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