@techreport{OPUS4-20232, type = {Working Paper}, title = {White Paper on Crowdsourced Network and QoE Measurements - Definitions, Use Cases and Challenges}, editor = {Hoßfeld, Tobias and Wunderer, Stefan}, doi = {10.25972/OPUS-20232}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-202327}, pages = {24}, year = {2020}, abstract = {The goal of the white paper at hand is as follows. The definitions of the terms build a framework for discussions around the hype topic 'crowdsourcing'. This serves as a basis for differentiation and a consistent view from different perspectives on crowdsourced network measurements, with the goal to provide a commonly accepted definition in the community. The focus is on the context of mobile and fixed network operators, but also on measurements of different layers (network, application, user layer). In addition, the white paper shows the value of crowdsourcing for selected use cases, e.g., to improve QoE or regulatory issues. Finally, the major challenges and issues for researchers and practitioners are highlighted. This white paper is the outcome of the W{\"u}rzburg seminar on "Crowdsourced Network and QoE Measurements" which took place from 25-26 September 2019 in W{\"u}rzburg, Germany. International experts were invited from industry and academia. They are well known in their communities, having different backgrounds in crowdsourcing, mobile networks, network measurements, network performance, Quality of Service (QoS), and Quality of Experience (QoE). The discussions in the seminar focused on how crowdsourcing will support vendors, operators, and regulators to determine the Quality of Experience in new 5G networks that enable various new applications and network architectures. As a result of the discussions, the need for a white paper manifested, with the goal of providing a scientific discussion of the terms "crowdsourced network measurements" and "crowdsourced QoE measurements", describing relevant use cases for such crowdsourced data, and its underlying challenges. During the seminar, those main topics were identified, intensively discussed in break-out groups, and brought back into the plenum several times. The outcome of the seminar is this white paper at hand which is - to our knowledge - the first one covering the topic of crowdsourced network and QoE measurements.}, subject = {Crowdsourcing}, language = {en} } @article{KaiserLeschRotheetal.2020, author = {Kaiser, Dennis and Lesch, Veronika and Rothe, Julian and Strohmeier, Michael and Spieß, Florian and Krupitzer, Christian and Montenegro, Sergio and Kounev, Samuel}, title = {Towards Self-Aware Multirotor Formations}, series = {Computers}, volume = {9}, journal = {Computers}, number = {1}, issn = {2073-431X}, doi = {10.3390/computers9010007}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-200572}, pages = {7}, year = {2020}, abstract = {In the present day, unmanned aerial vehicles become seemingly more popular every year, but, without regulation of the increasing number of these vehicles, the air space could become chaotic and uncontrollable. In this work, a framework is proposed to combine self-aware computing with multirotor formations to address this problem. The self-awareness is envisioned to improve the dynamic behavior of multirotors. The formation scheme that is implemented is called platooning, which arranges vehicles in a string behind the lead vehicle and is proposed to bring order into chaotic air space. Since multirotors define a general category of unmanned aerial vehicles, the focus of this thesis are quadcopters, platforms with four rotors. A modification for the LRA-M self-awareness loop is proposed and named Platooning Awareness. The implemented framework is able to offer two flight modes that enable waypoint following and the self-awareness module to find a path through scenarios, where obstacles are present on the way, onto a goal position. The evaluation of this work shows that the proposed framework is able to use self-awareness to learn about its environment, avoid obstacles, and can successfully move a platoon of drones through multiple scenarios.}, language = {en} } @article{DavidsonDuekingZinneretal.2020, author = {Davidson, Padraig and D{\"u}king, Peter and Zinner, Christoph and Sperlich, Billy and Hotho, Andreas}, title = {Smartwatch-Derived Data and Machine Learning Algorithms Estimate Classes of Ratings of Perceived Exertion in Runners: A Pilot Study}, series = {Sensors}, volume = {20}, journal = {Sensors}, number = {9}, issn = {1424-8220}, doi = {10.3390/s20092637}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-205686}, year = {2020}, abstract = {The rating of perceived exertion (RPE) is a subjective load marker and may assist in individualizing training prescription, particularly by adjusting running intensity. Unfortunately, RPE has shortcomings (e.g., underreporting) and cannot be monitored continuously and