Wireless Sensor Networks Get a Power Boost from Researchers' New Energy-Efficient Charging Algorithm
By figuring out how many devices are in a given cluster, this novel charging approach delivers a 10 percent efficiency gain.
Researchers at Chung-Ang University have developed a system for "energy-efficient adaptive directional charging" that, they say, can provide high-efficiency wireless power to sensor networks — by taking into account the density of sensor nodes in a given area.
"The wireless power transmission using a mobile charger was designed to be an efficient method," project lead Sungrae Cho, professor at the Chung-Ang University's School of Computer Science and Engineering, explains of the thinking behind the work, "but if a directional antenna was not used, this method was power inefficient. Therefore, I started researching to see if there is an efficient way to use it."
The problem: wireless power can be sent single-point or multi-point. When there's only once device to be charged, single-point transmission is the most efficient — but when there are multiple devices in range of a single charging transmitter, multi-point transmission offers higher efficiency. The difficulty: figuring out which to use at a given time.
The solution: an energy-efficient adaptive directional charging algorithm, or EEADC, which can take into account the density of devices within a given area. Clusters of sensor nodes are classified for single- or multi-charging, and directional transmitters configured accordingly — resulting in higher efficiency and a reduction in wasted energy energy, the team claims.
In testing, the algorithm was shown to outperform state-of-the-art wireless charging approaches by 10 percent in terms of overall power consumption and nine percent for charging delay. "Using this algorithm," Cho concludes, "the charging efficiency can be significantly increased by using a directional antenna and a directional beam for charging the sensor node, and sensors located close to each other can be efficiently charged at the same time."
The team's work has been published in the IEEE Internet of Things Journal, under closed-access terms.