## 3 Asset Localization
### Uw  
Ultra-Wideband (UWB) technologies are widely used in IIoT solutions to provide accurate localization, since UWB--by design--is especially suited to localization purposes.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Application** & **Typical Machine Learning Techniques** & **References** \\ \hline Intrusion detection & Classification on network data (SVM, Bayes networks, decision tree, Random forest, neural network) & [25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42] \\ Authentication & Classification on network data, Clustering Privacy leaking & [25; 26; 27; 28; 29; 30; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42] \\ Data integrity & Latent space methods (Boltzmann-machine, DBN), Classification (Random Forest, SVM) & [55; 56; 57; 58] \\ Availability & Reinforcement learning and Neural networks & [59; 60; 61] \\  & (DBN, autoencoders) & [62; 63; 64] \\ Offload security & Reinforcement learning & [62; 64; 65] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of applications of machine learning techniques in IIoT security and safety.  
Figure 4: Typical use-cases for industrial indoor and outdoor asset tracking and localization. Beyond classical indoor and outdoor use-cases, there are a couple of less know topics, e.g., tracing the food chain or tracking disposable items (icons from Flaticon.com).  
The huge bandwidth (a couple of hundreds megahertz) of UWB makes it possible to apply very short pulses (e.g., 1-2 ns long) which helps to distinguish between the rays in multi-path propagation to provide accurate timestamps for the received packets, resulting in a centimeter-capable localization. The method of location estimation in UWB systems is usually based on the calculation of the distances between anchors and tags (i.e., ranging); based on these calculated data, the geometric problem is solved by optimization. However, multipath propagation can distort the received signal, so errors in the timestamping process result in positioning errors.  
To overcome this issue, ref. [72] investigates decision tree, random forest and kNN (k-nearest neighbour) algorithms to improve the accuracy of localization based purely on the calculated location information. A more sophisticated solution is to use the indicator provided by the receiver which helps to determine whether the timestamp belongs to the real first path (line-of-sight, LOS) or not (non line-of-sight, NLOS). Based on this information, ref. [73] uses a naive Bayes method to infer the improved position for the device.  
In fact, machine learning methods are widely used in UWB systems to classify the reception as LOS or NLOS propagation. These methods are supported by the fact that most UWB chips provide the received channel impulse response (CIR) of the packet. Using CIR, the study in [74] trains a convolutional neural network to classify the reception of a packet as an LOS and NLOS reception, which helps the localization engine to weigh the measurement while calculating the position. Ref [75] also targets the classification of reception; however, it compares three machine learning techniques, namely support vector machines, random forests and dense neural networks. To make use of the temporal behaviour of the channel impulse response, ref. [76] investigates different combinations of convolutional neural networks and recurrent neural networks for CIR classification, showing that a CNN followed by stacked LSTM networks provides the best accuracy. The CIR can be used not only for classification, but for estimating the timestamping error of each of the received packets. Ref [77] uses convolutional networks to predict the error in timestamping based on the CIR of received packets, resulting in a one-order improvement in location accuracy on average compared to the geometric solution. The survey in [78] presents a couple of other UWB-based solutions, although not exclusively in the IIoT environment. A general approach for estimation of the position from CIR using deep learning techniques can be found in [79].