Energy-Aware Measurement Scheduling in WSNs Used in AAL Applications
Energy-Aware Measurement Scheduling in WSNs Used in AAL Applications
复制标题
DOI：
10.1109/tim.2012.2234598
复制
发表时间：
2013-05-01
影响因子：
5.6
通讯作者：
Pataki, Bela
中科院分区：
文献类型：
--
作者：
Gyoerke, Peter;Pataki, Bela
在为环境辅助生活应用而开发的无线传感器网络中，所需电源的供应是最具挑战性的问题之一。电池有明显的缺点，在某些情况下，电池的更换是不可能的（空间，感染区域等）。我们从两个方向来解决这个问题：1)传感器节点运行的能量应该从环境中获取，2)节点应该尽可能高效地工作。提出了一种新的方法，通过调度测量和传感器，在保持系统性能的同时优化整个网络的能源需求。考虑到物理变量的测量和信息的传输都有不同的成本。系统中传感器和测量间隔的选择基于分配给每个传感器的成本，其中考虑1)基于过去测量和模型的观察变量的估计状态，2)传感器的实际能量状态，以及3)可能影响能量水平和/或观察变量的未来事件。隐马尔可夫模型用于分配待观察的未知变量状态的概率。状态转换的概率由学习过程指定。然后，应用定义的成本函数计算每个传感器的成本，将成本最小的传感器配置为更频繁的测量以确保精度，而将其他传感器配置为较不频繁的测量以节省能源。
In wireless sensor networks developed for ambient assisted living applications, the supply of the required power is one of the most challenging problems. Batteries have remarkable drawbacks, and in some cases, the change of batteries is impossible (space, infected area, etc.). We approached the problem from two directions: 1) The energy for the sensor node's operation should be harvested from the environment, and 2) the nodes should work as efficiently as possible. A new method is presented, which optimizes the whole network energy demand while maintaining the performance of the system, with the scheduling of the measurements and sensors. It is taken into account that both the measurement of a physical variable and the transmission of a message have different costs. Selection of sensors and measurement intervals in the system is based on a cost assigned to each sensor, which considers 1) the estimated state of the observed variable based on the past measurements and a model, 2) the actual energy state of the sensor, and 3) the possible future events that will affect the energy levels and/or the observed variable. A hidden Markov model is used to assign probabilities to the states of the unknown variables, which are to be observed. The probabilities of the state transitions are specified by a learning process. Then, a defined cost function is applied to calculate the cost of each sensor, the sensors with the minimal cost will be configured for more frequent measurements ensuring precision, and the others will be configured to less frequent measurements to save energy.