Wifi-based localisation systems have gained significant interest with many researchers proposing different localisation techniques using publicly available datasets. However, these datasets are limited because they only contain Wifi fingerprints collected and labelled by users, and they are restricted to indoor locations.

We have generated the first Wifi-based localisation datasets for a GPS-denied open area. We selected a busy open area at Murdoch University to generate the datasets using so-called “smart bins”, which are rubbish bins that we enabled to work as access points. The data gathered consists of two different datasets. In the first, four users generated labelled WiFi fingerprints for all available Reference Points using four different smartphones. The second dataset includes 2450865 auto-generated rows received from more than 1000 devices.

We have developed a light-weight algorithm to label the second dataset from the first and we proposed a localisation approach that converts the second dataset from asynchronous format to synchronous, applies feature engineering and a deep learning classifier. Finally, we have demonstrated via simulations that by using this approach we achieve higher prediction accuracy, with up to 19% average improvement, compared with using only the fingerprint dataset.

基于 Wifi 的定位系统引起了许多研究人员的极大兴趣，他们提出了使用公开数据集的不同定位技术。但是，这些数据集是有限的，因为它们仅包含用户收集和标记的 Wifi 指纹，并且仅限于室内位置。

我们已经为拒绝 GPS 的开放区域生成了第一个基于 Wifi 的定位数据集。我们在默多克大学选择了一个繁忙的开放区域，使用所谓的“智能垃圾箱”生成数据集，这些垃圾箱是我们启用作为接入点的垃圾箱。收集的数据由两个不同的数据集组成。首先，四个用户使用四个不同的智能手机为所有可用的参考点生成了标记的 WiFi 指纹。第二个数据集包括从 1000 多个设备接收到的 2450865 个自动生成的行。

我们开发了一种轻量级算法来标记第一个数据集的第二个数据集，我们提出了一种定位方法，将第二个数据集从异步格式转换为同步格式，应用特征工程和深度学习分类器。最后，我们通过模拟证明，与仅使用指纹数据集相比，通过使用这种方法，我们实现了更高的预测精度，平均提高了 19%。