无线传感器网络中的数据传输精简算法  

Lightweight and robust data reduction algorithms for Wireless Sensor Network

作　　者：魏煜 嵩天[1] 

机构地区：[1]北京理工大学计算机学院,北京100081

出　　处：《计算机工程与应用》2018年第3期100-108,共9页Computer Engineering and Applications

基　　金：国家自然科学基金(No.61672101;No.U1636119;No.61272510)

摘　　要：在部署无线传感器网络的相关应用中,由于无线带宽、计算能力、电池能源和意外干扰等限制,通讯环境十分严峻。为了能减少数据传输量并较为精确地由源端传感器向汇聚节点(sink)传输数据,已有方法提出只向sink节点传输无法预测的数据。然而,很少有算法研究在这种严峻的环境中,丢包对数据精简的影响。基于线性预测模型和Heartbeat机制提出LRPH算法来抵制丢包带来的影响,并且及时监测传感器是否故障。另外,提出LRSH算法来优化LRPH,减少冗余信息。实验结果表明LRPH方式可以在一定的误差阈值内,通过只传输4.15%的数据来预测所有的数据。而LRSH只需要3.63%的传输。同已有的一些方法相比,这两种算法都可以在条件严峻的通讯环境下,有效地抑制丢包带来的影响。Wireless Sensor Networks applications are frequently deployed in severe and restricted communication scenarios because of limited wireless bandwidth, computing, batter power and unexpected noises. To efficiently and accurately transfer collected data from sensors to the sink, data reduction algorithms are proposed to decrease the transmission by sending those readings that deviate from the prediction by an error budget. However, few such algorithms consider the effect of packet lose in practical scenarios. In this paper, LRPH(Lightweight Robust algorithm with Plain Heartbeat)is proposed, which works on a lightweight prediction model and a heartbeat mechanism to resist the influence caused by packet loss and to be aware of the state of sensors in time. Furthermore, LRSH(Lightweight Robust algorithm with Smart Heartbeat)as an optimization is proposed to reduce the heartbeat cost in LRPH. Experiments with real traces show that the LRPH approach only requires 4.15% transmission to recover all data by a prediction model within an error threshold,and furthermore, the LRSH approach requires 3.63%. Both two algorithms can significantly resist packet loss in severe communication scenarios comparing with previous approaches.

关 键 词：无线传感器网络 数据精简 鲁棒性 轻量级 

分 类 号：TN911.72[电子电信—通信与信息系统]