基于大偏差的无线传感网中采样速率策略研究（英文）

Adaptive Data Sampling Rate Adjustment Based on Large Deviation Theory in Wireless Sensor Networks

针对具有能量捕获装置以及可充电电池的无线传感网,为了使无线传感器具有根据可用能量自适应调整数据采样速率的能力,所获得的能量具有随机性,定义了可等价表示信息获取度量的能量不足概率.将调节数据采样速率的问题定义为约束优化问题,使数据采样速率最大化,同时将能量不足概率保持在阈值以下.利用经典的大偏差理论估计能量不足概率.实验结果表明,所提出的算法具有自适应能力,既能适应能量动态变化,又能适应信道动态变化,提高了信息采集效率.

We consider the wireless senor network which is equipped with an energy harvesting device and a rechargeable battery,and the aim is to enable the sensor having the ability of adaptively adjusting the data sampling rate according to the available energy.Since the stochastic nature of the harvested energy,we define the energy deficiency probability for equivalently characterizing an information acquisition metric.We formulate the problem of adjusting data sampling rate as a constrained optimization problem,maximizing the data sampling rate while keeping the energy deficiency probability below a threshold.The classic large deviation theory is invoked for estimating the energy deficiency probability.Our experimental results verify that the algorithms proposed have the adaptation capability to accommodate both the energydynamics and the channel-dynamics for improving the information acquisition.