Location-based services (LBS) have gained increasing importance in our everyday lives and serve as the foundation for many smartphone applications. Whereas Global Navigation Satellite Systems (GNSS) enable reliable position estimation outdoors, there does not exist any comparable gold standard for indoor localization yet. Wireless local area network (WLAN) fingerprinting is still a promising and widely adopted approach to indoor localization, since it does not rely on preinstalled hardware but uses the existing WLAN infrastructure typically present in buildings. The accuracy of the method is, however, limited due to unstable fingerprints, etc. Deep learning has recently gained attention in the field of indoor localization and is also utilized to increase the performance of fingerprinting-based approaches. Current solutions can be grouped into models that either estimate the exact position of the user (regression) or classify the area (pre-segmented floor plan) or a reference location. We propose a model, DeepLocBox (DLB), that offers reliable area localization in multi-building/multi-floor environments without the prerequisite of a pre-segmented floor plan. Instead, the model predicts a bounding box that contains the user’s position while minimizing the required prediction space (size of the box). We compare the performance of DLB with the standard approach of neural network-based position estimation and demonstrate that DLB achieves a gain in success probability by 9.48% on a self-collected dataset at RWTH Aachen University, Germany; by 5.48% for a dataset provided by Tampere University of Technology (TUT), Finland; and by 3.71% for the UJIIndoorLoc dataset collected at Jaume I University (UJI) campus, Spain.

The accuracy of fingerprinting-based indoor localization correlates with the quality and up-to-dateness of collected training data. Perpetual crowdsourced data collection reduces manual labeling effort and provides a fresh data base. However, the decentralized collection comes with the cost of heterogeneous data that causes performance degradation. In settings with imperfect data, area localization can provide higher positioning guarantees than exact position estimation. Existing area localization solutions employ a static segmentation into areas that is independent of the available training data. This approach is not applicable for crowdsoucred data collection, which features an unbalanced spatial training data distribution that evolves over time. A segmentation is required that utilizes the existing training data distribution and adapts once new data is accumulated. We propose an algorithm for data-aware floor plan segmentation and a selection metric that balances expressiveness (information gain) and performance (correctly classified examples) of area classifiers. We utilize supervised machine learning, in particular, deep learning, to train the area classifiers. We demonstrate how to regularly provide an area localization model that adapts its prediction space to the accumulating training data. The resulting models are shown to provide higher reliability compared to models that pinpoint the exact position.

Scalable real-time processing of large amounts of data has become a research topic of particular importance due to the continuously rising amount of data that is generated by devices equipped with sensing components. While existing approaches allow for fault-tolerant and scalable stream processing, we present a pipeline architecture that consists of well-known open source tools to specifically integrate spatiotemporal internet of things (IoT) data streams. In a case study, we utilize the architecture to tackle the online map matching problem, a pre-processing step for trajectory mining algorithms. Given the rising amount of vehicle location data that is generated on a daily basis, existing map matching algorithms have to be implemented in a distributed manner to be executable in a stream processing framework that provides scalability. We demonstrate how to implement state-of-the-art map matching algorithms in our distributed stream processing pipeline and analyze measured latencies.

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Pedestrian localization within large-scale multi-building/multi-floor indoor environments remains a challenging task. Fingerprinting-based approaches are particularly suited for such large-scale deployments due to their low requirements of hardware installments. Recently, the fingerprinting problem has been addressed by deep learning. Existing models are mostly task specific by providing floor classification or position estimation within a small area. A strategy to support localization within large-scale environments is to sequentially apply hierarchical models. This has several drawbacks including missing scalability and increased deployment complexity on smartphones. We propose a unifying approach based on training a single neural network that classifies the building/floor and predicts the position in a single forward-pass of the network. Our model classifies a grid cell and performs within grid cell regression, which solves the performance degradation of applying regression within large areas. To reduce the error in case of misclassified grid cells, we propose a novel technique called multi cell encoding learning (multi-CEL), where a model simultaneously learns several redundant position representations within an overlapping grid cell encoding. For three public WLAN fingerprinting datasets, we demonstrate that multi-CEL surpasses existing state-of-the-art multi-task learning neural networks and even outperforms regression neural networks explicitly trained for 2D-positioning by up to 17%.

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<p>Over 50% of the energy consumption in Europe is used for heating and cooling, of which the majority still comes from fossil fuels. For the heating and cooling supply of buildings, heat pumps using borehole heat exchangers (BHE) are an efficient and sustainable alternative. Design and performance of BHE fields strongly depend on the thermal properties of the subsurface. For the dimensioning of BHE fields, the subsurface properties are assessed using geothermal potential maps published by federal states or authorities.</p><p>Such maps are created based on available geological and hydrogeological subsurface data. From these data, the stratigraphic sequence a BHE would intersect is inferred and thermal properties are assigned to the rock sequence. The mean thermal conductivity of the intersected sequence has a high uncertainty that is not quantified in most geothermal potential estimates. Moreover, geothermal potential maps are often not comparable because the used calculation methods differ in terms of complexity and calculated quantity. In Germany, for example, almost each of the federal states published its own geothermal potential map showing different quantities.</p><p>For these reasons, we propose an approach to estimate the subsurface thermal conductivity together with its uncertainty. To enable applicability to datasets of different regions, e.g., all German federal states, the method takes only publicly available and standardized hydrogeological and geological data as input. Thermal conductivity estimation takes place on</p><p>1) a large scale, to obtain a general estimate of thermal conductivity, and</p><p>2) on a fine scale, to update the large-scale estimate in smaller target areas.</p><p>For both estimations, we consider uncertainty by assigning not only the mean but also the minimum and maximum thermal conductivities of each rock type to the intersected sequence. Data handling and initiation of calculations is done via a geoportal. It connects the calculation method to geodata servers, instead of storing data locally, thus ensuring data up-to-dateness. This way, the approach does not only estimate the thermal conductivity, but also automatizes the cumbersome step of geological data gathering for all types of subsurface investigations.</p><p>We demonstrate the approach using data from the federal state of Hamburg and compare our results to a thermal conductivity map published by the city of Hamburg.  The partly considerable differences between the two estimates illustrate the uncertainty of the geothermal potential, and thus the need to communicate it.</p>

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