The Tenet Architecture for Tiered Sensor Networks

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The Tenet Architecture for Tiered Sensor Networks

• Ben Greenstein, Jeongyeup Paek
• Omprakash Gnawali, Ki-Young Jang, August Joki,
  Marcos Vieira, Deborah Estrin, Ramesh Govindan,
  Eddie Kohler

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The Problem

• Sensor data fusion within the network
• can result in energy-efficient implementations
• But implementing collaborative fusion on the
  motes for each application separately
• can result in fragile systems that are hard to
  program, debug, re-configure, and manage.
• We learnt this the hard way, through many trial
  deployments

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An Aggressive Position

• Why not design systems without sensor data fusion
  on the motes?
• A more aggressive position Why not design an
  architecture that prohibits collaborative data
  fusion on the motes?
• Questions
• How do we design this architecture?
• Will such an architecture perform well?

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Tiered Sensor Networks
Masters 32-bit CPUs (e.g. PC, Stargate) Higher-ban
dwidth radios Larger batteries or powered

• Real world deployments at,
• Great Duck Island (UCB, Szewczyk,04),
• James Reserve (UCLA, Guy,06),
• Exscal project (OSU, Arora,05),

Provide greater network capacity, larger spatial
reach
Future large-scale sensor network deployments
will be tiered
Motes Low-power, short-range radios Contain
sensing and actuation
Enable flexible deployment of dense
instrumentation
Many real-world sensor network deployments are
tiered
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Tenet Principle
Multi-node data fusion functionality and
multi-node application logic should be
implemented only in the master tier. The cost and
complexity of implementing this functionality in
a fully distributed fashion on motes outweighs
the performance benefits of doing so.
Aggressively use tiering to simplify system !
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Tenet Architecture
Applications run on masters, and masters task
motes
Motes process data,
and may return responses
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What do we gain ?

• Simplifies the application development
• Application writers do not need to write or debug
  embedded code for the motes
• Applications run on less-constrained masters
• Enables significant code re-use across
  applications
• Simple, generic, and re-usable mote tier
• Multiple applications can run concurrently with
  simplified mote functionality
• Robust and scalable network subsystem
• Networking functionality is generic enough to
  support various types of applications which
  improves network re-usability

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Challenges

• More bits communicated than necessary?

• Communication over longer hops?

The cost will be small

• In most deployments, there is a significant
  temporal correlation
• Mote-local processing can achieve significant
  compression
• but little spatial correlation
• Little additional gains from mote tier fusion
• Mote-local processing provides most of the
  aggregation benefits.

• Not an issue
• Typically the diameter of the mote tier will be
  small
• Can compensate by more aggressive processing at
  the motes

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System Overview
Tenet System
Tasking Subsystem
Networking Subsystem
Tasking Language
Task Parser
Tasklets and Runtime
Reliable Transport
Routing
Task Dissemination
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Tasking Subsystem

• Goals
• Flexibility Allow many applications to be
  implemented
• Efficiency Respect mote constraints
• Generality Avoid embedding application-specific
  functionality
• Simplicity Non-expert users should be able to
  develop applications

Tenet System
Tasking Subsystem
Networking Subsystem
Tasking Language
Task Parser
Tasklets and Runtime
Reliable Transport
Routing
Task Dissemination
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Tasking Language

• Linear data-flow language allowing flexible
  composition of tasklets
• A tasklet specifies an elementary sensing,
  actuation, or data processing action
• Tasklets can have several parameters, hence
  flexible
• Tasklets can be composed to form a task
• Sample(500ms, REPEAT, ADC0, LIGHT) ? Send()
• No loops, branches eases construction and
  analysis
• Not Turing-complete aggressively simple, but
  supports wide range of proposed applications

Data-flow style language natural for sensor data
processing
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Classes of Tasklets

• System
• Reboot
• Get
• Send
• Task Manipulation
• Issue (Wait, Alarm)
• DeleteTaskIf
• DeleteActiveTaskIf
• Sensor/Actuator
• Sample
• Actuate

• Data Manipulation
• Arithmetic
• Comparison
• Logical
• Bitwise
• Statistics
• DeleteAttributeIf
• Miscellaneous
• Storage
• Count / Constant

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Task Compositions

• CntToLedsAndRfm
• SenseToRfm
• With time-stamp and seq. number
• Get memory status for node 10
• If sample value is above 50, send sample data,
  node-id and time-stamp

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How many Tenet tasks can a mote run at the same
time?
Network Monitoring

(on Tmote Sky motes)
Memory is the constraint, and task complexity
matters!
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Overview of Networking Subsystem

• Goals
• Scalability and robustness
• Generality Should support diverse application
  needs

Tenet System
Tasking System
Networking Subsystem
Tasking Language
Task Parser
Tasklets and Runtime
Reliable Transport
Routing
Task Dissemination
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Task Dissemination

• Reliably flood task from any master to all motes
• Uses the master tier for dissemination
• Per-active-master cache with LRU replacement and
  aging

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Reliable Transport

• Delivering task responses from motes to masters
• Works transparently across the two tiers
• Three different types of transport mechanism for
  different application requirements
• Best-effort delivery (least overhead)
• End-to-end reliable packet transport
• End-to-end reliable stream transport

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Putting it all together
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Application Case Study PEG

• Goal
• Compare performance with an implementation that
  performs in-mote multi-node fusion
• Pursuit-Evasion Game
• Pursuers (robots) collectively determine the
  location of evaders, and try to corral them.

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Mote-PEG vs. Tenet-PEG
Leader Election
Evader Detected
Evader Detected
Leader
Evader
Evader
Pursuer
Pursuer
Re-task the motes
Task the motes
Mote-PEG
Tenet-PEG
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PEG Results-1
Comparable positional estimate error
Comparable reporting message overhead
Error in Position Estimate
Reporting Message Overhead
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PEG Results-2
A Tenet implementation of an application can
perform as well as an implementation with
in-mote collaborative fusion
Latency is nearly identical
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Real-world Tenet deployment on Vincent Thomas
Bridge
Ran successfully for 24 hours 100 reliable data
delivery Deployment time 2.5 hours Total sensor
data received 860 MB
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Interesting Observations
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Related Work

• Architecture
• SNA Culler,05, Polastre,05, Internet
  Saltzer, 84,
• Virtual Machine
• Mate Levis,02,
• Tasking Library
• VanGo Greenstein,06, SNACK Greenstein,04
• Task Dissemination
• Deluge Hui,04, Trickle Levis,04,
• Reliable Transport
• RMST Stann,03, Wisden Xu,04,
• Routing
• ESS Guy,06, Centroute Stathopoulos,05,

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Conclusion
Applications
Simplifies application development
Simple, generic and re-usable system
Networking Subsystem
Robust and scalable network
Tasking Subsystem
Re-usable generic mote tier
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Software Available Soon

• Master tier
• Cygwin
• Linux Fedora Core 3
• Stargate
• Mote tier
• TelosB
• MicaZ

http//tenet.usc.edu
Thank you!