2021
DOI: 10.3390/e23060658
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Selection of the Optimal Smart Meter to Act as a Data Concentrator with the Use of Graph Theory

Abstract: Changing the construction of mart Meter (SM) devices, more specifically equipping them with more than one communication module, facilitates the elimination of a Transformer Station Data Concentrator (TSC) module, moving its function to one of the SMs. The opportunity to equip a chosen device in an additional communication module makes it possible to use it as an acquisition node. The introduction of this solution creates a problem with the optimum selection of the above-mentioned node out of all the nodes of t… Show more

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Cited by 5 publications
(3 citation statements)
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“…The foundation of graph transformations and their applications are resumed in [16][17][18]. To specify the IoT behavior, we use graph transformations as they have been successfully applied a few times to specification of the behavior of large parallel systems [19,20] or IoT networks [21].…”
Section: Graph Representation Of the Iot Networkmentioning
confidence: 99%
“…The foundation of graph transformations and their applications are resumed in [16][17][18]. To specify the IoT behavior, we use graph transformations as they have been successfully applied a few times to specification of the behavior of large parallel systems [19,20] or IoT networks [21].…”
Section: Graph Representation Of the Iot Networkmentioning
confidence: 99%
“…Previous studies have commented on the importance of considering multi-hopping as an option to guarantee the sending and arrival of information from the sensor to the concentrator [22,23]. Additionally, incorporating graph theory to solve a routing problem by controlling the multi-hop adjacency matrix allows the least-cost paths to be evaluated and ensures that the constraint on the maximum number of hops is maintained throughout the routing process [24,25].…”
Section: Motivationmentioning
confidence: 99%
“…Three neural network architectures were proposed: the first based on the convolutional network, the second on SincNet and the third on the convolutional network but with additional entropy-based features. The author of [ 11 ] touches on a new approach in the last-mile network structural solutions for smart grid networks and suggests a new method for finding the optimal SM localization, which can also work as a data concentrator.…”
mentioning
confidence: 99%