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Real-time operating systems for mixed-criticality systems
must support different types of software, such as
real-time applications and general purpose applications,
and, at the same time, must provide strong spatial and
temporal isolation between independent software components.
Therefore, state-of-the-art real-time operating systems
focus mainly on predictability and bounded worst-case behavior.
However, general purpose operating systems such as Linux
often feature more efficient---but less deterministic---mechanisms
that significantly improve the average execution time.
This thesis addresses the combination of the two contradicting
requirements and shows thread synchronization mechanisms
with efficient average-case behavior, but without sacrificing
predictability and worst-case behavior.
This thesis explores and evaluates the design space of fast paths
in the implementation of typical blocking synchronization
mechanisms, such as mutexes, condition variables, counting
semaphores, barriers, or message queues. The key technique here
is to avoid unnecessary system calls, as system calls have high
costs compared to other processor operations available in user
space, such as low-level atomic synchronization primitives.
In particular, the thesis explores futexes, the state-of-the-art
design for blocking synchronization mechanisms in Linux
that handles the uncontended case of thread synchronization
by using atomic operations in user space and calls into the
kernel only to suspend and wake up threads. The thesis also
proposes non-preemptive busy-waiting monitors that use an
efficient priority ceiling mechanism to prevent the lock holder
preemption problem without using system calls, and according
low-level kernel primitives to construct efficient wait and
notify operations.
The evaluation shows that the presented approaches
improve the average performance comparable
to state-of-the-art approaches in Linux.
At the same time, a worst-case timing analysis shows
that the approaches only need constant or bounded temporal
overheads at the operating system kernel level.
Exploiting these fast paths is a worthwhile approach
when designing systems that not only have to fulfill
real-time requirements, but also best-effort workloads.
Leaf litter breakdown is a fundamental process in aquatic ecosystems, being mainly mediated by decomposer-detritivore systems that are composed of microbial decomposers and leaf-shredding, detritivorous invertebrates. The ecological integrity of these systems can, however, be disturbed, amongst others, by chemical stressors. Fungicides might pose a particular risk as they can have negative effects on the involved microbial decomposers but may also affect shredders via both waterborne toxicity and their diet; the latter by toxic effects due to dietary exposure as a result of fungicides’ accumulation on leaf material and by negatively affecting fungal leaf decomposers, on which shredders’ nutrition heavily relies. The primary aim of this thesis was therefore to provide an in-depth assessment of the ecotoxicological implications of fungicides in a model decomposer-detritivore system using a tiered experimental approach to investigate (1) waterborne toxicity in a model shredder, i.e., Gammarus fossarum, (2) structural and functional implications in leaf-associated microbial communities, and (3) the relative importance of waterborne and diet-related effects for the model shredder.
Additionally, knowledge gaps were tackled that were related to potential differences in the ecotoxicological impact of inorganic (also authorized for organic farming in large parts of the world) and organic fungicides, the mixture toxicity of these substances, the field-relevance of their effects, and the appropriateness of current environmental risk assessment (ERA).
In the course of this thesis, major differences in the effects of inorganic and organic fungicides on the model decomposer-detritivore system were uncovered; e.g., the palatability of leaves for G. fossarum was increased by inorganic fungicides but deteriorated by organic substances. Furthermore, non-additive action of fungicides was observed, rendering mixture effects of these substances hardly predictable. While the relative importance of the waterborne and diet-related effect pathway for the model shredder seems to depend on the fungicide group and the exposure concentration, it was demonstrated that neither path must be ignored due to additive action. Finally, it was shown that effects can be expected at field-relevant fungicide levels and that current ERA may provide insufficient protection for decomposer-detritivore systems. To safeguard aquatic ecosystem functioning, this thesis thus recommends including leaf-associated microbial communities and long-term feeding studies using detritus feeders in ERA testing schemes, and identifies several knowledge gaps whose filling seems mandatory to develop further reasonable refinements for fungicide ERA.
