Abstract
Portable meteorological stations are widely applied in environment monitoring systems, but they are always limited in power-supplying due to no cable power, especially in long-term monitoring scenarios. Reducing power consumption by adjusting a suitable frequency of sensor acquisition is very important for wireless sensor nodes. The regularity of historical environment data from a monitoring system is analyzed, and then an optimization model of an adaptive genetic algorithm for environment monitoring data acquisition strategies is proposed to lessen sampling frequency. According to the historical characteristics, the algorithm dynamically changes the recent data acquisition frequency so as to collect data with a smaller acquisition frequency, which will reduce the energy consumption of the sensor. Experiment results in a practical environment show that the algorithm can greatly reduce the acquisition frequency, and can obtain the environment monitoring data changing curve with less error compared with the high-frequency acquisition of fixed frequency.
Highlights
A portable meteorological station (PMS), in brief, is a kind of equipment that can automatically observe ground environment states, such as temperature, humidity, illumination and so on [1].It is mainly used in many fields such as weather forecast [2], environmental monitoring [3] and biometeorology [4]
This paper proposes an adaptive adjustment method of environment monitoring data This paper proposes an adaptive adjustment method of environment monitoring data acquisition strategy for reducing the power consumption of PMSes
This method is based on the acquisition strategy for reducing the power consumption of PMSes
Summary
A portable meteorological station (PMS), in brief, is a kind of equipment that can automatically observe ground environment states, such as temperature, humidity, illumination and so on [1] It is mainly used in many fields such as weather forecast [2], environmental monitoring [3] and biometeorology [4]. An optimal power consumption control method of policy discrete-time queues was proposed in [17] These algorithms consider the real-time state of the sensor and the external environment, so as to change the acquisition frequency of the sensor dynamically. The main contribution of this paper is adjusting acquisition time intervals dynamically for wireless sensors by learning the changing tendency of monitoring data. By learning historical weather data with the genetic algorithm model, the acquisition frequency is dynamically adjusted to reduce power consumption
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