Efficient Network Flooding and Time Synchronization with Glossy

Federico Ferrari, Marco Zimmerling, Lothar Thiele, and Olga Saukh

ETH ZurichIPSN 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

Δ

1. Identical packet2. Small 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