TY - JOUR A1 - Kirikkayis, Yusuf A1 - Gallik, Florian A1 - Winter, Michael A1 - Reichert, Manfred T1 - BPMNE4IoT: a framework for modeling, executing and monitoring IoT-driven processes JF - Future Internet N2 - The Internet of Things (IoT) enables a variety of smart applications, including smart home, smart manufacturing, and smart city. By enhancing Business Process Management Systems with IoT capabilities, the execution and monitoring of business processes can be significantly improved. Providing a holistic support for modeling, executing and monitoring IoT-driven processes, however, constitutes a challenge. Existing process modeling and process execution languages, such as BPMN 2.0, are unable to fully meet the IoT characteristics (e.g., asynchronicity and parallelism) of IoT-driven processes. In this article, we present BPMNE4IoT—A holistic framework for modeling, executing and monitoring IoT-driven processes. We introduce various artifacts and events based on the BPMN 2.0 metamodel that allow realizing the desired IoT awareness of business processes. The framework is evaluated along two real-world scenarios from two different domains. Moreover, we present a user study for comparing BPMNE4IoT and BPMN 2.0. In particular, this study has confirmed that the BPMNE4IoT framework facilitates the support of IoT-driven processes. KW - IoT KW - BPM KW - BPMN KW - IoT-driven processes Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-304097 SN - 1999-5903 VL - 15 IS - 3 ER - TY - JOUR A1 - Loh, Frank A1 - Mehling, Noah A1 - Hoßfeld, Tobias T1 - Towards LoRaWAN without data loss: studying the performance of different channel access approaches JF - Sensors N2 - The Long Range Wide Area Network (LoRaWAN) is one of the fastest growing Internet of Things (IoT) access protocols. It operates in the license free 868 MHz band and gives everyone the possibility to create their own small sensor networks. The drawback of this technology is often unscheduled or random channel access, which leads to message collisions and potential data loss. For that reason, recent literature studies alternative approaches for LoRaWAN channel access. In this work, state-of-the-art random channel access is compared with alternative approaches from the literature by means of collision probability. Furthermore, a time scheduled channel access methodology is presented to completely avoid collisions in LoRaWAN. For this approach, an exhaustive simulation study was conducted and the performance was evaluated with random access cross-traffic. In a general theoretical analysis the limits of the time scheduled approach are discussed to comply with duty cycle regulations in LoRaWAN. KW - LoRaWAN KW - IoT KW - channel management KW - scheduling KW - collision Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-302418 SN - 1424-8220 VL - 22 IS - 2 ER - TY - RPRT A1 - Vomhoff, Viktoria A1 - Geißler, Stefan A1 - Hoßfeld, Tobias T1 - Identification of Signaling Patterns in Mobile IoT Signaling Traffic T2 - Würzburg Workshop on Next-Generation Communication Networks (WueWoWas'22) N2 - We attempt to identify sequences of signaling dialogs, to strengthen our understanding of the signaling behavior of IoT devices by examining a dataset containing over 270.000 distinct IoT devices whose signaling traffic has been observed over a 31-day period in a 2G network [4]. We propose a set of rules that allows the assembly of signaling dialogs into so-called sessions in order to identify common patterns and lay the foundation for future research in the areas of traffic modeling and anomaly detection. KW - Datennetz KW - IoT Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-280819 ER - TY - THES A1 - Runge, Isabel Madeleine T1 - Network Coding for Reliable Data Dissemination in Wireless Sensor Networks T1 - Netzwerkkodierung für zuverlässige Datenverteilung in drahtlosen Sensornetzen N2 - The application of Wireless Sensor Networks (WSNs) with a large number of tiny, cost-efficient, battery-powered sensor nodes that are able to communicate directly with each other poses many challenges. Due to the large number of communicating objects and despite a used CSMA/CA MAC protocol, there may be many signal collisions. In addition, WSNs frequently operate under harsh conditions and nodes are often prone to failure, for example, due to a depleted battery or unreliable components. Thus, nodes or even large parts of the network can fail. These aspects lead to reliable data dissemination and data storage being a key issue. Therefore, these issues are addressed herein while keeping latency low, throughput high, and energy consumption reduced. Furthermore, simplicity as well as robustness to changes in conditions are essential here. In order to achieve these aims, a certain amount of redundancy has to be included. This can be realized, for example, by using network coding. Existing approaches, however, often only perform well under certain conditions or for a specific scenario, have to perform a time-consuming initialization, require complex calculations, or do not provide the possibility of early decoding. Therefore, we developed a network coding procedure called Broadcast Growth Codes (BCGC) for reliable data dissemination, which performs well under a broad range of diverse conditions. These can be a high probability of signal collisions, any degree of nodes' mobility, a large number of nodes, or occurring node failures, for example. BCGC do not require complex initialization and only use simple XOR operations for encoding and decoding. Furthermore, decoding can be started as soon as a first packet/codeword has been received. Evaluations by using an in-house implemented network simulator as well as a real-world testbed showed that BCGC enhance reliability and enable to retrieve data dependably despite an unreliable network. In terms of latency, throughput, and energy consumption, depending on the conditions and the procedure being compared, BCGC can achieve the same performance or even outperform existing procedures significantly while being robust to changes in conditions and allowing low complexity of the nodes as well as early decoding. N2 - Der Einsatz von drahtlosen Sensornetzen (Wireless Sensor Networks, WSNs) mit einer Vielzahl hochintegrierter, kostengünstiger und batteriebetriebener Sensorknoten, die direkt miteinander kommunizieren können, birgt viele Herausforderungen. Aufgrund der großen Anzahl von kommunizierenden Objekten kann es trotz eines verwendeten CSMA/CA MAC Protokolls zu vielen Signalkollisionen kommen. Darüber hinaus arbeiten WSNs häufig unter rauen Bedingungen und die Knoten sind oft anfällig für Ausfälle, z.B. aufgrund aufgebrauchter Energiekapazität oder defekter Komponenten. Infolgedessen können einzelne Knoten oder auch große Teile des Netzes ausfallen. Diese Aspekte führen dazu, dass zuverlässige Datenverteilung und Datenhaltung von entscheidender Bedeutung sind und folglich im Rahmen dieser Arbeit adressiert werden. Gleichzeitig soll die Latenz niedrig, der Durchsatz hoch und der Energieverbrauch möglichst gering gehalten werden. Des Weiteren sind eine geringe Komplexität sowie Robustheit gegenüber veränderten Bedingungen wesentlich. Um diese Ziele zu erreichen, ist ein gewisses Maß an Redundanz nötig. Dies kann beispielsweise durch die Verwendung von Netzwerkkodierung realisiert werden. Bestehende Ansätze liefern jedoch oft nur unter bestimmten Bedingungen oder für ein spezifisches Szenario gute Performanz-Ergebnisse, müssen aufwändig initialisiert werden, benötigen komplexe Berechnungen oder bieten keine Möglichkeit für frühzeitige Dekodierung. Daher haben wir ein als Broadcast Growth Codes (BCGC) bezeichnetes Netzwerkkodierungsverfahren für zuverlässige Datenverteilung entwickelt, welches unter einem breiten Spektrum unterschiedlicher Bedingungen gute Ergebnisse erzielt. Zu diesen Bedingungen gehören zum Beispiel eine hohe Wahrscheinlichkeit von Signalkollisionen, ein beliebiger Grad an Knotenmobilität, eine große Knotenanzahl oder das Auftreten von Knotenausfällen. BCGC benötigen keine komplexe Initialisierung und verwenden nur einfache XOR-Operationen für Kodierung und Dekodierung. Darüber hinaus kann mit der Dekodierung bereits begonnen werden, sobald ein erstes Paket/Codewort empfangen wurde. Evaluationen mit einem eigens implementierten Netzwerksimulator sowie einem realen Testbed haben gezeigt, dass BCGC ermöglichen, Daten trotz eines unzuverlässigen Netzwerks zuverlässig zu erhalten. In Bezug auf Latenz, Durchsatz und Energieverbrauch können BCGC, je nach Bedingungen und verglichenem Verfahren, vergleichbare Ergebnisse wie bestehende Verfahren erzielen oder diese sogar deutlich übertreffen, während sie gleichzeitig robust gegenüber veränderten Bedingungen sind, eine geringe Komplexität der Knoten erlauben sowie eine frühzeitige Dekodierung ermöglichen. KW - Zuverlässigkeit KW - Drahtloses Sensorsystem KW - IoT KW - Industrie 4.0 KW - adaptive network coding KW - Broadcast Growth Codes (BCGC) KW - RLNC KW - wireless sensor network KW - multi-source multi-sink problem KW - IEEE Std 802.15.4 Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-272245 ER - TY - JOUR A1 - Krupitzer, Christian A1 - Temizer, Timur A1 - Prantl, Thomas A1 - Raibulet, Claudia T1 - An Overview of Design Patterns for Self-Adaptive Systems in the Context of the Internet of Things JF - IEEE Access N2 - The Internet of Things (IoT) requires the integration of all available, highly specialized, and heterogeneous devices, ranging from embedded sensor nodes to servers in the cloud. The self-adaptive research domain provides adaptive capabilities that can support the integration in IoT systems. However, developing such systems is a challenging, error-prone, and time-consuming task. In this context, design patterns propose already used and optimized solutions to specific problems in various contexts. Applying design patterns might help to reuse existing knowledge about similar development issues. However, so far, there is a lack of taxonomies on design patterns for self-adaptive systems. To tackle this issue, in this paper, we provide a taxonomy on design patterns for self-adaptive systems that can be transferred to support adaptivity in IoT systems. Besides describing the taxonomy and the design patterns, we discuss their applicability in an Industrial IoT case study. KW - Design patterns KW - Internet of Things KW - IoT KW - self-adaptive systems KW - software engineering Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-229984 VL - 8 ER - TY - JOUR A1 - Düking, Peter A1 - Achtzehn, Silvia A1 - Holmberg, Hans-Christer A1 - Sperlich, Billy T1 - Integrated framework of load monitoring by a combination of smartphone applications, wearables and point-of-care testing provides feedback that allows individual responsive adjustments to activities of daily living JF - Sensors N2 - Athletes schedule their training and recovery in periods, often utilizing a pre-defined strategy. To avoid underperformance and/or compromised health, the external load during training should take into account the individual’s physiological and perceptual responses. No single variable provides an adequate basis for planning, but continuous monitoring of a combination of several indicators of internal and external load during training, recovery and off-training as well may allow individual responsive adjustments of a training program in an effective manner. From a practical perspective, including that of coaches, monitoring of potential changes in health and performance should ideally be valid, reliable and sensitive, as well as time-efficient, easily applicable, non-fatiguing and as non-invasive as possible. Accordingly, smartphone applications, wearable sensors and point-of-care testing appear to offer a suitable monitoring framework allowing responsive adjustments to exercise prescription. Here, we outline 24-h monitoring of selected parameters by these technologies that (i) allows responsive adjustments of exercise programs, (ii) enhances performance and/or (iii) reduces the risk for overuse, injury and/or illness. KW - biofeedback KW - eHealth KW - individualized training KW - injury prevention KW - IoT KW - periodization KW - load management Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-176506 VL - 18 IS - 5 ER -