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This is a preprint version of the paper entitled “EWOk: Towards Efficient Multidimensional Compression of Indoor Positioning Datasets”, published in IEEE Transactions on Mobile Computing. Please cite this paper as L. Klus, R. Klus, J. Torres-Sospedra, E. S. Lohan, C. Granell and J. Nurmi, "EWOk: Towards Efficient Multidimensional Compression of Indoor Positioning Datasets," in IEEE Transactions on Mobile Computing, doi: 10.1109/TMC.2023.3277333. DOI 10.1109/TMC.2023.3277333 Publisher Name IEEE License This publication is copyrighted. You may download, display and print it for your own personal use. Commercial use is prohibited. Link to publication https://ieeexplore.ieee.org/document/10128720 Take down policy If you believe that this document breaches copyright, please contact the authors, and we will investigate your claim

Emerging technologies enable massive deployment of wireless sensor networks across many industries. Internet of Things (IoT) devices are often deployed in critical infrastructure or health monitoring and require fast reaction time,reasonable accuracy, and high energy efficiency. In this letter, we introduce a lossy compression method for time-seriesdata, named direct lightweight temporal compression (DLTC), enabling energy-efficient data transfer for power-restricteddevices. Our method is based on the lightweight temporal compression method, targeting further reconstruction errorminimization and complexity reduction. This letter highlights the key advantages of the proposed method and evaluates themethod’s performance on several sensor-based, time-series data types. We prove that DLTC outperforms the consideredbenchmark methods in compression efficiency at the same reconstruction error level.

Ponencia presentada en la 2021 International Conference on Indoor Positioning and Indoor Navigation (IPIN), 29 Nov.-2 Dec. 2021, Lloret de Mar (Spain) Fingerprinting is a widely used technique in indoor positioning, mainly due to its simplicity. Usually, this technique is used with the deterministic k - Nearest Neighbors (k-NN) algorithm. Utilizing a neural network model for fingerprinting positioning purposes can greatly improve the prediction speed compared to the k-NN approach, but requires a voluminous training dataset to achieve comparable performance. In many indoor positioning datasets, the number of samples is only at a level of hundreds, which results in poor performance of the neural network solution. In this work, we develop a novel algorithm based on a transfer learning approach, which combines samples from 15 different Wi-Fi RSS indoor positioning datasets, to train a single convolutional neural network model, which learns the common patterns in the combined data. The proposed model is then fine-tuned to optimally fit the individual databases. We show that the proposed solution reduces the positioning error by up to 25% compared to the benchmark model while reducing the number of outlier predictions.

Crowdsourcing is a modern and growing technique of acquiring large amount of information. This project utilizes the data on mobile connectivity gathered by RTR NetzTest application to evaluate the performance indicators of the network. The software tool capable of assessing the network based on location, operator and other parameters will be created and utilized for benchmarking of the Austrian network operators. The software tool will be able to monitor the individual telecommunication nodes to estimate their performance in time.

Nuclear cells are the devices converting the energy of ionizing radiation into electricity. This project describes the types and characteristics of nuclear cells. It introduces the reader to nuclear cells, their types and applications. Then the betavoltaic nuclear cell and its components are described. As the next step the available materials will be analysed and the measuring will be performed to check the functionality of the principle.