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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

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Energy Efficient Real-Time Scheduling Using DPM on Mobile Sensors with a Uniform Multi-Cores.

Youngmin Kim¹, Ki-Seong Lee², Chan-Gun Lee³

¹Da Vind College of Genneral Education, Chung-Ang University, Heuksuk-ro 84, Dongjak-gu, Seoul 06974, Korea. e010120302@cau.ac.kr.

Sensors (Basel, Switzerland)

|December 15, 2017

Summary

This study introduces an energy-efficient scheduling method for wireless sensor networks (WSNs) with multi-core processors. The approach optimizes power management to extend sensor node lifetime, outperforming existing methods.

Keywords:

DPM T-Ler plane WSNs energy efficiency mobile sensor real-time scheduling

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Area of Science:

Computer Science
Electrical Engineering
Network Engineering

Background:

Wireless Sensor Networks (WSNs) rely on nodes with limited battery life, making energy efficiency crucial for network longevity.
Traditional power efficiency efforts in WSNs primarily targeted data transmission, neglecting multi-core processor energy consumption.
The increasing prevalence of multi-core processors in sensor nodes necessitates new strategies for reducing power usage.

Purpose of the Study:

To propose an energy-efficient scheduling method specifically designed for uniform multi-core sensor nodes.
To enhance dynamic power management by optimizing task scheduling and processor selection.
To extend the operational lifetime of sensor nodes through improved energy management.

Main Methods:

Extension of the T-Ler plane-based scheduling for global optimal scheduling in multi-core, multi-processor systems.
Development of a processor selection and task-to-processor mapping strategy for efficient dynamic power management.
Experimental validation of the proposed scheduling method against existing approaches.

Main Results:

The proposed energy-efficient scheduling method demonstrates significant improvements in power management for multi-core sensor nodes.
The approach effectively utilizes dynamic power management through optimized processor selection and task mapping.
Experimental results confirm the superior performance of the proposed method compared to current techniques.

Conclusions:

The developed scheduling method is effective in reducing power consumption in multi-core sensor nodes within WSNs.
This work addresses the critical need for energy efficiency in modern, multi-core sensor node architectures.
The proposed approach offers a viable solution for extending the lifetime of WSNs by optimizing energy usage.