Robust Messaging Minitask Report

Notre Dame

Ohio State

PARC

UC 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

5

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

6

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

7

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

9

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

10

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

11

Network testbed

• 7 * 7 grid of MICA2 motes

0 1 2 3 4 5 60

1

2

3

4

5

6

Base station

12

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

13

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

14

Per-node packet delivery ratio: GridRouting/ReliableComm

Base station

15

Per-node packet delivery ratio: MintRoute

Base station

16

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

17

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

19

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

20

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’

21

Network Topology Models (I)

• Default Regular Grid• Parameter Settings

22

Network Topology Models (II)

• Small and Large Random Offsets

23

Network Topology Models (III)

• Grid Shifts

24

Network Topology Models (IV)

• Distance and Density

25

Network Topology Models (V)

• Fixed and Random Holes

26

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

27

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

28

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

29

Layered Routing Architecture

StatsStats

AppApp

MACMAC

RouterRouter

generator_application

Init_ApplicationPacket_SentPacket_ReceivedClock_Tick

Send_Packet

30

Baseline Routing Strategies

StatsStats

AppApp

MACMAC

FloodFlood

StatsStats

AppApp

MACMAC

SpanTreeSpanTree

Ignore_DuplicateIgnore_Duplicate

Unconstrained Flood Routing Spanning Tree Routing

31

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

32

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

33

Routing Strategies Comparisons

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

• Four metrics Latency Throughput Loss rate Energy

34

A Line in the Sand

Flood

Span Tree

Adaptive Tree

Constraint-based Search

Constrained Broadcast

35

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

37

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

39

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

PN

Group 1 Group 2 Group 3

Group 4 Group 5 Group 6

PowerNode master gate worker

40

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

su

ccess r

ate

(%

)

# hops = 1

# hops = 3

# hops = 5

End-to-End Delivery Success Rate

41

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.

42

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

43

Simulation Results

Worst-case Response Time from Different Scenarios

0

2

4

6

8

10

12

4x6 grid 5x10 grid 10x10 grid

resp

on

se t

ime

(sec

)

Average load

Heavy load

Tracking

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 reliability

If 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 locations

Perfectly 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.

46

Difficulties II

(C) Channel characteristics

Channel 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 network

To achieve good scaling, we need high

47

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)

48

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.