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Energy-Efficient Control with Harvesting Predictions for Solar-Powered Wireless Sensor Networks
Tengyue Zou1, Shouying Lin2, Qijie Feng3
1College of Mechanical and Electronic Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China. zouty@fafu.edu.cn.
Sensors (Basel, Switzerland)
|January 8, 2016
Summary
This study introduces energy harvesting prediction algorithms for wireless sensor networks, enabling nodes to optimize energy use and extend operational lifetime. Environmental shadow detection and intelligent scheduling ensure efficient, uninterrupted monitoring.
Area of Science:
- Environmental Science
- Computer Science
- Electrical Engineering
Background:
- Wireless sensor networks (WSNs) are crucial for outdoor environmental monitoring but face significant energy constraints.
- Rechargeable batteries and solar energy harvesting offer sustainability but suffer from intermittent power supply due to weather dependency.
Purpose of the Study:
- To enhance the energy efficiency and operational lifetime of WSNs for long-term environmental monitoring.
- To develop predictive algorithms for harvested energy to enable intelligent power management.
Main Methods:
- Proposed algorithms for harvested energy prediction using environmental shadow detection.
- Implemented clustering and routing selection methods for optimized data transmission.
- Utilized a Bayesian network for bottleneck warning notifications.
Main Results:
- Experimental validation on a Texas Instruments CC2530 platform demonstrated improved network sustainability.
- The proposed mechanisms effectively managed energy production and residual battery levels.
- Achieved uninterrupted and efficient network activities.
Conclusions:
- Harvested energy prediction and intelligent scheduling significantly improve WSN longevity.
- Optimized data transmission and bottleneck detection further enhance network performance.
- The developed system provides a robust solution for sustainable outdoor environmental monitoring.
