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Blockchain for the IoT: Privacy-Preserving Protection of Sensor Data

, Chanson, Mathieu , Bogner, Andreas , Bilgeri, Dominik , Fleisch, Elgar , Wortmann, Felix

A constantly growing pool of smart, connected Internet of Things (IoT) devices poses completely new challenges for business regarding security and privacy. In fact, the widespread adoption of smart products might depend on the ability of organizations to offer systems that ensure adequate sensor data integrity while guaranteeing sufficient user privacy. In light of these challenges, previous research indicates that blockchain technology may be a promising means to mitigate issues of data security arising in the IoT. Building upon the existing body of knowledge, we propose a design theory, including requirements, design principles, and features, for a blockchain-based sensor data protection system (SDPS) that leverages data certification. We then design and develop an instantiation of an SDPS (CertifiCar) in three iterative cycles that prevents the fraudulent manipulation of car mileage data. Furthermore, we provide an ex-post evaluation of our design theory considering CertifiCar and two additional use cases in the realm of pharmaceutical supply chains and energy microgrids. The evaluation results suggest that the proposed design ensures the tamper-resistant gathering, processing, and exchange of IoT sensor data in a privacy-preserving, scalable, and efficient manner.

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Data-driven business and data privacy: Challenges and measures for product-based companies

2023 , Fabian Alfred Schäfer , Heiko Gebauer , Christoph Gröger , Oliver Gassmann , Felix Wortmann

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A KPI Set for Steering the IoT Business in Product Companies

2022-02-17 , Lamprecht, Claudio , Gebauer, Heiko , Fleisch, Elgar , Wortmann, Felix

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Understanding the Interactions Between Driving Behavior and Well-being in Daily Driving: Causal Analysis of a Field Study

2022-08 , Stephan, Paul , Wortmann, Felix , Koch, Kevin

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Smartwatches for non‐invasive hypoglycaemia detection during cognitive and psychomotor stress

2024 , Martin Maritsch , Simon Föll , Vera Lehmann , Naïma Styger , Caterina Bérubé , Mathias Kraus , Stefan Feuerriegel , Tobias Kowatsch , Thomas Züger , Elgar Fleisch , Felix Wortmann , Christoph Stettler

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Machine learning for non-invasive sensing of hypoglycaemia while driving in people with diabetes

2023-02-15 , Lehmann, Vera , Zueger, Thomas , Maritsch, Martin , Kraus, Mathias , Albrecht, Caroline , Bérubé, Caterina , Feuerriegel, Stefan , Wortmann, Felix , Kowatsch, Tobias , Styger, Naïma , Lagger, Sophie , Laimer, Markus , Fleisch, Elgar , Stettler, Christoph

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Towards Non-intrusive Camera-Based Heart Rate Variability Estimation in the Car Under Naturalistic Condition

2022-07-15 , Liu, Shu , Koch, Kevin , Zhou, Zimu , Maritsch, Martin , He, Xiaoxi , Fleisch, Elgar , Wortmann, Felix

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Machine Learning to Infer a Health State Using Biomedical Signals — Detection of Hypoglycemia in People with Diabetes while Driving Real Cars

2024 , Vera Lehmann , Thomas Zueger , Martin Maritsch , Michael Notter , Simon Schallmoser , Caterina Bérubé , Caroline Albrecht , Mathias Kraus , Stefan Feuerriegel , Elgar Fleisch , Tobias Kowatsch , Sophie Lagger , Markus Laimer , Felix Wortmann , Christoph Stettler

BACKGROUND Hypoglycemia, one of the most dangerous acute complications of diabetes, poses a substantial risk for vehicle accidents. To date, both reliable detection and warning of hypoglycemia while driving remain unmet needs, as current sensing approaches are restricted by diagnostic delay, invasiveness, low availability, and high costs. This research aimed to develop and evaluate a machine learning (ML) approach for the detection of hypoglycemia during driving through data collected on driving characteristics and gaze/head motion. METHODS We collected driving and gaze/head motion data (47,998 observations) during controlled euglycemia and hypoglycemia from 30 individuals with type 1 diabetes (24 male participants; mean ±SD age, 40.1±10.3 years; mean glycated hemoglobin value, 6.9±0.7% [51.9±8.0 mmol/mol]) while participants drove a real car. ML models were built and evaluated to detect hypoglycemia solely on the basis of data regarding driving characteristics and gaze/head motion. RESULTS The ML approach detected hypoglycemia with high accuracy (area under the receiver-operating characteristic curve [AUROC], 0.80±0.11). When restricted to either driving characteristics or gaze/head motion data only, the detection performance remained high (AUROC, 0.73±0.07 and 0.70±0.16, respectively). CONCLUSIONS Hypoglycemia could be detected noninvasively during real car driving with an ML approach that used only data on driving characteristics and gaze/head motion, thus improving driving safety and self-management for people with diabetes. Interpretable ML also provided novel insights into behavioral changes in people driving while hypoglycemic. (Funded by the Swiss National Science Foundation and others; ClinicalTrials.gov numbers, NCT04569630 and NCT05308095.)

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A Scalable Risk-Scoring System Based on Consumer-Grade Wearables for Inpatients With COVID-19: Statistical Analysis and Model Development

2022-05-25 , Föll, Simon , Lison, Adrian , Maritsch, Martin , Klingberg, Karsten , Lehmann, Vera , Züger, Thomas , Srivastava, David , Jegerlehner, Sabrina , Feuerriegel, Stefan , Fleisch, Elgar , Exadaktylos, Aristomenis , Wortmann, Felix

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Patterns of business model innovation for advancing IoT platforms

2022-01-03 , Markfort, Lino , Arzt, Alexander , Kögler, Philipp , Jung, Sven , Gebauer, Heiko , Haugk, Sebastian , Leyh, Christian , Wortmann, Felix

Purpose – The emergence of Internet of Things (IoT) platforms in product companies opens up new data-driven business opportunities. This paper looks at the emergence of these IoT platforms from a business-model perspective. Design/methodology/approach – The study applies a mixed method with two research studies: Study I–a cluster analysis based on a quantitative survey, and Study II–case studies based on qualitative interviews. Findings – The findings reveal that there is no gradual shift in a company’s business model, but in fact three distinct and sequential patterns of business model innovations: (1) platform skimming, (2) platform revenue generation and (3) platform orchestration. Research limitations/implications – The results are subject to the typical limitations of both quantitative and qualitative studies. Practical implications – The results provide guidance to managers on how to modify the components of the business model (value proposition, value creation and/or delivery and profit equation) in order to enable platforms to advance. Social implications – As IoT platforms continue to advance, product companies achieve better performance in terms of productivity and profitability, and more easily secure competitive advantages and jobs. Originality/value – The paper makes three original contributions: (1) it is the first quantitative study on IoT platforms in product companies, (2) identifies three patterns of business model innovations and (3) offers a first process perspective for understanding the sequence of these patterns as IoT platforms advance.