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摘要
In internet of things (IoT) study, to determine that the location of an event is the key issue, realize that a target location in IoT is one of the research hotspots by using the multihop range-free method. Multihop range-free could obtain relatively reasonable location estimation in the isotropic network, however, during the practical application, it tends to be affected by various anisotropic factors such as the electromagnetic interference, barriers and network attack, which can significantly reduce its performance. In accordance with these problems faced by multihop range-free, this paper proposes a novel IoT localization method: location estimation-kernel partial least squares (LE-KPLS). First of all, this method uses kernel function to define the connectivity information (hop-counts) between nodes, then, the maximum covariance is used to guide and build the inter-node localization model, and then, this model and the hop-counts between the unknown nodes and beacons are used to estimate the coordinate of the unknown nodes. Compared to the existing methods, the LE-KPLS has a high localization precision, great stability and strong generalization performance, without having a high requirement of the number of beacons, and it can well adapt to numerous complicated environments.
- 出版日期2016-3
- 单位东南大学; 金陵科技学院
