物联网中基于相似性计算的传感器搜索  

Sensor Search Based on Sensor Similarity Computing in the Internet of Things

作　　者：刘素艳 刘元安[1] 吴帆[1,2] 范文浩[1,2] LIU Suyan;LIU Yuanan;WU lean;FAN Wenhaoq(School of Electronic Engineering,Beijing University of Posts and Telecommunications,Beijing 100876,China;Beijing Key Laboratory of Work Safety Intelligent Monitoring,Beijing University of Posts and Telecommunications,Beijing 100876,China)

机构地区：[1]北京邮电大学电子工程学院,北京100876 [2]北京邮电大学安全生产智能监控北京市重点实验室,北京100876

出　　处：《电子与信息学报》2018年第12期3020-3027,共8页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(61272518;61502050);安全生产智能监控北京市重点实验室主任基金(北京邮电大学);广东省‘扬帆计划’引进创新创业团队项目~~

摘　　要：物联网逐渐成为学术界研究的热点领域,无处不在的传感器设备促进了传感器搜索服务的产生。物联网中搜索的强时空性、海量数据的异构性与传感器节点的资源受限性,给物联网搜索引擎高效地查询传感器提出了挑战。该文提出基于传感器定量数值的线性分段拟合相似性(PLSS)搜索算法。PLSS算法通过分段和线性拟合的方法,构建传感器定量数值的相似性计算模型,从而计算传感器的相似度,根据相似度查找最相似的传感器集群。与模糊集(FUZZY)算法和最小二乘法相比,PLSS算法平均查询精度和查询效率较高。与原数据相比,PLSS算法的存储开销至少降低了两个数量级。The Internet of Things （IoT） is becoming a hot research area, and tens of billions of devices are being connected to the Internet which are advancing on the sensor search service. IoT features （searches are strong spatiotemporal variability, limited resources of the sensor, and mass heterogeneous dynamic data） raise a challenge to the search engines for efficiently and effectively searching and selecting the sensors. In this paper, Piecewise-Linear fitting Sensor Similarity （PLSS） search method is proposed. Based on the content values, PLSS calculates the sensor similarity models to search most similarity sensors. PLSS improves the accuracy and efficiency of search compared with FUZZY set algorithm （FUZZY） and least squares method. PLSS storage costs are at least two order of magnitude less than raw data.

关 键 词：物联网 搜索服务 传感器搜索 传感器相似性计算 线性分段拟合 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]