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- Provoost, Michiel, et al.
(författare)
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DingNet : a self-adaptive Internet-of-things exemplar
- 2019
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Ingår i: 2019 IEEE/ACM 14th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS). - : IEEE. - 9781728133683 - 9781728133690 ; , s. 195-201
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Konferensbidrag (refereegranskat)abstract
- Recent efforts have shown that research on self-adaptive systems can benefit from exemplars to evaluate and compare new methods, techniques and tools. One highly relevant application domain for self-adaptation is the Internet-of-Things (IoT). While some initial exemplars have been proposed for IoT, these exemplars are limited in scope to support research in realistic IoT domains, such as smart cities. To address this limitation, we introduce the DingNet exemplar, a reference implementation for research on self-adaptation in the domain of IoT. DingNet offers a simulator that maps directly to a physical IoT system that is deployed in the area of Leuven, Belgium. DingNet models a set of geographically distributed gateways, which are connected to a user application that is deployed at a front-end server. The gateways can interact over a LoRaWAN network with local stationary and mobile motes that can be equipped with sensors and actuators. The exemplar comes with a set of scenarios for comparing the effectiveness of different self-adaptive solutions. We illustrate how the exemplar is used for a typical adaptation problem of smart city IoT application, where mobile motes dynamically have to adapt their communication settings to ensure reliable and energy efficient communication.
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| 2. |
- Provoost, Michiel, et al.
(författare)
-
Joint Learning : A Pattern for Efficient Decision-Making and Reliable Communication in Self-Adaptive Internet of Things
- 2023
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Ingår i: EuroPLoP '23: Proceedings of the 28th European Conference on Pattern Languages of Programs, 5 July 2023. - : ACM Publications. - 9798400700408
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Konferensbidrag (refereegranskat)abstract
- An Internet-of-Things (IoT) system typically comprises many small computing elements (nodes) that are battery-powered and communicate over a wireless network. These elements monitor properties in the environment and send the data to client applications via gateways. The wireless networks used by the elements are subject to uncertainties that are difficult to predict upfront, such as dynamic objects (swaying trees, cars, …) and changing weather conditions that may deteriorate the transmissions. To ensure reliable communication over a wireless network of energy-constrained elements, recent research has proposed self-adaptive IoT systems. Such a self-adaptive system equips the network of elements – referred to as the managed system – with a feedback loop – the managing system. The managing system monitors the changing conditions and adapts the transmission settings of the IoT network to ensure the system’s quality goals. Leveraging and consolidating the existing knowledge in this area, we present a pattern that we coined Joint Learning that provides a solution to the decision-making problem of large, distributed self-adaptive IoT systems. With this pattern, elements use a joint learner to make adaptation decisions for individual elements while yielding reliable communication of the overall network. The pattern is applied to two cases to show that the solutions realize the system goals while operating under uncertainties.
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