大规模输电线路状态监测传感器网络的周期性低功耗通信技术方案

为了实现输电线路监测的功耗低、寿命长、绿色发展的目的，提出大规模输电线路状态监测传感器网络周期性低功耗通信技术方案。依据网络中传感器网络组网特征以及节点运行状态转换特点，设置睡眠定时器，以周期性运行方式使传感器网络通信节点在初始化、睡眠、激活状态间转换，通信节点在输电线路状态监测数据无传递需求时进入睡眠状态，节省通信功耗；传感器网络汇聚（sink）节点利用梯度创建上行路由，通过源路由的方式创建下行路由，以跳数和剩余能量为依据进行上、下行路由数据分组传递，降低节点功耗，延长通信运行时间。实验显示，大规模输电线路状态监测传感器网络应用该技术方案后，通信功耗明显降低，运行时间明显延长，且不会影响监测传感器网络的数据传输性能，延长了监测传感器网络的使用寿命。     

Channel status prediction for cognitive radio networks

The cognitive radio (CR) technology appears as an attractive solution to effectively allocate the radio spectrum among the licensed and unlicensed users. With the CR technology the unlicensed users take the responsibility of dynamically sensing and accessing any unused channels (frequency bands) in the spectrum allocated to the licensed users. As spectrum sensing consumes considerable energy, predictive methods for inferring the availability of spectrum holes can reduce energy consumption of the unlicensed users to only sense those channels which are predicted to be idle. Prediction‐based channel sensing also helps to improve the spectrum utilization (SU) for the unlicensed users. In this paper, we demonstrate the advantages of channel status prediction to the spectrum sensing operation in terms of improving the SU and saving the sensing energy. We design the channel status predictor using two different adaptive schemes, i.e., a neural network based on multilayer perceptron (MLP) and the hidden Markov model (HMM). The advantage of the proposed channel status prediction schemes is that these schemes do not require a priori knowledge of the statistics of channel usage. Performance analysis of the two channel status prediction schemes is performed and the accuracy of the two prediction schemes is investigated. Copyright © 2010 John Wiley & Sons, Ltd.