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

1
Nano-RK An Energy-Aware Resource Centric RTOS
for Sensor Networks

• Anand Eswaran, Anthony Rowe and Raj Rajkumar
• Presented by Ravi Ramaseshan

2
Design Goals

• Motivation
• 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

3
The Nano-RK Architecture

• Motivation
• 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.

4
The Nano-RK Architecture

• Motivation
• 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

5
The Nano-RK Architecture

• Motivation
• 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.

6
The Nano-RK Architecture

• Motivation
• 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

7
The Nano-RK Implementation

• Motivation
• 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.

8
The Nano-RK Implementation

• Motivation
• 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

9
The Nano-RK Implementation

• Motivation
• 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

10
The Nano-RK Implementation

• Motivation
• 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

11
Application using Nano-RK APIs
void Sound_Task () int prev_sound,
sound char tx_buff1 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_buff0
sound nrk_port_send (my_port) wait_until_sen
d () prev_sound sound nrk_suspend_task
()

• nrk_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
12
Future Work

• Motivation
• 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

13
Contributions

• Motivation
• 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.

14
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