup

• Network topology 4x6 grid (UCI), 5x10 grid (UND), and 10x10 grid (OSU)

• Simulation scenarios Heavy load: message demand of 1 packet per TDMA round Average load: message demand of 1 packet per 2 TDMA rounds Tracking of one moving target

Trajectory: one linear movement along one axis (OSU’s application model)

As long as a mote detects the target, it transmits one packet per TDMA round.

• Packet losses due to buffer overflow • Evaluation metric: Worst-case response time

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Simulation Results

Worst-case Response Time from Different Scenarios

0

2

4

6

8

10

12

4x6 grid 5x10 grid 10x10 grid

resp

onse

tim

e (s

ec)

Average loadHeavy loadTracking

Robust Messaging: Fundamental issues and strategies in “Red Force Tagging”

What causes difficulties?(A) Node reliability(B) Node locations (incl uncertainty)(C) Channel characteristics (incl interference)

Note: Power trivially solves all robustness (and latency) problems. So, for a meaningful problem, maximum power and average power must be bounded

Notre Dame

45

Difficulties I

(A) Node reliabilityIf failures are independent with failure rate p and nodes are uniformly randomly distributed with density λ, the node density is (1-p)λ

(B) Node locationsPerfectly known locations: The variance in internode distances results in varying link quality or stringent requirements for power control (in particular for nearest-neighbor routing)

Uncertainty in positions: Can be viewed as uncertainty in the channel.

Lifetime is an issue in irregular networks.

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Difficulties II

(C) Channel characteristicsChannel is unknown due to fading and interference (and localization errors)

- Slow fading: obstacles, multipath geometry (lognormal)- Fast fading: mobility (Rayleigh, Rice)- Interference: makes channel estimation difficult

(need to distinguish between noise and interference)

Remark: Low path loss exponents are desirable in terms of power consumption. But the average per-node throughput goes to zero if =m in an m-dimensional networkTo achieve good scaling, we need high

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Robust Messaging Strategies

Techniques to achieve robustness:• Avoid random node placement. Deploy nodes regularly• No nearest-neighbor routing in random networks• Estimate link quality. Choose good links• Exploit time, frequency, and path diversity:

* retransmissions (with implicit/explicit ACK); coding * frequency hopping or spread-spectrum * multipath routing; find backup routes

• Reduce interference (good MAC, spread-spectrum, light traffic [high data rates], power control, directional transmission)

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Characteristics: Mobile Tagmote. Large amount of data. Only one connection active. Throughput is crucial.

Approach:- Regular network topology- Always use maximum power- Use ARQ-N ACKnowledgments (increases throughput)- Keep track of number of “retries” for a link estimate- Maintain list of multiple next-hop neighbors (multi-tree structure)

Robust Messaging in Red Force Tagging

Achievable reliability: 90-100% with a goodput of 200bytes/s.