Low-Power TinyOS Tuned Processor Platform for Wireless Sensor Network Motes

Files in This Item:
File Description SizeFormat 
Low_Power_TinyOS_Tuned_Processor_Platform_for_Wireless_Sensor_Network_Motes.pdf556.56 kBAdobe PDFDownload
Title: Low-Power TinyOS Tuned Processor Platform for Wireless Sensor Network Motes
Authors: Raval, Rajkumar K.
Fernandez, Carlos H.
Bleakley, Chris J.
Permanent link: http://hdl.handle.net/10197/7123
Date: 3-May-2010
Abstract: In this article we describe a low power processor platform for use in Wireless Sensor Network (WSN) nodes (motes). WSN motes are small, battery-powered devices comprised of a processor, sensors, and a Radio Frequency transceiver. It is expected that WSNs consisting of large numbers of motes will offer long-term, distributed monitoring, and control of real-world equipment and phenomena. A key requirement for these applications is long battery life. We investigate a processor platform architecture based on an application-specific programmable processor core, System-On-Chip bus, and a hardware accelerator. The architecture improves on the energy consumption of a conventional microprocessor design by tuning the architecture for a suite of TinyOS based WSN applications. The tuning method used minimizes changes to the Instruction Set Architecture facilitating rapid software migration to the new platform. The processor platform was implemented and validated in an FPGA-based WSN mote. The benefits of the approach in terms of energy consumption are estimated to be a reduction of 48% for ASIC implementation relative to a conventional programmable processor for a typical TinyOS application suite without use of voltage scaling.
Funding Details: Enterprise Ireland
Science Foundation Ireland
Type of material: Journal Article
Publisher: Association for Computing Machinery (ACM)
Copyright (published version): 2010 ACM
Keywords: Embedded system design;Hardware-software co-design;Wireless Sensor Network;Low power processor
DOI: 10.1145/1754405.1754408
Language: en
Status of Item: Peer reviewed
Appears in Collections:Computer Science Research Collection
CASL Research Collection

Show full item record

SCOPUSTM   
Citations 50

4
Last Week
0
Last month
checked on Jun 22, 2018

Download(s) 50

196
checked on May 25, 2018

Google ScholarTM

Check

Altmetric


This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. For other possible restrictions on use please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.