Next Article in Journal
One Single Molecule as a Multifunctional Fluorescent Probe for Ratiometric Sensing of Fe3+, Cr3+ and Colorimetric Sensing of Cu2+
Next Article in Special Issue
LIDAR Developments at Clermont-Ferrand—France for Atmospheric Observation
Previous Article in Journal
Application of Gas Sensor Arrays in Assessment of Wastewater Purification Effects
Previous Article in Special Issue
High Frequency Amplitude Detector for GMI Magnetic Sensors
Article Menu

Export Article

Open AccessArticle
Sensors 2015, 15(1), 22-48; doi:10.3390/s150100022

Memory and Energy Optimization Strategies for Multithreaded Operating System on the Resource-Constrained Wireless Sensor Node

1
LIMOS Laboratory, CNRS UMR 6158, Blaise Pascal University, Les Cézeaux, BP 10125, Clermont-Ferrand 63173, France
2
Internet and Information Technology Laboratory, Electronic Information School, Wuhan University, Road LuoJia, Wuhan 430072, China
3
School of Electrical & Information, Hubei University of Automotive Technology, Shiyan 442002, China
*
Authors to whom correspondence should be addressed.
Received: 3 November 2014 / Accepted: 10 December 2014 / Published: 23 December 2014
(This article belongs to the Special Issue State-of-the-Art Sensors Technology in France)

Abstract

Memory and energy optimization strategies are essential for the resource-constrained wireless sensor network (WSN) nodes. In this article, a new memory-optimized and energy-optimized multithreaded WSN operating system (OS) LiveOS is designed and implemented. Memory cost of LiveOS is optimized by using the stack-shifting hybrid scheduling approach. Different from the traditional multithreaded OS in which thread stacks are allocated statically by the pre-reservation, thread stacks in LiveOS are allocated dynamically by using the stack-shifting technique. As a result, memory waste problems caused by the static pre-reservation can be avoided. In addition to the stack-shifting dynamic allocation approach, the hybrid scheduling mechanism which can decrease both the thread scheduling overhead and the thread stack number is also implemented in LiveOS. With these mechanisms, the stack memory cost of LiveOS can be reduced more than 50% if compared to that of a traditional multithreaded OS. Not is memory cost optimized, but also the energy cost is optimized in LiveOS, and this is achieved by using the multi-core “context aware” and multi-core “power-off/wakeup” energy conservation approaches. By using these approaches, energy cost of LiveOS can be reduced more than 30% when compared to the single-core WSN system. Memory and energy optimization strategies in LiveOS not only prolong the lifetime of WSN nodes, but also make the multithreaded OS feasible to run on the memory-constrained WSN nodes. View Full-Text
Keywords: memory optimization; energy conservation; operating system; wireless sensor network; multi-core memory optimization; energy conservation; operating system; wireless sensor network; multi-core
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Liu, X.; Hou, K.M.; de Vaulx, C.; Xu, J.; Yang, J.; Zhou, H.; Shi, H.; Zhou, P. Memory and Energy Optimization Strategies for Multithreaded Operating System on the Resource-Constrained Wireless Sensor Node. Sensors 2015, 15, 22-48.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top