Ubiquitous Green Computing Techniques for High Demand Applications in Smart Environments
Ubiquitous sensor network deployments, such as the ones found in Smart cities and Ambient intelligence applications, require constantly increasing high computational demands in order to process data and offer services to users. The nature of these applications imply the usage of data centers. Resear...
Main Authors: | , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2012-08-01
|
Series: | Sensors |
Subjects: | |
Online Access: | http://www.mdpi.com/1424-8220/12/8/10659 |
id |
doaj-424226b3b6784a3e95784a08a48c3bc6 |
---|---|
record_format |
Article |
spelling |
doaj-424226b3b6784a3e95784a08a48c3bc62020-11-25T01:05:28ZengMDPI AGSensors1424-82202012-08-01128106591067710.3390/s120810659Ubiquitous Green Computing Techniques for High Demand Applications in Smart EnvironmentsJose M. MoyaJosé L. Risco-MartínJose L. AyalaCesar SanchezMarina ZapaterUbiquitous sensor network deployments, such as the ones found in Smart cities and Ambient intelligence applications, require constantly increasing high computational demands in order to process data and offer services to users. The nature of these applications imply the usage of data centers. Research has paid much attention to the energy consumption of the sensor nodes in WSNs infrastructures. However, supercomputing facilities are the ones presenting a higher economic and environmental impact due to their very high power consumption. The latter problem, however, has been disregarded in the field of smart environment services. This paper proposes an energy-minimization workload assignment technique, based on heterogeneity and application-awareness, that redistributes low-demand computational tasks from high-performance facilities to idle nodes with low and medium resources in the WSN infrastructure. These non-optimal allocation policies reduce the energy consumed by the whole infrastructure and the total execution time.http://www.mdpi.com/1424-8220/12/8/10659ubiquitous sensor networkgreen computingheterogeneous systemsdata centershigh performance computingsmart citiesambient intelligence |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jose M. Moya José L. Risco-Martín Jose L. Ayala Cesar Sanchez Marina Zapater |
spellingShingle |
Jose M. Moya José L. Risco-Martín Jose L. Ayala Cesar Sanchez Marina Zapater Ubiquitous Green Computing Techniques for High Demand Applications in Smart Environments Sensors ubiquitous sensor network green computing heterogeneous systems data centers high performance computing smart cities ambient intelligence |
author_facet |
Jose M. Moya José L. Risco-Martín Jose L. Ayala Cesar Sanchez Marina Zapater |
author_sort |
Jose M. Moya |
title |
Ubiquitous Green Computing Techniques for High Demand Applications in Smart Environments |
title_short |
Ubiquitous Green Computing Techniques for High Demand Applications in Smart Environments |
title_full |
Ubiquitous Green Computing Techniques for High Demand Applications in Smart Environments |
title_fullStr |
Ubiquitous Green Computing Techniques for High Demand Applications in Smart Environments |
title_full_unstemmed |
Ubiquitous Green Computing Techniques for High Demand Applications in Smart Environments |
title_sort |
ubiquitous green computing techniques for high demand applications in smart environments |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2012-08-01 |
description |
Ubiquitous sensor network deployments, such as the ones found in Smart cities and Ambient intelligence applications, require constantly increasing high computational demands in order to process data and offer services to users. The nature of these applications imply the usage of data centers. Research has paid much attention to the energy consumption of the sensor nodes in WSNs infrastructures. However, supercomputing facilities are the ones presenting a higher economic and environmental impact due to their very high power consumption. The latter problem, however, has been disregarded in the field of smart environment services. This paper proposes an energy-minimization workload assignment technique, based on heterogeneity and application-awareness, that redistributes low-demand computational tasks from high-performance facilities to idle nodes with low and medium resources in the WSN infrastructure. These non-optimal allocation policies reduce the energy consumed by the whole infrastructure and the total execution time. |
topic |
ubiquitous sensor network green computing heterogeneous systems data centers high performance computing smart cities ambient intelligence |
url |
http://www.mdpi.com/1424-8220/12/8/10659 |
work_keys_str_mv |
AT josemmoya ubiquitousgreencomputingtechniquesforhighdemandapplicationsinsmartenvironments AT joselriscomartin ubiquitousgreencomputingtechniquesforhighdemandapplicationsinsmartenvironments AT joselayala ubiquitousgreencomputingtechniquesforhighdemandapplicationsinsmartenvironments AT cesarsanchez ubiquitousgreencomputingtechniquesforhighdemandapplicationsinsmartenvironments AT marinazapater ubiquitousgreencomputingtechniquesforhighdemandapplicationsinsmartenvironments |
_version_ |
1725194314206674944 |