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Related Experiment Videos

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

Marina Zapater¹, Cesar Sanchez, Jose L Ayala

¹CEI Campus Moncloa, UCM-UPM, Madrid 28040, Spain. marina@die.upm.es

Sensors (Basel, Switzerland)

|November 1, 2012

Summary

This study introduces an energy-saving method for smart environments by reallocating low-demand tasks from powerful data centers to less utilized sensor network nodes, reducing overall energy consumption and execution time.

Keywords:

ambient intelligence data centers green computing heterogeneous systems high performance computing smart cities ubiquitous sensor network

Related Experiment Videos

Area of Science:

Computer Science
Ubiquitous Computing
Green Computing

Background:

Smart city and ambient intelligence applications require significant computational power, often relying on data centers.
While sensor node energy consumption in WSNs is studied, the high power demands of supercomputing facilities in smart environments are overlooked.
High-performance computing facilities contribute significantly to economic and environmental impact due to substantial energy consumption.

Purpose of the Study:

To propose an energy-minimization technique for workload assignment in ubiquitous sensor network deployments.
To address the energy consumption issue of high-performance facilities in smart environment services.
To optimize resource utilization by redistributing computational tasks.

Main Methods:

Developed an energy-minimization workload assignment technique.
Incorporated heterogeneity and application-awareness into the task allocation strategy.
Redistributed low-demand computational tasks from high-performance facilities to idle, low-to-medium resource nodes within the WSN infrastructure.

Main Results:

The proposed technique successfully reduces energy consumed by the entire infrastructure.
Implementation of non-optimal allocation policies led to a decrease in total execution time.
Demonstrated effective workload redistribution from high-performance computing to WSN nodes.

Conclusions:

The proposed workload assignment technique offers a viable solution for minimizing energy consumption in smart environments.
Heterogeneity and application-aware task redistribution are effective in reducing both energy usage and execution time.
This approach highlights the importance of considering the energy impact of data centers in ubiquitous sensor network applications.