the tenet architecture for tiered sensor networks o. gnawali, b. greenstein, k-y. jang, a. joki, j....

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, UCLASenSys 2006

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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 high-

confidence 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

Tasks are dynamically allocated Active Containers hold task data

Cloned when a tasklet repeats

Attributes are 3-tuples:<tag, length, value>

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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

1. Take 10 samples, timestamp it2. classify as interesting if 3 or more samples >

453. calculates the deviation from the running mean4. displays the sample on the LEDs5. 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

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 Actuation Mote-tier storage Bounded-latency communication

Long term 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