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Robust Messaging Minitask Report
Notre DameOhio State
PARCUC Berkeley
UC Irvine
Hongwei Zhang & Vinod Kulathumani, OSU
Dec 2003
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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
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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
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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
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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
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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
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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
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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
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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
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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.