Based on dual process models of information processing, the present research addressed how explicit disgust sensitivity is re-adapted according to implicit disgust sensitivity via self-perception of automatic behavioral cues. Contrary to preceding studies (Hofmann, Gschwendner, & Schmitt, 2009) that concluded that there was a "blind spot" for self- but not for observer perception of automatic behavioral cues, in the present research, a re-adaption process was found for self-perceivers and observers. In Study 1 (N = 75), the predictive validity of an indirect disgust sensitivity measure was tested with a double-dissociation strategy. Study 2 (N = 117) reinvestigated the hypothesis that self-perception of automatic behavioral cues, predicted by an indirect disgust sensitivity measure, led to a re-adaption of explicit disgust sensitivity measures. Using a different approach from Hofmann et al. (2009), the self-perception procedure was modified by (a) feeding back the behavior several times while a small number of cues had to be rated for each feedback condition, (b) using disgust sensitivity as a domain with clearly unequivocal cues of automatic behavior (facial expression, body movements) and describing these cues unambiguously, and (c) using a specific explicit disgust sensitivity measure in addition to a general explicit disgust sensitivity measure. In Study 3 (N = 130), the findings of Study 2 were replicated and display rules and need for closure as moderator effects of predictive validity and cue utilization were additionally investigated. The moderator effects give hints that both displaying a disgusted facial expression and self-perception of one- own disgusted facial expression are subject to a self-serving bias, indicating that facial expression may not be an automatic behavior. Practical implications and implications for future research are discussed.
Web-programming is a huge field of different technologies and concepts. Each technology implements a web-application requirement like content generation or client-server communication. Different technologies within one application are organized by concepts, for example architectural patterns. The thesis describes an approach for creating a taxonomy about these web-programming components using the free encyclopaedia Wikipedia. Our 101companies project uses implementations to identify and classify the different technology sets and concepts behind a web-application framework. These classifications can be used to create taxonomies and ontologies within the project. The thesis also describes, how we priorize useful web-application frameworks with the help of Wikipedia. Finally, the created implementations concerning web-programming are documented.
Wikipedia is the biggest, free online encyclopaedia that can be expanded by any-one. For the users, who create content on a specific Wikipedia language edition, a social network exists. In this social network users are categorised into different roles. These are normal users, administrators and functional bots. Within the networks, a user can post reviews, suggestions or send simple messages to the "talk page" of another user. Each language in the Wikipedia domain has this type of social network.
In this thesis characteristics of the three different roles are analysed in order to learn how they function in one language network of Wikipedia and apply them to another Wikipedia network to identify bots. Timestamps from created posts are analysed to reveal noticeable characteristics referring to continuous messages, message rates and irregular behaviour of a user are discovered. Through this process we show that there exist differences between the roles for the mentioned characteristics.
Placing questions before the material or after the material constitute different reading situations. To adapt to these reading situations, readers may apply appropriate reading strategies. Reading strategy caused by location of question has been intensively explored in the context of text comprehension. (1) However, there is still not enough knowledge about whether text plays the same role as pictures when readers apply different reading strategies. To answer this research question, three reading strategies are experimentally manipulated by displaying question before or after the blended text and picture materials: (a) Unguided processing with text and pictures and without the question. (b) Information gathering to answer the questions after the prior experience with text and pictures. (c) Comprehending text and pictures to solve the questions with the prior information of the questions. (2) Besides, it is arguable whether readers prefer text or pictures when the instructed questions are in different difficulty levels. (3) Furthermore, it is still uncertain whether students from higher school tier (Gymnasium) emphasize more on text or on pictures than students from lower school tier (Realschule). (4) Finally, it is rarely mentioned whether higher graders are more able to apply reading strategies in text processing and picture processing than lower graders.
Two experiments were undertaken to investigate the usage of text and pictures in the perspectives of task orientation, question difficulty, school and grade. For a 2x2(x2x2x2) mixed design adopting eye tracking method, participants were recruited from grade 5 (N = 72) and grade 8 (N = 72). In Experiment 1, thirty-six 5th graders were recruited from higher tier (Gymnasium) and thirty-six 5th graders were from lower tier (Realschule). In Experiment 2, thirty-six 8th graders were recruited from higher tier and thirty-six were from lower tier. They were supposed to comprehend the materials combining text and pictures and to answer the questions. A Tobii XL60 eye tracker recorded their eye movements and their answers to the questions. Eye tracking indicators were analyzed and reported, such as accumulated fixation duration, time to the first fixation and transitions between different Areas of Interest. The results reveal that students process text differently from pictures when they follow different reading strategies. (1) Consistent with Hypothesis 1, students mainly use text to construct their mental model in unguided spontaneous processing of text and pictures. They seem to mainly rely on the pictures as external representations when trying to answer questions after the prior experience with the material. They emphasize on both text and pictures when questions are presented before the material. (2) Inconsistent with Hypothesis 2, students are inclined to emphasize on text and on pictures as question difficulty increases. However, the increase of focus on pictures is more than on text when the presented question is difficult. (3) Different from Hypothesis 3, the current study discovers that higher tier students did not differ from lower tier students in text processing. Conversely, students from higher tier attend more to pictures than students from lower tier. (4) Differed from Hypothesis 4, 8th graders outperform 5th graders mainly in text processing. Only a subtle difference is found between 5th graders and 8th graders in picture processing.
