Adaptive directional charging for wireless sensor networks
Researchers in Korea have developed an energy-efficient adaptive directional charging (EEADC) algorithm that considers the density of sensor nodes to adaptively choose single charging or multicharging.
The EEADC algorithm developed by the team from Chung-Ang University, Korea, significantly outperforms existing methods in terms of power consumption and charging delay.
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Smart factories, vehicles, and cities increasingly use rechargeable wireless sensor networks (WSNs) for communication. A distinct advantage of WSNs is that they can be placed in remote, inaccessible, or even biologically or chemically contaminated areas for communication, surveillance, and reconnaissance in military and environmental applications. However, the potential of these WSNs is restricted by their reliance on the batteries and the replacement cycle.
With wireless charging, the efficiency falls as the charging distance increases. Therefore, single charging is more energy-efficient than multicharging as it can charge a sensor node at a closer range. However, when multiple nodes are present, multicharging may achieve higher efficiency.
The researchers from the School of Computer Science and Engineering at Chung-Ang University developed the algorithm to optimize mobile charging of sensors efficiently through wireless power transmission technology.
“The wireless power transmission using a mobile charger was designed to be an efficient method, 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,” said Professor Sungrae Cho, who led the team.
The energy-efficient adaptive directional charging (EEADC) algorithm adaptively considers the density of sensor nodes to adaptively choose single charging or multicharging. As EEADC dynamically determines the charging strategy based on the charging efficiency, the researchers achieved equal or better charging efficiency than single charging and simultaneously reduced energy waste due to overuse of multicharging.
EEADC employs a mean-shift algorithm considering node density to determine single charging/multicharging clusters that is more efficient than the standard K-Means algorithms employed in most Monte Carlo (MC) clustering methods. Each cluster is classified as a single-charging or multicharging cluster according to the number of sensor nodes it contains.
The charging strategy, which includes the charging point, beam direction, charging power, and charging time, is then determined according to the type of cluster.
For a multicharging cluster, the non-convex optimization problem having multiple feasible regions led the researchers to employ the discretized charging strategy decision (DCSD) algorithm to solve the problem efficiently.
The DCSD divides the problem into two subproblems. The candidate charging points are obtained by solving the first subproblem. Then, DCSD selects the point with the lowest energy consumption among the candidate charging points as the optimal charging point.
The researchers used simulations to compare EEADC to conventional charging methods in practice and showed that EEADC outperformed the existing methods with respect to power consumption and charging delay by 10% and 9%, respectively.
“Using this algorithm, 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,” said Cho.
doi.org/10.1109/JIOT.2022.3163400
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