nano-rk: an energy-aware resource centric rtos for sensor networks anand eswaran, anthony rowe and...

Nano-RK: An Energy-Aware Resource Centric RTOS for

Sensor Networks

Anand Eswaran, Anthony Rowe and Raj Rajkumar

Presented by: Ravi Ramaseshan

Nano-RK: An Energy-Aware Resource Centric RTOS for Sensor Networks 2

Design GoalsMotivation

Architecture

Implementation

Sample Application

Future Work

Contributions

• Small Footprint• Battery Lifetime Requirements• Networking Stack Support

• Classical OS Multitasking• Unified Sensor Interface Abstraction

• Priority-based Preemptive Scheduling• Timeliness and Schedulability• Enforcement of Resource Usage Limits


The Nano-RK ArchitectureMotivation

Architecture

Implementation

Sample Application

Future Work

Contributions

• Static Approach

– OS & application co-located in a single address space.

– Admission control and schedulability analysis tests done offline.



Architecture

Implementation

Sample Application

Future Work

Contributions

• The Reservation Paradigm

The following are specified per task:– CPU Reservations– Sender/Receiver Bandwidth Reservations– Sensor/Actuator Reservations



Architecture

Implementation

Sample Application

Future Work

Contributions

• Power Awareness Support

– Energy consumed by a task is the sum of:• CPU energy• Radio energy• Sensor/Actuator energy

– Virtual Energy Reservations• (CPU, Network, Sensor)• Tweak parameters at pre-deployment stage.



Architecture

Implementation

Sample Application

Future Work

Contributions

• Socket Abstraction and Support

– High-level socket like abstraction– Following are handled by Nano-RK:

• Populating application buffers on receipt of packets

• Routing table data structures• Destination look-up functions• One-hop transmission of packets

– Aggregate packets


The Nano-RK ImplementationMotivation

Architecture

Implementation

Sample Application

Future Work

Contributions

• Hardware and Sensor Support

– Atmel ATMEGA128-based sensor node called Firefly build at CMU.

– Provides sensor system calls reading raw sensor data and converting it to meaningful units.

– Functions are atomic and reservations are updated on calls.



Architecture

Implementation

Sample Application

Future Work

Contributions

• Task Management and Scheduling

– Task Control Block (TCB)Register context, priority, period, (CPU, Network,

Sensor) reservation-tuple, port identifiers

– Semaphores and mutexes for task synchronization

• Priority ceilings for mutexes

– Priority-based preemptive solution• PCEP protocol resource allocation protocol



Architecture

Implementation

Sample Application

Future Work

Contributions

• Reservation Support

– Reservation Policy• Hard• Soft

– CPU: Cycles used by task– Network: Bytes sent / received– Sensors: Number of reads

Resource reservation exhausted

Slack

Task suspended

Slack

Task Suspended

Slack

Reservation Replenished

Reservation Replenished

Next Period



Architecture

Implementation

Sample Application

Future Work

Contributions

• Network Stack

– Lightweight network protocol– Allows port based communication– Tightly integrated with OS allowing:

• Automatic packet aggregation• Network reservation• Buffer management policies


Application using Nano-RK APIsnrk_task_type Task1

Task1.task = Sound_Task;

Task1.TaskID = 1;

Task1.priority = 3;

Task1.Period = 10;

Task1.set_cpu_reserve = 5;

Task1.set_network_reserve = 3;

Task1.set_sensor_reserve = 3;

nrk_activate_task (Task1);

Motivation

Architecture

Implementation

Sample Application

Future Work

Contributions

void Sound_Task () {

int prev_sound, sound;

char tx_buff[1];

nrk_port_des my_port;

port_des =

nrk_port (tx_buff, 1, 0);

nrk_connect (port_des, -1);

while (1) {

sound = read_sensor (MIC);

tx_buff[0] = sound;

nrk_port_send (my_port);

wait_until_send ();

prev_sound = sound

nrk_suspend_task ();

}

}


Future WorkMotivation

Architecture

Implementation

Sample Application

Future Work

Contributions

• Support end-to-end deadline guarantees for packet delivery

• Routing based on TDMA using global time synchronization

• Dynamic Energy-efficient routing schemes


ContributionsMotivation

Architecture

Implementation

Sample Application

Future Work

Contributions

• Classical structured multitasking OS– Allows sensor application developers to work in a

familiar paradigm resulting in short learning curves and quicker application development times.

• API support for:– Task management– Synchronization– IPC and high level network abstractions

• Reservation based approach to provide bounds on timeliness & QoS of node life.


Comparison with TinyOS

• The TinyOS is less intuitive for application developers.

• TinyOS has a smaller memory footprint.• Tasks cannot be preempted in TinyOS.• Real Time Embedded Operating System?

– No priority based scheduling policy– No resource allocation policy

• Design objective: Flexibility and accelerate innovation.

Motivation

Architecture

Implementation

Sample Application

Future Work

Contributions

Comparison

nano-rk: an energy-aware resource centric rtos for sensor networks anand eswaran, anthony rowe and...

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