efficient network flooding and time synchronization with glossy federico ferrari, marco zimmerling,...

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