automatically throughout a training sessions. In this pilot study, we aimed to predict two classes of RPE (≤15 "Somewhat hard to hard" on Borg's 6-20 scale vs. RPE >15 in runners by analyzing data recorded by a commercially-available smartwatch with machine learning algorithms. Twelve trained and untrained runners performed long-continuous runs at a constant self-selected pace to volitional exhaustion. Untrained runners reported their RPE each kilometer, whereas trained runners reported every five kilometers. The kinetics of heart rate, step cadence, and running velocity were recorded continuously ( 1 Hz ) with a commercially-available smartwatch (Polar V800). We trained different machine learning algorithms to estimate the two classes of RPE based on the time series sensor data derived from the smartwatch. Predictions were analyzed in different settings: accuracy overall and per runner type; i.e., accuracy for trained and untrained runners independently. We achieved top accuracies of 84.8 \% for the whole dataset, 81.8 \% for the trained runners, and 86.1 \% for the untrained runners. We predict two classes of RPE with high accuracy using machine learning and smartwatch data. This approach might aid in individualizing training prescriptions.}, language = {en} } @article{KammererPryssHoppenstedtetal.2020, author = {Kammerer, Klaus and Pryss, R{\"u}diger and Hoppenstedt, Burkhard and Sommer, Kevin and Reichert, Manfred}, title = {Process-driven and flow-based processing of industrial sensor data}, series = {Sensors}, volume = {20}, journal = {Sensors}, number = {18}, issn = {1424-8220}, doi = {10.3390/s20185245}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-213089}, year = {2020}, abstract = {For machine manufacturing companies, besides the production of high quality and reliable machines, requirements have emerged to maintain machine-related aspects through digital services. The development of such services in the field of the Industrial Internet of Things (IIoT) is dealing with solutions such as effective condition monitoring and predictive maintenance. However, appropriate data sources are needed on which digital services can be technically based. As many powerful and cheap sensors have been introduced over the last years, their integration into complex machines is promising for developing digital services for various scenarios. It is apparent that for components handling recorded data of these sensors they must usually deal with large amounts of data. In particular, the labeling of raw sensor data must be furthered by a technical solution. To deal with these data handling challenges in a generic way, a sensor processing pipeline (SPP) was developed, which provides effective methods to capture, process, store, and visualize raw sensor data based on a processing chain. Based on the example of a machine manufacturing company, the SPP approach is presented in this work. For the company involved, the approach has revealed promising results.}, language = {en} } @article{KrupitzerEberhardingerGerostathopoulosetal.2020, author = {Krupitzer, Christian and Eberhardinger, Benedikt and Gerostathopoulos, Ilias and Raibulet, Claudia}, title = {Introduction to the special issue "Applications in Self-Aware Computing Systems and their Evaluation"}, series = {Computers}, volume = {9}, journal = {Computers}, number = {1}, issn = {2073-431X}, doi = {10.3390/computers9010022}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-203439}, year = {2020}, abstract = {The joint 1st Workshop on Evaluations and Measurements in Self-Aware Computing Systems (EMSAC 2019) and Workshop on Self-Aware Computing (SeAC) was held as part of the FAS* conference alliance in conjunction with the 16th IEEE International Conference on Autonomic Computing (ICAC) and the 13th IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO) in Ume{\aa}, Sweden on 20 June 2019. The goal of this one-day workshop was to bring together researchers and practitioners from academic environments and from the industry to share their solutions, ideas, visions, and doubts in self-aware computing systems in general and in the evaluation and measurements of such systems in particular. The workshop aimed to enable discussions, partnerships, and collaborations among the participants. This special issue follows the theme of the workshop. It contains extended versions of workshop presentations as well as additional contributions.