The Firecracker Protocol

The Firecracker Protocol

Philip Levis and David Culler UC Berkeley

9/19/04 SIGOPSEW 1

Data Dissemination in Sensor Nets

•  Sensor net: many low power, wireless “motes” –  1-10 KB RAM, 4-8MHz CPU, 10-100Kbs radio

•  Dissemination: deliver a data item to every mote in a network –  Configuration constants –  Code updates, virtual programs

•  Requires a continuous protocol –  Transient disconnections, network repopulation

•  Two metrics: energy efficiency, rate

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Broadcast-based Protocols

•  Every node forwards •  Energy efficient

–  Can use physical density, opportunistic receptions •  Slow: can’t immediately forward

–  Suppression mechanisms, timers –  CSMA: broadcast storms –  RTS/CTS: control packet exchange latency

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Routing-based Protocols

•  One node forwards •  Fast

–  Next hop can immediately retransmit •  Energy inefficient: naming

–  Need many routes to reach entire network –  Naming every node unfeasible

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Firecracker Dissemination

•  Combine routing and broadcasts –  Routing’s speed –  Broadcasting’s efficiency

•  Seeding phase –  Route data to distant points in the network

•  Propagation phase –  Start broadcasting from routes

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Firecracker Example

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Firecracker Example

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Firecracker Example

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Firecracker Example

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Outline

•  Data dissemination •  Sensor networking, Trickle •  Firecracker •  Randomized Seeds •  Conclusion

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Outline


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Sensor Networking

•  Energy is critical, communication is costly •  Local wireless broadcast primitive

–  Unique node identifiers •  Many application requirements, many network

protocols –  Collection –  Any-to-any (logical coordinates: GEM, BVR, etc.) –  Local aggregation –  Dissemination

•  Trickle

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Trickle Algorithm

•  Periodically broadcast metadata M •  Suppression interval of length T •  Pick a random point b in T

– Broadcast unless you hear M •  When T expires, double it (up to a max) •  If you hear M+, make T very small (1 sec) •  If you hear M-, send an update •  Trickle plots

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Experimental Methodology

•  TOSSIM, a TinyOS simulator •  Compiles applications into a simulator engine •  Radio loss model based on empirical distributions

–  Asymmetric –  Highly variable

•  Unit disk interference model •  Bit-level or packet-level simulation

–  We used packet-level

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Trickle Plot

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•  20x20 grid (400 nodes) •  New datum •  15 foot spacing •  32 hop network •  Time to reception in

seconds •  Wave of activity

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Outline


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Firecracker

•  Start disssemination by seeding network •  Route data to a few distant points •  Start broadcast dissemination along paths

–  Destination, route, snooping •  Example: corners on a grid-based protocol

–  Nodes can forward to manhattan neighbors –  If two options, select randomly –  Network density ensures manhattan links exist

•  Same methodology as Trickle example

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Basic Trickle

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Adjacent Corners

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Opposite Corner

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All Corners

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Reception Time Distributions

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Time to Reception (seconds)

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Reception Time Distributions

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20835 Sends" 19544 Sends" 18275 Sends" 6665 Sends"

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Routing Reduces Cost

•  Routing happens quickly –  Synchronizes nodes –  Trickle performance improves

•  Fewer nodes need metadata exchanges –  Metadata is most of the traffic

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Hybrid Approach is Beneficial

•  Distant seed points –  Improves rate –  Reduces cost

•  Can’t assume knowing what distant points exist –  Can’t store all the names –  Need a way to select seeds –  Randomization prevents corner cases

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Outline


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Experimental Methodology

•  Use same grid arrangement •  Run twenty experiments, average results

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Four Policies

•  From corner to one random node in the grid •  From corner to three random nodes •  From center to three random nodes •  From corner to three random distant nodes

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Histograms

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One from corner Three from corner

Three distant from corner Three from center

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Results

•  Picking random nodes works OK –  Adding more does not improve results a great deal

•  Coverage improves from center •  Distant nodes works best •  Need route to edge of the network

–  Logical coordinate spaces support this

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Outline


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Network Protocols

•  Varying communication requirements –  Collection (n to one) –  Dissemination (one to n) –  Diffusion (m to n) –  Local Aggregation

•  Forwarding predicates –  Density estimation

•  Predicate and media access interaction •  Routing’s scoping enables fast propagation •  Slower broadcasts fill in the holes

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Questions and Discussion

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Any-to-Any Routing

•  Current protocols use logical coordinates –  GEM (Graph Embedding, polar coordinates) –  BVR (Beacon Vector Routing, n-dimensional)

•  GPSR uses geographic coordinates –  Requires localization –  Virtual coordinates may be possible

•  Data dissemination benefits from being able to name a distant node (network distance)

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Broadcasting

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Broadcasting

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Broadcasting

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Broadcasting

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Broadcasting

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Broadcasting

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Broadcasting

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Routing

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Routing

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Routing

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Routing

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Routing

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Routing, With Snooping

The Firecracker Protocol

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