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Gravitational outlier detection for wireless sensor networks

https://doi.org/10.1002/dac.3155
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-1-4612-1686-5openalex-snapshot
Cumulative Sum Charts and Charting for Quality Improvement
inferredcites →https://doi.org/10.1007/978-3-540-77690-1_6openalex-snapshot
Spatiotemporal Anomaly Detection in Gas Monitoring Sensor Networks
inferredcites →https://doi.org/10.1007/s10115-011-0474-5openalex-snapshot
In-network outlier detection in wireless sensor networks
inferredcites →https://doi.org/10.1016/j.eij.2013.06.001openalex-snapshot
Outliers detection and classification in wireless sensor networks
inferredcites →https://doi.org/10.1017/cbo9780511667572openalex-snapshot
Unlocking the Power of OPNET Modeler
inferredcites →https://doi.org/10.1049/iet-ifs.2009.0192openalex-snapshot
Outlier detection and countermeasure for hierarchical wireless sensor networks
inferredcites →https://doi.org/10.1080/13658816.2012.654493openalex-snapshot
Statistics-based outlier detection for wireless sensor networks
inferredcites →https://doi.org/10.1093/comjnl/bxr026openalex-snapshot
A Survey of Outlier Detection Methods in Network Anomaly Identification
inferredcites →https://doi.org/10.1109/iccs.2006.301508openalex-snapshot
Distributed Anomaly Detection in Wireless Sensor Networks
inferredcites →https://doi.org/10.1109/mc.2012.394openalex-snapshot
Prototyping Connected Devices for the Internet of Things
inferredcites →https://doi.org/10.1109/surv.2010.021510.00088openalex-snapshot
Outlier Detection Techniques for Wireless Sensor Networks: A Survey
inferredcites →https://doi.org/10.1109/tcst.2013.2288519openalex-snapshot
Dealing With Outliers in Wireless Sensor Networks: An Oil Refinery Application
inferredcites →https://doi.org/10.1109/tcyb.2014.2338611openalex-snapshot
Adaptive Distributed Outlier Detection for WSNs
inferredcites →https://doi.org/10.1109/tetc.2013.2278698openalex-snapshot
A Priced Public Sensing Framework for Heterogeneous IoT Architectures
inferredcites →https://doi.org/10.1109/tii.2014.2306782openalex-snapshot
QoS-Aware Scheduling of Services-Oriented Internet of Things
inferredcites →https://doi.org/10.1109/tkde.2007.1062openalex-snapshot
On the Optimal Robot Routing Problem in Wireless Sensor Networks
inferredcites →https://doi.org/10.1109/tkde.2007.1067openalex-snapshot
Localized Outlying and Boundary Data Detection in Sensor Networks
inferredcites →https://doi.org/10.1109/tmc.2007.70768openalex-snapshot
Performance Analysis and Optimization of Handoff Algorithms in Heterogeneous Wireless Networks
inferredcites →https://doi.org/10.1109/vetecf.2003.1286180openalex-snapshot
Learning from class-imbalanced data in wireless sensor networks
inferredcites →https://doi.org/10.1145/1288107.1288137openalex-snapshot
Outlier detection in sensor networks
inferredcites →https://doi.org/10.1145/335191.335437openalex-snapshot
Efficient algorithms for mining outliers from large data sets
inferredcites →https://doi.org/10.1145/956750.956758openalex-snapshot
Mining distance-based outliers in near linear time with randomization and a simple pruning rule
inferredcites →https://doi.org/10.2307/2669658openalex-snapshot
Introduction to Probability Models
inferredcites →https://doi.org/10.5772/9394openalex-snapshot
Introduction to Machine Learning

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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An on-line detection method for outliers of dynamic unstable measurement data
inferred← citeshttps://doi.org/10.1007/s12652-019-01194-5openalex-snapshot
Contextual outlier detection for wireless sensor networks
inferred← citeshttps://doi.org/10.1016/j.autcon.2018.01.010openalex-snapshot
An integrated visualization framework to support whole-process management of water pipeline safety
inferred← citeshttps://doi.org/10.1016/j.jnca.2019.01.013openalex-snapshot
In-network context inference in IoT sensory environment for efficient network resource utilization
inferred← citeshttps://doi.org/10.1016/j.jnca.2019.06.013openalex-snapshot
A walkthrough of the emerging IoT paradigm: Visualizing inside functionalities, key features, and open issues
inferred← citeshttps://doi.org/10.1016/j.knosys.2019.105331openalex-snapshot
A local-gravitation-based method for the detection of outliers and boundary points
inferred← citeshttps://doi.org/10.1016/j.ress.2024.110553openalex-snapshot
Machine learning-based outlier detection for pipeline in-line inspection data
inferred← citeshttps://doi.org/10.1109/jiot.2021.3114259openalex-snapshot
A Data Agent Inspired by Interpersonal Interaction Behaviors for Wireless Sensor Networks
inferred← citeshttps://doi.org/10.1109/jiot.2025.3539723openalex-snapshot
Infrastructure–User Heterogeneous Data Interaction and Fusion of Mobile-IoT Sensing Networks in a Smart City: Application to a Footbridge
inferred← citeshttps://doi.org/10.17576/jsm-2017-4611-22openalex-snapshot
On-line Detection Method for Outliers of Dynamic Instability Measurement Data in Geological Exploration Control Process
inferred← citeshttps://doi.org/10.20944/preprints201903.0131.v1openalex-snapshot
Energy Consumption Prediction Using Machine Learning; A Review
inferred← citeshttps://doi.org/10.48550/arxiv.2008.06724openalex-snapshot
Damage Detection in Bridge Structures: An Edge Computing Approach
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