}, language = {en} } @article{LiGuanGaoetal.2020, author = {Li, Ningbo and Guan, Lianwu and Gao, Yanbin and Du, Shitong and Wu, Menghao and Guang, Xingxing and Cong, Xiaodan}, title = {Indoor and outdoor low-cost seamless integrated navigation system based on the integration of INS/GNSS/LIDAR system}, series = {Remote Sensing}, volume = {12}, journal = {Remote Sensing}, number = {19}, issn = {2072-4292}, doi = {10.3390/rs12193271}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-216229}, year = {2020}, abstract = {Global Navigation Satellite System (GNSS) provides accurate positioning data for vehicular navigation in open outdoor environment. In an indoor environment, Light Detection and Ranging (LIDAR) Simultaneous Localization and Mapping (SLAM) establishes a two-dimensional map and provides positioning data. However, LIDAR can only provide relative positioning data and it cannot directly provide the latitude and longitude of the current position. As a consequence, GNSS/Inertial Navigation System (INS) integrated navigation could be employed in outdoors, while the indoors part makes use of INS/LIDAR integrated navigation and the corresponding switching navigation will make the indoor and outdoor positioning consistent. In addition, when the vehicle enters the garage, the GNSS signal will be blurred for a while and then disappeared. Ambiguous GNSS satellite signals will lead to the continuous distortion or overall drift of the positioning trajectory in the indoor condition. Therefore, an INS/LIDAR seamless integrated navigation algorithm and a switching algorithm based on vehicle navigation system are designed. According to the experimental data, the positioning accuracy of the INS/LIDAR navigation algorithm in the simulated environmental experiment is 50\% higher than that of the Dead Reckoning (DR) algorithm. Besides, the switching algorithm developed based on the INS/LIDAR integrated navigation algorithm can achieve 80\% success rate in navigation mode switching.}, language = {en} } @article{SchloerRingHotho2020, author = {Schl{\"o}r, Daniel and Ring, Markus and Hotho, Andreas}, title = {iNALU: Improved Neural Arithmetic Logic Unit}, series = {Frontiers in Artificial Intelligence}, volume = {3}, journal = {Frontiers in Artificial Intelligence}, issn = {2624-8212}, doi = {10.3389/frai.2020.00071}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-212301}, year = {2020}, abstract = {Neural networks have to capture mathematical relationships in order to learn various tasks. They approximate these relations implicitly and therefore often do not generalize well. The recently proposed Neural Arithmetic Logic Unit (NALU) is a novel neural architecture which is able to explicitly represent the mathematical relationships by the units of the network to learn operations such as summation, subtraction or multiplication. Although NALUs have been shown to perform well on various downstream tasks, an in-depth analysis reveals practical shortcomings by design, such as the inability to multiply or divide negative input values or training stability issues for deeper networks. We address these issues and propose an improved model architecture. We evaluate our model empirically in various settings from learning basic arithmetic operations to more complex functions. Our experiments indicate that our model solves stability issues and outperforms the original NALU model in means of arithmetic precision and convergence.}, language = {en} } @article{KraftBirkReichertetal.2020, author = {Kraft, Robin and Birk, Ferdinand and Reichert, Manfred and Deshpande, Aniruddha and Schlee, Winfried and Langguth, Berthold and Baumeister, Harald and Probst, Thomas and Spiliopoulou, Myra and Pryss, R{\"u}diger}, title = {Efficient processing of geospatial mHealth data using a scalable crowdsensing platform}, series = {Sensors}, volume = {20}, journal = {Sensors}, number = {12}, issn = {1424-8220}, doi = {10.3390/s20123456}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-207826}, year = {2020}, abstract = {Smart sensors and smartphones are becoming increasingly prevalent. Both can be used to gather environmental data (e.g., noise). Importantly, these devices can be connected to each other as well as to the Internet to collect large amounts of sensor data, which leads to many new opportunities. In particular, mobile crowdsensing techniques can be used to capture phenomena of common interest. Especially valuable insights can be gained if the collected data are additionally related to the time and place of the measurements. However, many technical solutions still use monolithic backends that are not capable of processing crowdsensing data in a flexible, efficient, and scalable manner. In this work, an architectural design was conceived with the goal to manage geospatial data in challenging crowdsensing healthcare scenarios. It will be shown how the proposed approach can be used to provide users with an interactive map of environmental noise, allowing tinnitus patients and other health-conscious people to avoid locations with harmful sound levels. Technically, the shown approach combines cloud-native applications with Big Data and stream processing concepts. In general, the presented architectural design shall serve as a foundation to implement practical and scalable crowdsensing platforms for various healthcare scenarios beyond the addressed use case.