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Ubiquitous green computing techniques for high demand applications in Smart environments.

Zapater M, Sanchez C, Ayala JL, Moya JM, Risco-Martín JL - Sensors (Basel) (2012)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: CEI Campus Moncloa, UCM-UPM, Madrid 28040, Spain. marina@die.upm.es

ABSTRACT
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.

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Energy Optimization System.
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f5-sensors-12-10659: Energy Optimization System.

Mentions: The complete system is described by Figure 5, and works as follows:


Ubiquitous green computing techniques for high demand applications in Smart environments.

Zapater M, Sanchez C, Ayala JL, Moya JM, Risco-Martín JL - Sensors (Basel) (2012)

Energy Optimization System.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3472849&req=5

f5-sensors-12-10659: Energy Optimization System.
Mentions: The complete system is described by Figure 5, and works as follows:

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: CEI Campus Moncloa, UCM-UPM, Madrid 28040, Spain. marina@die.upm.es

ABSTRACT
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.

Show MeSH