Edge Mining the Internet of Things
Edge Mining the Internet of Things
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DOI：
10.1109/jsen.2013.2266895
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发表时间：
2013-10-01
影响因子：
4.3
通讯作者：
Rednic, Ramona
中科院分区：
文献类型：
--
作者：
Gaura, Elena I.;Brusey, James;Rednic, Ramona
本文探讨了边缘挖掘的好处--在位于物联网边缘点的无线、电池供电和智能传感设备上进行的数据挖掘。通过本地数据减少和转换，边缘挖掘可以量化地减少必须发送的数据包数量，减少能源使用和远程存储需求。此外，边缘挖掘有可能通过在传感点嵌入信息需求来降低个人隐私的风险，限制不适当的使用。边缘挖掘的好处进行检查方面的三个具体的算法：线性西班牙查询协议（L-SIP），ClassAct，和裸必需品（BN），这是所有的实例一般SIP。一般来说，边缘挖掘提供的好处与数据流的可预测性和精确信息需求的可用性有关;结果显示，L-SIP通常将数据包传输减少约95%（20倍），BN将数据包传输减少99.98%（5000倍），ClassAct将数据包传输减少99.6%（250倍）。虽然由于其他开销，能源减少并不那么彻底，但这些开销的最小化可以使L-SIP的电池寿命延长10倍。这些结果证明了边缘挖掘对许多物联网应用的可行性的重要性。
This paper examines the benefits of edge mining-data mining that takes place on the wireless, battery-powered, and smart sensing devices that sit at the edge points of the Internet of Things. Through local data reduction and transformation, edge mining can quantifiably reduce the number of packets that must be sent, reducing energy usage, and remote storage requirements. In addition, edge mining has the potential to reduce the risk in personal privacy through embedding of information requirements at the sensing point, limiting inappropriate use. The benefits of edge mining are examined with respect to three specific algorithms: linear Spanish inquisition protocol (L-SIP), ClassAct, and bare necessities (BN), which are all instantiations of general SIP. In general, the benefits provided by edge mining are related to the predictability of data streams and availability of precise information requirements; results show that L-SIP typically reduces packet transmission by around 95% (20-fold), BN reduces packet transmission by 99.98% (5000-fold), and ClassAct reduces packet transmission by 99.6% (250-fold). Although energy reduction is not as radical because of other overheads, minimization of these overheads can lead up to a 10-fold battery life extension for L-SIP, for example. These results demonstrate the importance of edge mining to the feasibility of many IoT applications.