To sum up, text processing differs from picture processing when applying different reading strategies. In line with the Integrative Model of Text and Picture Comprehension by Schnotz (2014), text is likely to play a major part in guiding the processing of meaning or general reading, whereas pictures are applied as external representations for information retrieval or selective reading. When question is difficulty, pictures are emphasized due to their advantages in visualizing the internal structure of information. Compared to lower tier students (poorer problem solvers), higher tier students (good problem solvers) are more capable of comprehending pictures rather than text. Eighth graders are more efficient than 5th graders in text processing rather than picture processing. It also suggests that in designing school curricula, more attention should be paid to students’ competence on picture comprehension or text-picture integration in the future.
With the appearance of modern virtual reality (VR) headsets on the consumer market, there has been the biggest boom in the history of VR technology. Naturally, this was accompanied by an increasing focus on the problems of current VR hardware. Especially the control in VR has always been a complex topic.
One possible solution is the Leap Motion, a hand tracking device that was initially developed for desktop use, but with the last major software update it can be attached to standard VR headsets. This device allows very precise tracking of the user’s hands and fingers and their replication in the virtual world.
The aim of this work is to design virtual user interfaces that can be operated with the Leap Motion to provide a natural method of interaction between the user and the VR environment. After that, subject tests are performed to evaluate their performance and compare them to traditional VR controllers.
The estimation of various social objects is necessary in different fields of social life, science, education, etc. This estimation is usually used for forecasting, for evaluating of different properties and for other goals in complex man-machine systems. At present this estimation is possible by means of computer and mathematical simulation methods which is connected with significant difficulties, such as: - time-distributed process of receiving information about the object; - determination of a corresponding mathematical device and structure identification of the mathematical model; - approximation of the mathematical model to real data, generalization and parametric identification of the mathematical model; - identification of the structure of the links of the real social object. The solution of these problems is impossible without a special intellectual information system which combines different processes and allows predicting the behaviour of such an object. However, most existing information systems lead to the solution of only one special problem. From this point of view the development of a more general technology of designing such systems is very important. The technology of intellectual information system development for estimation and forecasting the professional ability of respondents in the sphere of education can be a concrete example of such a technology. Job orientation is necessary and topical in present economic conditions. It helps tornsolve the problem of expediency of investments to a certain sphere of education. Scientifically validated combined diagnostic methods of job orientation are necessary to carry out professional selection in higher education establishments. The requirements of a modern society are growing, with the earlier developed techniques being unable to correspond to them sufficiently. All these techniques lack an opportunity to account all necessary professional and personal characteristics. Therefore, it is necessary to use a system of various tests. Thus, the development of new methods of job orientation for entrants is necessary. The information model of the process of job orientation is necessary for this purpose. Therefore, it would be desirable to have an information system capable of giving recommendations concerning the choice of a trade on the basis of complex personal characteristics of entrants.
While the 1960s and 1970s still knew permanent education (Council of Europe), recurrent education (OECD) and lifelong education (UNESCO), over the past 20 years, lifelong learning has become the single emblem for reforms in (pre-) primary, higher and adult education systems and international debates on education. Both highly industrialized and less industrialized countries embrace the concept as a response to the most diverse economic, social and demographic challenges - in many cases motivated by international organizations (IOs).
Yet, literature on the nature of this influence, the diffusion of the concept among IOs and their understanding of it is scant and usually focuses on a small set of actors. Based on longitudinal data and a large set of education documents, the work identifies rapid diffusion of the concept across a heterogeneous, expansive and dynamic international field of 88 IOs in the period 1990-2013, which is difficult to explain with functionalist accounts.
Based on the premises of world polity theory, this paper argues that what diffuses resembles less the bundle of systemic reforms usually associated with the concept in the literature and more a surprisingly detailed model of a new actor " the lifelong learner.
