We outline the problem of concept drifts for time series data. In this work, we analyze the temporal inconsistency of streaming wireless signals in the context of device-free passive indoor localization. We show that data obtained from WiFi channel state information (CSI) can be used to train a robust system capable of performing room level localization. One of the most challenging issues for such a system is the movement of input data distribution to an unexplored space over time, which leads to an unwanted shift in the learned boundaries of the output space. In this work, we propose a phase and magnitude augmented feature space along with a standardization technique that is little affected by drifts. We show that this robust representation of the data yields better learning accuracy and requires less number of retraining. Concept drift is one of the most common problems that degrades the predictive performance of passive WiFi-based localization systems. In most of the predictive models it is assumed that a static relationship between input and output exits. Thus in the context of machine learning, there is a mapping function f (x) = y, where the algorithm tries to estimate the underlying relationship between the input x and the output y. The presence of concept drift means that the accuracy of the predictive models that is trained from historical data degrades over time due to evolving nature of the data. Hence, predictive models often needs to be retrained frequently with a new set of labelled data, which might be expensive to obtain. These pattern changes can be categorized based on their transition speed from one state to another into abrupt, or gradual drifts BID1 . In either case, the deployed solution is expected to diagnose unintended changes automatically and adapt accordingly.The problem of concept drift in WiFi-based localization systems, was first mentioned in BID2 , which presents a technology that utilizes only off-the-shelf WiFi-enabled devices such as access points, laptops, smart TV for passive sensing in the environment of interest. The authors have applied an online semi-supervised approach to automatically detect gradual shifts in the feature space and propose an adaptive learning strategy to regain the prediction accuracy. We aim to address the same problem without making any assumption about the drift type. In this work, we illustrate that from time to time, both sudden and gradual drifts, can occur to the streaming WiFi data, which often hinder the performance of the trained models when tested on the measurements.Majority of the existing WiFi-based indoor localization systems are device-based, where the user's location is determined by a WiFi-enabled target device that needs to be carried by the subject all the time BID9 . Practical challenges of using device-based approaches, impose some restrictions and therefore, a device-free and passive solution is a promising line of research both for academia and industry. For example, (Wang et al., 2015a; b; BID5 , are some of the existing research where device free passive WiFi localization is used along with deep learning. In BID0 , the authors address drifts and the inconsistency of WiFi fingerprints for stationary subjects. However, most of these researches and their experiments were performed in a very controlled environment and within a limited time frames. On the other hand, the effect of concept drift mostly appears over time due to real-world conditions such as natural WiFi channel or bandwidth switches, or when certain exogenous factor such as temperature and humidity changes. Therefore, the existing methods do not address them explicitly and the experimental results does not reflect the performance of the model taken from measurements that are a few days apart. In this paper, we use the idea of feature augmentation in order to include both phase and magnitude of the CSI data. To the best of our knowledge this is the first work that exploits both the phase and magnitude of the CSI in order to construct a feature space that is less affected by drifts. We show that once such a feature space has been constructed,we can use classical machine learning algorithms in order to create a more robust model. In the next sections, we discuss nature of the WiFi CSI data being obtained and how drifts cause a shift in the feature space. In Section 3 we discuss our methods including the phase and the magnitude sanitization procedure. In Section ?? we present the training strategy for off line training and online prediction. Finally in Section 5, we conclude our paper and present discussions on future work. We have presented a comprehensive study in order to handle drifts for WiFi CSI data. We focused on the challenges presented by drifts for the application of indoor localization and proposed a combined feature space that is robust to drifts. We then incorporate this augmented feature space and provided a detailed analysis of the performance of different learning algorithms. Although we mainly focus on off line training, our work also focuses on robust online prediction in the presence of drifts. Such a stable feature space will will mean that we do not have to learn the abrupt and gradual drifts and retrain our models each time when there one. Our proposed feature space will also allow for applying deep convolution neural network, that has been only applied to either the phase or the magnitude information, but not both. The proposed feature space can be projected into an RGB image where, vital information can captured using a convolution layer which we keep for future work.
We introduce an augmented robust feature space for streaming wifi data that is capable of tackling concept drift for indoor localization
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