Efficient Network Flooding and Time Synchronization with Glossy

Report
Efficient Network Flooding and
Time Synchronization with Glossy
Federico Ferrari, Marco Zimmerling, Lothar Thiele,
and Olga Saukh
ETH Zurich
IPSN 2011 Best Paper Award
Presenter: SY
Outline
•
•
•
•
Introduction
Design
Evaluation
Conclusion
Flooding
• Packet transmission from one node to all
other
• Challenges
– Packet loss
– Delay
– Flooding storm
Glossy
• Flooding for wireless sensor networks
– Fast: 94 nodes within 2.39ms
– Reliable: 99.99%
– Scalable
– Time synchronization at no additional cost
Interference
• Capture effect
– Two signals interfere which other
– If one is stronger that the other
– Or received significantly earlier than the others
– Receiver might still receive the packet
• Constructive interference
1. Identical packet
2. Small Δ
Δ
Generating Constructive Interference
• Matlab simulations
Related Works
• Capture effect
• Backcast: Dutta et al. 2008
– Concurrent ACK transmission
• A-MAC: Dutta et al. 2010
– Receiver-initiated link layer protocol
Outline
•
•
•
•
Introduction
Design
Evaluation
Conclusion
Overview
• Decouples flooding
• Concurrent transmission
• Constant slot length
Glossy in Detail
Timeline
Implementation
• Platform
– Tmote Sky = Taroko
– MSP430F1611 + CC2420
– MCU and timer source by DCO
• temperature and voltage drifts of -0.38%/◦C and 5%/V
• Challenges
– Deterministic execution timing
– Start execution at same time
– Compensate for hardware variations
Deterministic execution timing
• Start reading content while receiving
• Immediately trigger transmission
Start execution at same time
• SFD interrupt
• Variable delay in serving interrupt
– Execute NOPs determined at runtime
Compensate for hardware variations
• Synchronizes the DCO every time Glossy starts
– with respect to 32.768KHz crystal
• Software delay uncertainty
Outline
•
•
•
•
Introduction
Design
Evaluation
Conclusion
Theoretical Analysis
• Scenario
• Worst-Case Drift of Radio Clock
– Assume an upper/lower bound of radio clock drift
– Worst-case scenario:
• one path at highest clock drift, another at lowest
– Model worst-case transmission time uncertainty
• Worst-case temporal displacement
– Uncertainty on pair of radio and MCU clock
– Worst-case scenario:
• one path at minimum variation, another at maximum
– Worst-case temporal displacement Δ
Results
• Network size
• Node density
Controlled Experiments
• Setup 1
– One initiator, two receivers
– Delay one receiver by [0,8]us
– Non-delay receiver@-20dBm, delayed@-13dBm
Controlled Experiments
• Setup 2
– One initiator, variable # of recievers
– No delay
Controlled Experiments
• Setup 3
–
–
–
–
One initiator, four receivers
Start a Glossy phase, computes reference time
Schedules next phase
All nodes activate an external pin when a phase start
Testbed Experiments
• Testbed
– Motelab: 94 nodes over three floors
– Twist: 92 nodes
– Local: 39 nodes
• Metrics
– Flooding latency L
– Flooding reliability R
– Radio on time T
Results
• Node density no noticeable dependency
• Performance depends on network size
• Increase N significantly enhances flooding
reliability
Performance on Twist
• Larger size, higher latency
• 80% of nodes has 99.99%
reliability even with lowest
power
• Radio on time increase
with network size
Maximum Number of Transmissions
• Vary N
Conclusion
• Flooding and time sync are two important
services
• Well written, systematically analysis
• Promising results
• Detailed implementation
• Testbed evaluation
• Integrate with application might not be easy

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