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LIBRIS Formathandbok  (Information om MARC21)
FältnamnIndikatorerMetadata
00003287naa a2200349 4500
001oai:DiVA.org:lnu-129977
003SwePub
008240605s2023 | |||||||||||000 ||eng|
024a https://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-1299772 URI
024a https://doi.org/10.1145/3628034.36280432 DOI
040 a (SwePub)lnu
041 a engb eng
042 9 SwePub
072 7a ref2 swepub-contenttype
072 7a kon2 swepub-publicationtype
100a Provoost, Michielu Katholieke Universiteit Leuven, Belgium4 aut
2451 0a Joint Learning :b A Pattern for Efficient Decision-Making and Reliable Communication in Self-Adaptive Internet of Things
264 1b ACM Publications,c 2023
338 a print2 rdacarrier
520 a 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.
650 7a NATURVETENSKAPx Data- och informationsvetenskap0 (SwePub)1022 hsv//swe
650 7a NATURAL SCIENCESx Computer and Information Sciences0 (SwePub)1022 hsv//eng
653 a Data- och informationsvetenskap
653 a Computer and Information Sciences Computer Science
700a Weyns, Dannyu Linnéuniversitetet,Institutionen för datavetenskap och medieteknik (DM),Katholieke Universiteit Leuven, Belgium4 aut0 (Swepub:lnu)daweaa
700a Van Landuyt, Dimitriu Katholieke Universiteit Leuven, Belgium4 aut
700a Michiels, Samu Katholieke Universiteit Leuven, Belgium4 aut
700a Bureš, Tomášu Charles University, Czech Republic4 aut
710a Katholieke Universiteit Leuven, Belgiumb Institutionen för datavetenskap och medieteknik (DM)4 org
773t EuroPLoP '23: Proceedings of the 28th European Conference on Pattern Languages of Programs, 5 July 2023d : ACM Publicationsz 9798400700408
8564 8u https://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-129977
8564 8u https://doi.org/10.1145/3628034.3628043

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