Transcript Tenet-seminar - Washington University in St. Louis

The Tenet Architecture for Tiered
Sensor Networks
O. Gnawali, B. Greenstein, K-Y. Jang, A. Joki, J.
Paek, M. Viera, D. Estrin, R. Govindan, E. Kohler
USC, UCLA
SenSys 2006
The Tenet Two-Tier Architecture
 Motes and Masters
 Multi-node data fusion done on masters
 Masters program motes using tasks
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Example Task
 Notify application when temperature > 50F
 A task contains an arbitrary number of tasklets linked
together.
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Efficiency Costs
 Opportunity cost of multi-mote data fusion

Motes can still fuse locally-generated data
•
Sensor data have high temporal but low spatial redundancy
 More data routed to the masters

A well-designed WSN will have a small diameter
 Higher congestion

Application parameters can be tuned, e.g., only highconfidence pursuers report to masters
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Five Design Principles
 Asymmetric Task Communication

Master send mote tasks, mote send master reply, mote
cannot initiate tasks (no inter-mote communication)
 Addressability

Masters can talk to each other, any master can talk to any
mote, a mote can reply to its tasking master
 Task Library

Each task is a subset of a mote’s generic functionality
 Robustness

Resilience to extensive network failures
 Manageability

Tools must offer useful insight into network failures
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Tenet Task and Task Library
 Focus on simplicity rather than expressiveness
 A task is composed of tasklets, which are
parameterized services
 Linear composition
 Tasklets maximize flexibility while remaining simple
 Each task has a unique ID, a list of tasklets, and their
parameters
 Task library composed at compile-time due to TinyOS
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Tasklets
 Can be composed into a wide range of tasks
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Task Data Structure
Attributes are 3-tuples:
<tag, length, value>
 Tasks are dynamically allocated
 Active Containers hold task data

Cloned when a tasklet repeats
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The Mote Runtime
 Task-aware queues used by services (e.g., wait)
 Tenet scheduler operates at tasklet-level granularity

Allows multiple tasks to execute concurrently
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Three Task Operations
 Installation

Receive a task with a new ID
 Modification

Receive a task with an existing ID and a body
 Deletion


Receive a task with an existing ID and no body
All active containers associated with a task are destroyed
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Example Tasks
 Blink
 CntToLedsAndRfm
 Ping and MeasureHeap
 SenseToRfm
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Data Fusion Example
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Take 10 samples, timestamp it
classify as interesting if 3 or more samples > 45
calculates the deviation from the running mean
displays the sample on the LEDs
sends the statistic, timestamp, and sample if
interesting
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Network Subsystem Requirements
 Must support different applications on tiered networks
 Routing must be robust and scalable



Master-to-mote
Mote-to-master
Small memory footprint
 Tasks must be reliably disseminated from any master
to all motes
 Results must be delivered with end-to-end reliability
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Addressing and Routing
 Every mote and master has a globally unique 16-bit
address


Motes use TinyOS address
Masters use last 16-bits of IP address
 Master-to-master: IP routing
 Mote-to-master: tiered routing


First route to nearest master, then to destination master
Use standard WSN tree-routing protocol like MintRoute
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Tiered Task Dissemination
 Reliably floods tasks to all motes

Partial network re-tasking achieved using a predicate tasklet
 Implemented in a generic packet flooding protocol
called TRD


Reliably floods packets to all nodes (both motes and masters)
Based on beaconing
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Reliable Transport
 Transmits responses from motes to masters
 Three types
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Best effort
Reliable transactional
Stream transport for high data rate applications
 All use hop-by-hop retransmissions
 The reliable protocols use a simplified version of TCP
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Summary of Novel Networking Mech.
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Evalution: Concurrency
 How many tasks can a tmote support at once?
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Execution Time
 Most CPU-intensive tasklet, GatherStatistics, can
process 1200 samples in 14.8ms
 CPU-bound max sampling rate is 81,000 samples per
second
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Application: PEG
 PEG = Pursuit-Evasion Game

One or more pursuers collaborate to corral one or more
evaders
 Use WSN to help pursuers detect non-line-of-sight
evaders
 Native implementation uses a leader


Multiple nodes sense the evader, leader fuses the data
Stress tests Tenet (no mote-level fusion)
 Tenet implementation adjusts the detection threshold
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PEG Experimental Setup


56 tmotes, 6 stargates
Simplifications


Evader detected using RSSI
Radio transmit power limited to achieve multihop
•

9-hop diameter
One evader, one stationary pursuer on central master
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PEG Evaluation
 Tenet has higher accuracy but higher latency
 Tenet has lower message overhead
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Vibration Monitoring Case Study
 Tenet used to implement Wisden
•DetectOnSet reduces
network traffic
•Tenet simplifies
programming
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Manageability
 The following task can be used to capture the routing
trees:
 This can be used to evaluate the task dissemination
latency:
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Robustness
 Failure of a master forces routing algorithm to adjust
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Future Work
 Near term

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Actuation
Mote-tier storage
Bounded-latency communication
 Long term



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Impact of disconnection due to mobility
Authenticity
Data Integrity
Multi-user control and resource management
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Conclusion
 Tenet simplifies programming while not significantly
increasing overhead
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Application
 Pursuit-Evasion


Pursuer mobile robots chase after evader robots with the
help of a sensor network
Traditional implementation employs mote-tier data
aggregation to reduce redundant evader reports
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