基于线性回归的无线传感器网络分布式数据采集优化策略

事件监测是无线传感器网络中最重要的应用之一,部署在监测区域内的传感器节点通过对感知数据信息的采集、处理和传输等基本操作完成具体的监测任务,在各种操作中,节点之间的数据传输是最消耗能量的.为了减少节点之间的通信数据量,达到降低网络能耗和延长网络生命周期的目的,该文提出了一种能量高效的基于线性回归的无线传感器网络分布式数据采集优化策略,通过应用线性回归分析方法构建感知数据模型,保持感知数据的特征,使节点仅传输回归模型的参数信息,代替传输实际监测的感知数据信息.仿真实验结果表明,文中提出的数据采集优化策略能通过较小的通信量有效地实现事件监测区域感知数据的预测和估计,降低网络的总能量消耗,延长网络的生命周期.

Linear Regression Based Distributed Data Gathering Optimization Strategy for Wireless Sensor Networks

Event detection is one of the most critical applications in wireless sensor networks(WSN).For implementing the event detection task,the sensor nodes deployed in monitoring region are capable of gathering data,processing data,transmitting data to sink node and so on.For such sensors,transmission is much more energy consuming than computation.Therefore,the amount of sensory data communication overhead should be kept as low as possible,in order to prolong the lifetime of wireless sensor networks and reduce energy consumption.In this paper,an energy-efficient linear-regression-based distributed data gathering optimization strategy is proposed.The linear regression model can accurately represent the feature of the original monitoring data.Rather than transmitting measurements to another node,nodes transfer constraints on the model parameters,drastically reducing the communication required.The theoretical analysis and experimental results show that the proposed strategy is able to implement measurements prediction and estimate with lower communication cost.The designed algorithm achieves more energy savings and extends the wireless sensor networks lifetime.