Sensor Search Based on Sensor Similarity Computing in the Internet of Things
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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。Abstract: 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.
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表 1 数据存储开销分析
传感器1 传感器20 数据个数统计 原数据
(时间，传感器值)1317×2 2059×2 6.752×103 FUZZY算法
(传感器平均数据密度函数)16×4×10 20×4×10 2.400×103 FUZZY算法
(传感器平均数据斜率密度函数)10×4×10 14×4×10 最小二乘多项式拟合算法
(传感器函数系数)9 9 1.800×10 PLSS算法
(传感器函数系数)16 25 4.100×10 
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