Foliicolous lichens are one of the most abundant epiphytes in tropical rainforests and one of the few groups of organisms that characterize these forests. Tropical rainforests are increasingly affected by anthropogenic disturbance resulting in forest destruction and degradation. However, not much is known on the effects of anthropogenic disturbance on the diversity of foliicolous lichens. Understanding such effects is crucial for the development of appropriate measures for the conservation of such organisms. In this study, foliicolous lichens diversity was investigated in three tropical rainforests in East Africa. Godere Forest in Southwest Ethiopia is a transitional rainforest with a mixture of Afromontane and Guineo-Congolian species. The forest is secondary and has been affected by shifting cultivation, semi-forest coffee management and commercial coffee plantation. Budongo Forest in West Uganda is a Guineo-Congolian rainforest consisting of primary and secondary forests. Kakamega Forest in western Kenya is a transitional rainforest with a mixture of Guineo-Congolian and Afromontane species. The forest is a mosaic of near-primary forest, secondary forests of different seral stages, grasslands, plantations, and natural glades.
The purpose of this thesis is to explore the sentiment distributions of Wikipedia concepts.
We analyse the sentiment of the entire English Wikipedia corpus, which includes 5,669,867 articles and 1,906,375 talks, by using a lexicon-based method with four different lexicons.
Also, we explore the sentiment distributions from a time perspective using the sentiment scores obtained from our selected corpus. The results obtained have been compared not only between articles and talks but also among four lexicons: OL, MPQA, LIWC, and ANEW.
Our findings show that among the four lexicons, MPQA has the highest sensitivity and ANEW has the lowest sensitivity to emotional expressions. Wikipedia articles show more sentiments than talks according to OL, MPQA, and LIWC, whereas Wikipedia talks show more sentiments than articles according to ANEW. Besides, the sentiment has a trend regarding time series, and each lexicon has its own bias regarding text describing different things.
Moreover, our research provides three interactive widgets for visualising sentiment distributions for Wikipedia concepts regarding the time and geolocation attributes of concepts.
In a world where language defines the boundaries of one's understanding, the words of Austrian philosopher Ludwig Wittgenstein resonate profoundly. Wittgenstein's assertion that "Die Grenzen meine Sprache bedeuten die Grenzen meiner Welt" (Wittgenstein 2016: v. 5.6) underscores the vital role of language in shaping our perceptions. Today, in a globalized and interconnected society, fluency in foreign languages is indispensable for individual success. Education must break down these linguistic barriers, and one promising approach is the integration of foreign languages into content subjects.
Teaching content subjects in a foreign language, a practice known as Content Language Integrated Learning (CLIL), not only enhances language skills but also cultivates cognitive abilities and intercultural competence. This approach expands horizons and aligns with the core principles of European education (Leaton Gray, Scott & Mehisto 2018: 50). The Kultusministerkonferenz (KMK) recognizes the benefits of CLIL and encourages its implementation in German schools (cf. KMK 2013a).
With the rising popularity of CLIL, textbooks in foreign languages have become widely available, simplifying teaching. However, the appropriateness of the language used in these materials remains an unanswered question. If textbooks impose excessive linguistic demands, they may inadvertently limit students' development and contradict the goal of CLIL.
This thesis focuses on addressing this issue by systematically analyzing language requirements in CLIL teaching materials, emphasizing receptive and productive skills in various subjects based on the Common European Framework of Reference. The aim is to identify a sequence of subjects that facilitates students' language skill development throughout their school years. Such a sequence would enable teachers to harness the full potential of CLIL, fostering a bidirectional approach where content subjects facilitate language learning.
While research on CLIL is extensive, studies on language requirements for bilingual students are limited. This thesis seeks to bridge this gap by presenting findings for History, Geography, Biology, and Mathematics, allowing for a comprehensive understanding of language demands. This research endeavors to enrich the field of bilingual education and CLIL, ultimately benefiting the academic success of students in an interconnected world.
Climate change is an existential threat to human survival, the social organization of society, and the stability of ecosystems. It is thereby profoundly frightening. In the face of threat, people often want to protect themselves instead of engaging in mitigating behaviors. When psychological resources are insufficient to cope, people often respond with different forms of denial. In this dissertation, I contribute original knowledge to the understanding of the multifaceted phenomenon of climate denial from a psychological perspective.