}, language = {en} } @article{DuLauterbachLietal.2020, author = {Du, Shitong and Lauterbach, Helge A. and Li, Xuyou and Demisse, Girum G. and Borrmann, Dorit and N{\"u}chter, Andreas}, title = {Curvefusion — A Method for Combining Estimated Trajectories with Applications to SLAM and Time-Calibration}, series = {Sensors}, volume = {20}, journal = {Sensors}, number = {23}, issn = {1424-8220}, doi = {10.3390/s20236918}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-219988}, year = {2020}, abstract = {Mapping and localization of mobile robots in an unknown environment are essential for most high-level operations like autonomous navigation or exploration. This paper presents a novel approach for combining estimated trajectories, namely curvefusion. The robot used in the experiments is equipped with a horizontally mounted 2D profiler, a constantly spinning 3D laser scanner and a GPS module. The proposed algorithm first combines trajectories from different sensors to optimize poses of the planar three degrees of freedom (DoF) trajectory, which is then fed into continuous-time simultaneous localization and mapping (SLAM) to further improve the trajectory. While state-of-the-art multi-sensor fusion methods mainly focus on probabilistic methods, our approach instead adopts a deformation-based method to optimize poses. To this end, a similarity metric for curved shapes is introduced into the robotics community to fuse the estimated trajectories. Additionally, a shape-based point correspondence estimation method is applied to the multi-sensor time calibration. Experiments show that the proposed fusion method can achieve relatively better accuracy, even if the error of the trajectory before fusion is large, which demonstrates that our method can still maintain a certain degree of accuracy in an environment where typical pose estimation methods have poor performance. In addition, the proposed time-calibration method also achieves high accuracy in estimating point correspondences.}, language = {en} } @techreport{Metzger2020, type = {Working Paper}, author = {Metzger, Florian}, title = {Crowdsensed QoE for the community - a concept to make QoE assessment accessible}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-203748}, pages = {7}, year = {2020}, abstract = {In recent years several community testbeds as well as participatory sensing platforms have successfully established themselves to provide open data to everyone interested. Each of them with a specific goal in mind, ranging from collecting radio coverage data up to environmental and radiation data. Such data can be used by the community in their decision making, whether to subscribe to a specific mobile phone service that provides good coverage in an area or in finding a sunny and warm region for the summer holidays. However, the existing platforms are usually limiting themselves to directly measurable network QoS. If such a crowdsourced data set provides more in-depth derived measures, this would enable an even better decision making. A community-driven crowdsensing platform that derives spatial application-layer user experience from resource-friendly bandwidth estimates would be such a case, video streaming services come to mind as a prime example. In this paper we present a concept for such a system based on an initial prototype that eases the collection of data necessary to determine mobile-specific QoE at large scale. In addition we reason why the simple quality metric proposed here can hold its own.}, subject = {Quality of Experience}, language = {en} } @article{GrohmannHerbstChalbanietal.2020, author = {Grohmann, Johannes and Herbst, Nikolas and Chalbani, Avi and Arian, Yair and Peretz, Noam and Kounev, Samuel}, title = {A Taxonomy of Techniques for SLO Failure Prediction in Software Systems}, series = {Computers}, volume = {9}, journal = {Computers}, number = {1}, issn = {2073-431X}, doi = {10.3390/computers9010010}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-200594}, pages = {10}, year = {2020}, abstract = {Failure prediction is an important aspect of self-aware computing systems. Therefore, a multitude of different approaches has been proposed in the literature over the past few years. In this work, we propose a taxonomy for organizing works focusing on the prediction of Service Level Objective (SLO) failures. Our taxonomy classifies related work along the dimensions of the prediction target (e.g., anomaly detection, performance prediction, or failure prediction), the time horizon (e.g., detection or prediction, online or offline application), and the applied modeling type (e.g., time series forecasting, machine learning, or queueing theory). The classification is derived based on a systematic mapping of relevant papers in the area. Additionally, we give an overview of different techniques in each sub-group and address remaining challenges in order to guide future research.}, language = {en} }