Evolutionary Graph Models with Dynamic Topologies on the Ubichip

Peña, Juan Camilo; Peña, Jorge; Upegui, Andrés

doi:10.1007/978-3-540-85857-7_6

Cited by 5 publications

(1 citation statement)

References 9 publications

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“…where 𝑍 is matrix with the data from the sensors with one row for each sonsor and one column for each time stamp, 𝑊 * is the optimal weighted adjacency matrix, 1 ⊤ log(𝑊 1) ensures overall connectivity of the graph by forcing the degrees to be positive while allowing sparsity, 𝛼 and 𝛽 are parameters to control connectivity and sparsity respectively. We follow the implementation in [44] to determine the weighted adjacency matrix, 𝑊 . We create an unweighted adjacency matrix, 𝐴 and graph, 𝐺 𝑔𝑠𝑝 by creating an edge in 𝐴 and 𝐺 𝑔𝑠𝑝 if the edge weight in 𝑊 is greater than threshold.…”

Section: Approaches Based On Graph Signalmentioning

confidence: 99%

Finding Representative Sampling Subsets in Sensor Graphs using Time Series Similarities

Chakraborty¹,

Holm²,

Pedersen³

et al. 2022

Preprint

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With the increasing use of IoT-enabled sensors, it is important to have effective methods for querying the sensors. For example, in a dense network of battery-driven temperature sensors, it is often possible to query (sample) just a subset of the sensors at any given time, since the values of the non-sampled sensors can be estimated from the sampled values. If we can divide the set of sensors into disjoint so-called representative sampling subsets that each represent the other sensors sufficiently well, we can alternate the sampling between the sampling subsets and thus, increase battery life significantly. In this paper, we formulate the problem of finding representative sampling subsets as a graph problem on a so-called sensor graph with the sensors as nodes. Our proposed solution, SubGraphSample, consists of two phases. In Phase-I, we create edges in the sensor graph based on the similarities between the time series of sensor values, analyzing six different techniques based on proven time series similarity metrics. In Phase-II, we propose two new techniques and extend four existing ones to find the maximal number of representative sampling subsets. Finally, we propose AutoSubGraphSample which auto-selects the best technique for Phase-I and Phase-II for a given dataset. Our extensive experimental evaluation shows that our approach can yield significant battery life improvements within realistic error bounds.

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Section: Approaches Based On Graph Signalmentioning

confidence: 99%