There are four major gaps in the literature on climate denial: First, the spectrum of climate denial as a self-protective response to the climate crisis has not received attention within psychology. Second, basic psychological need satisfaction, a fundamental indicator of human functioning and the ability to cope with threat, has not been investigated as a predictor of climate denial. Third, relations of the spectrum of climate denial to climate-relevant emotions, specifically climate anxiety, have not been examined empirically. Forth, it has not been investigated how the spectrum of climate denial relates to established predictors of climate denial, namely right-wing ideological convictions and male gender. To address those gaps, I investigate what the spectrum of climate denial looks like in the German context and how it relates to basic psychological need satisfaction and frustration, pro-environmental behavior, climate anxiety, ideological conviction, and gender.
Five manuscripts reveal that climate denial exists on a spectrum in the German context, ranging from the distortion of facts (interpretive climate denial, specifically denial of personal and global outcome severity) to the denial of the implications of climate change (implicatory climate denial, specifically avoidance, denial of guilt, and rationalization of one's own involvement). Across analyses, low basic psychological need satisfaction predicted the spectrum of climate denial, which was negatively related to pro-environmental behavior. Climate denial was generally negatively related to climate anxiety, except for a positive association of avoidance and climate anxiety. Right-wing ideological conviction was the strongest predictor of climate denial across the spectrum. However, low need satisfaction and male gender were additional weaker predictors of implicatory climate denial.
These findings suggest that the spectrum of climate denial serves many psychological functions. Climate denial is possibly both a self-protective strategy to downregulate emotions and to protect oneself from loss of privilege. In short, it represents a barrier to climate action that may only be resolved once people have sufficient psychological resources to face the threat of climate change and cope with their underlying self-protective, emotional responses.
This thesis addresses the automated identification and localization of a time-varying number of objects in a stream of sensor data. The problem is challenging due to its combinatorial nature: If the number of objects is unknown, the number of possible object trajectories grows exponentially with the number of observations. Random finite sets are a relatively new theory that has been developed to derive at principled and efficient approximations. It is based around set-valued random variables that contain an unknown number of elements which appear in arbitrary order and are themselves random. While extensively studied in theory, random finite sets have not yet become a leading paradigm in practical computer vision and robotics applications. This thesis explores random finite sets in visual tracking applications. The first method developed in this thesis combines set-valued recursive filtering with global optimization. The problem is approached in a min-cost flow network formulation, which has become a standard inference framework for multiple object tracking due to its efficiency and optimality. A main limitation of this formulation is a restriction to unary and pairwise cost terms. This circumstance makes integration of higher-order motion models challenging. The method developed in this thesis approaches this limitation by application of a Probability Hypothesis Density filter. The Probability Hypothesis Density filter was the first practically implemented state estimator based on random finite sets. It circumvents the combinatorial nature of data association itself by propagation of an object density measure that can be computed efficiently, without maintaining explicit trajectory hypotheses. In this work, the filter recursion is used to augment measurements with an additional hidden kinematic state to be used for construction of more informed flow network cost terms, e.g., based on linear motion models. The method is evaluated on public benchmarks where a considerate improvement is achieved compared to network flow formulations that are based on static features alone, such as distance between detections and appearance similarity. A second part of this thesis focuses on the related task of detecting and tracking a single robot operator in crowded environments. Different from the conventional multiple object tracking scenario, the tracked individual can leave the scene and later reappear after a longer period of absence. Therefore, a re-identification component is required that picks up the track on reentrance. Based on random finite sets, the Bernoulli filter is an optimal Bayes filter that provides a natural representation for this type of problem. In this work, it is shown how the Bernoulli filter can be combined with a Probability Hypothesis Density filter to track operator and non-operators simultaneously. The method is evaluated on a publicly available multiple object tracking dataset as well as on custom sequences that are specific to the targeted application. Experiments show reliable tracking in crowded scenes and robust re-identification after long term occlusion. Finally, a third part of this thesis focuses on appearance modeling as an essential aspect of any method that is applied to visual object tracking scenarios. Therefore, a feature representation that is robust to pose variations and changing lighting conditions is learned offline, before the actual tracking application. This thesis proposes a joint classification and metric learning objective where a deep convolutional neural network is trained to identify the individuals in the training set. At test time, the final classification layer can be stripped from the network and appearance similarity can be queried using cosine distance in representation space. This framework represents an alternative to direct metric learning objectives that have required sophisticated pair or triplet sampling strategies in the past. The method is evaluated on two large scale person re-identification datasets where competitive results are achieved overall. In particular, the proposed method better generalizes to the test set compared to a network trained with the well-established triplet loss.
