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 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

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

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

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

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

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

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

Nano-RK: An Energy-Aware Resource Centric RTOS for Sensor Networks 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 Resource reservation exhausted Slack Task suspended Slack Task Suspended Slack Reservation Replenished Next Period

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

Nano-RK: An Energy-Aware Resource Centric RTOS for Sensor Networks 11 Application using Nano-RK APIs 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 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 (); }

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

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

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