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

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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
Online since: 2015-09-25T15:48:52Z
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)
Journal: Transactions on Design Automation of Electronic Systems
Volume: 15
Issue: 3
Start page: 23.1
End page: 23.17
Copyright (published version): 2010 ACM
Keywords: Embedded system designHardware-software co-designWireless Sensor NetworkLow power processor
DOI: 10.1145/1754405.1754408
Language: en
Status of Item: Peer reviewed
Appears in Collections:Computer Science Research Collection
CASL Research Collection

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