A Data Fusion Algorithm for Multiple Applications in Wireless Sensor Networks

Aquino, Gabriel; Pìrmez, Luci; Farias, Claudio M. de; Delicato, Flávia C.; Pires, Paulo F.

doi:10.1007/978-3-319-91146-5_14

Cited by 3 publications

(7 citation statements)

References 31 publications

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“…The EDP allows an application to recognize relevant events in the environment. As it is the first step to perform semantic clustering, we evaluate the algorithms DFLER , which calculates EDP using a distributed fuzzy system and Hephaestus , which analyzes the data entropy of the node neighborhood.…”

Section: Resultsmentioning

confidence: 99%

“…In the case of DM, initNumSamples is also 4 to represent the length of the data time series. For Hephaestus implementations, initNumSamples is 2, since it is the minimum to calculate the mean (section ).…”

Section: Methodsmentioning

confidence: 99%

“…Smart grid applications, like overheating power line monitoring and sensor battery monitoring use Hephaestus to detect leptokurtic data (relevant events) in their context. A sensor node collects neighborhood data and processes it running Hephaestus . In our approach, each node executes the Hephaestus algorithm using local and neighborhood data to estimate the EDP in a semantic clustering‐enabled WSN.…”

Section: Related Workmentioning

confidence: 99%

“…The value that quantifies the QoM for an embedded application depends on sensing tasks done by the allocated nodes . We evaluated the performance of two distributed algorithms() softly enough to run on a restricted WSN. As we used them for EDP estimation, we have measured their practical applicability in semantic clustering with/without VR and DM attributes resulting in an analysis of twelve clustering configurations.…”

Section: Introductionmentioning

confidence: 99%

“…The Hephaestus algorithm analyzes the entropy of local and neighborhood data through the estimation of mean, kurtosis, and skewness embedded in sensor nodes. The mean represents a detected event since it is the mass of the data center.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Towards a better sensor data accuracy via quality of monitoring and semantic clustering

Campos

Rocha²,

Gomes³

2019

Int J Communication

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Semantic clustering organizes wireless sensor nodes in clusters to detect a relevant event for an application. These nodes send only a message reporting it to the sink node while the other nodes decrease their resource utilization to save energy. In this work, we enhance semantic clustering with quality of monitoring (QoM) attributes of the event detection probability with variance reduction or dissimilarity measure. This approach allows each node to evaluate the accuracy of detection and the correlation level of the data gathered locally and by its neighbors. Experiments in a WLAN and FIT/IoT-Lab show QoM attributes improve the accuracy, especially in an extensive network where dissimilarity measure can keep it high independently of the cluster size. QoM also increases the sampling of these events by up to 60.7% while it decreases the number of messages sent to the sink between 13.1% and 32.9% without affecting the cluster formation or power consumption in almost all experiments. KEYWORDS distributed data fusion, QoM, semantic clustering, wireless sensor networks Int J Commun Syst. 2019;32:e3957.wileyonlinelibrary.com/journal/dac

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Towards a better sensor data accuracy via quality of monitoring and semantic clustering

Campos

Rocha²,

Gomes³

2019

Int J Communication

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Traffic-based Congestion Management algorithm for Wireless Sensor networks

Tshilongamulenzhe

Mathonsi

Mphahlele

et al. 2020

2020 International Conference on Computational Science and Computational Intelligence (CSCI)

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Analysis of Spatially Distributed Data in Internet of Things in the Environmental Context

Azevedo

Estrella

Delbem

et al. 2022

Sensors

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The Internet of Things consists of “things” made up of small sensors and actuators capable of interacting with the environment. The combination of devices with sensor networks and Internet access enables the communication between the physical world and cyberspace, enabling the development of solutions to many real-world problems. However, most existing applications are dedicated to solving a specific problem using only private sensor networks, which limits the actual capacity of the Internet of Things. In addition, these applications are concerned with the quality of service offered by the sensor network or the correct analysis method that can lead to inaccurate or irrelevant conclusions, which can cause significant harm for decision makers. In this context, we propose two systematic methods to analyze spatially distributed data Internet of Things. We show with the results that geostatistics and spatial statistics are more appropriate than classical statistics to do this analysis.

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A Data Fusion Algorithm for Multiple Applications in Wireless Sensor Networks

Cited by 3 publications

References 31 publications

Towards a better sensor data accuracy via quality of monitoring and semantic clustering

Towards a better sensor data accuracy via quality of monitoring and semantic clustering

Traffic-based Congestion Management algorithm for Wireless Sensor networks

Analysis of Spatially Distributed Data in Internet of Things in the Environmental Context

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