The automatic detection of position and orientation of subsea cables and pipelines in camera images enables underwater vehicles to make autonomous inspections. Plants like algae growing on top and nearby cables and pipelines however complicate their visual detection: the determination of the position via border detection followed by line extraction often fails. Probabilistic approaches are here superior to deterministic approaches. Through modeling probabilities it is possible to make assumptions on the state of the system even if the number of extracted features is small. This work introduces a new tracking system for cable/pipeline following in image sequences which is based on particle filters. Extensive experiments on realistic underwater videos show robustness and performance of this approach and demonstrate advantages over previous works.
Social media platforms such as Twitter or Reddit allow users almost unrestricted access to publish their opinions on recent events or discuss trending topics. While the majority of users approach these platforms innocently, some groups have set their mind on spreading misinformation and influencing or manipulating public opinion. These groups disguise as native users from various countries to spread frequently manufactured articles, strong polarizing opinions in the political spectrum and possibly become providers of hate-speech or extremely political positions. This thesis aims to implement an AutoML pipeline for identifying second language speakers from English social media texts. We investigate style differences of text in different topics and across the platforms Reddit and Twitter, and analyse linguistic features. We employ feature-based models with datasets from Reddit, which include mostly English conversation from European users, and Twitter, which was newly created by collecting English tweets from selected trending topics in different countries. The pipeline classifies language family, native language and origin (Native or non-Native English speakers) of a given textual input. We evaluate the resulting classifications by comparing prediction accuracy, precision and F1 scores of our classification pipeline to traditional machine learning processes. Lastly, we compare the results from each dataset and find differences in language use for topics and platforms. We obtained high prediction accuracy for all categories on the Twitter dataset and observed high variance in features such as average text length especially for Balto-Slavic countries.
As Enterprise 2.0 (E2.0) initiatives are gradually moving out of the early experimentation phase it is time to focus greater attention on examining the structures, processes and operations surrounding E2.0 projects. In this paper we present the findings of an empirical study to investigate and understand the reasons for initiating E2.0 projects and the benefits being derived from them. Our study comprises seven in-depth case studies of E2.0 implementations. We develop a classification and means of visualising the scope of E2.0 initiatives and use these methods to analyse and compare projects.
Our findings indicate a wide range of motivations and combinations of technology in use and show a strong emphasis towards the content management functionality of E2.0 technologies.
Remote Working Study 2022
(2022)
The Remote Working Study 2022 is focused on the transition to work from home (WFH) triggered by the stay at home directives of 2020. These directives required employees to work in their private premises wherever possible to reduce the transmission of the coronavirus. The study, conducted by the Center for Enterprise Information Research (CEIR) at the University of Koblenz from December 2021 to January 2022, explores the transition to remote working.
The objective of the survey is to collect baseline information about organisations’ remote work experiences during and immediately following the COVID-19 lockdowns. The survey was completed by the key persons responsible for the implementation and/or management of the digital workplace in 19 German and Swiss organisations.
The data presented in this report was collected from member organisations of the IndustryConnect initiative. IndustryConnect is a university-industry research programme that is coordinated by researchers from the University of Koblenz. It focuses on research in the areas of the digital workplace and enterprise collaboration technologies, and facilitates the generation of new research insights and the exchange of experiences among user companies.
The Internet of Things (IoT) is a concept in which connected physical objects are integrated into the virtual world to become active partakers of businesses and everyday processes (Uckelmann, Harrison and Michahelles, 2011; Shrouf, Ordieres and Miragliotta, 2014). It is expected to have a major impact on businesses (Council, Nic and Intelligence, 2008), but small and medium enterprises’ business models are threatened if they do not adopt the new concept (Sommer, 2015). Thus, this thesis aims to showcase a sample implementation of connected devices in a small enterprise, demonstrating its added benefits for the business.
Design Science Research (DSR) is used to develop a prototype based on a use case provided by a carpentry. The prototype comprises a hardware sensor and a web application which can be used by the wood shop to improve their processes. The thesis documents the iterative process of developing a prototype from the grounds up to useable hard- and software.
This contribution provides an example of how IoT can be used and implemented at a small business.