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Clustering‐based resource discovery on Internet‐of‐Things

https://doi.org/10.1002/dac.3501
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Outgoing claims: the papers this work cites, builds on, or argues with.

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A novel intuitionistic fuzzy C means clustering algorithm and its application to medical images
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Effective FCM noise clustering algorithms in medical images
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Intelligent Resource Inquisition Framework on Internet-of-Things
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Fuzzy c-means clustering with spatial information for image segmentation
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Clustering: A neural network approach
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Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation
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Robust-learning fuzzy c-means clustering algorithm with unknown number of clusters
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A Proactive Trickle-based Mechanism for Discovering CoRE Resource Directories
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Document ranking and the vector-space model
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When discriminative K-means meets Grassmann manifold: Disease gene identification via a general multi-view clustering method
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An Effective IoT Services Indexing and Query Technique
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Describing things in the Internet of Things: From CoRE link format to semantic based descriptions
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A Resource Discovery Algorithm in Mobile Peer-to-Peer Based on Vector Location
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Web Services Dynamic Discovery Based on Modified CLIQUE Algorithm
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Light-Weight Multicast DNS and DNS-SD (lmDNS-SD): IPv6-Based Resource and Service Discovery for the Web of Things
inferredcites →https://doi.org/10.1109/jiot.2016.2618909openalex-snapshot
Adaptive Clustering for Dynamic IoT Data Streams
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Advanced Message Queuing Protocol
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From today's INTRAnet of things to a future INTERnet of things: a wireless- and mobility-related view
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The Constrained Application Protocol for pervasive machine-to-machine communications
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An Incremental CFS Algorithm for Clustering Large Data in Industrial Internet of Things
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A Robust Fuzzy Local Information C-Means Clustering Algorithm
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SOSLite: Lightweight Sensor Observation Service (SOS)
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K-Means-Type Algorithms: A Generalized Convergence Theorem and Characterization of Local Optimality
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An efficient k-means clustering algorithm: analysis and implementation
inferredcites →https://doi.org/10.1109/wf-iot.2015.7389057openalex-snapshot
Device clustering for fault monitoring in Internet of Things systems
inferredcites →https://doi.org/10.1109/wf-iot.2015.7389112openalex-snapshot
Resource discovery in Internet of Things: Current trends and future standardization aspects
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A Hybrid Search Engine Framework for the Internet of Things
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A Federated Discovery Service for the Internet of Things
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A Categorization of Discovery Technologies for the Internet of Things
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Computer Evaluation of Indexing and Text Processing
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A Fast Density-Based Clustering Algorithm for Real-Time Internet of Things Stream
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Multiorder Fusion Data Privacy-Preserving Scheme for Wireless Sensor Networks
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Is Metabolic Syndrome Predictive of Prevalence, Extent, and Risk of Coronary Artery Disease beyond Its Components? Results from the Multinational Coronary CT Angiography Evaluation for Clinical Outcome: An International Multicenter Registry (CONFIRM)
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IoT Big-Data Centred Knowledge Granule Analytic and Cluster Framework for BI Applications: A Case Base Analysis
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An efficient indexing and query mechanism for ubiquitous IoT services
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Fuzzy Cluster Analysis: Methods for Classification, Data Analysis and Image Recognition
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A Solution Based on Bluetooth Low Energy for Smart Home Energy Management
inferredcites →https://doi.org/10.3390/s16050600openalex-snapshot
Search Techniques for the Web of Things: A Taxonomy and Survey
inferredcites →https://doi.org/10.3390/s16081264openalex-snapshot
Human Behavior Analysis by Means of Multimodal Context Mining
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Hybrid Genetic Algorithm with K-Means for Clustering Problems
inferredcites →https://doi.org/10.48550/arxiv.1703.00391openalex-snapshot
A Hypercat-enabled Semantic Internet of Things Data Hub: Technical Report

What points at this work

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

inferred← citeshttps://doi.org/10.1002/dac.4259openalex-snapshot
A cloud service composition method using a trust‐based clustering algorithm and honeybee mating optimization algorithm
inferred← citeshttps://doi.org/10.1007/s11227-020-03315-wopenalex-snapshot
Optimized clustering-based discovery framework on Internet of Things
inferred← citeshttps://doi.org/10.1007/s11227-021-03824-2openalex-snapshot
Resource discovery in the Internet of Things integrated with fog computing using Markov learning model
inferred← citeshttps://doi.org/10.1007/s12065-021-00580-3openalex-snapshot
Social Internet of Things: a novel selection approach for dynamic resources substitution
inferred← citeshttps://doi.org/10.1007/s12083-022-01349-wopenalex-snapshot
A Mixed-integer programming model using particle swarm optimization algorithm for resource discovery in the cloudiot
inferred← citeshttps://doi.org/10.1016/j.comcom.2020.01.026openalex-snapshot
Dynamic clustering method based on power demand and information volume for intelligent and green IoT
inferred← citeshttps://doi.org/10.1108/ijpcc-04-2019-0036openalex-snapshot
A weighted resource discovery approach in grid computing
inferred← citeshttps://doi.org/10.1109/iecon.2019.8927612openalex-snapshot
A three level sensor ranking method based on active perception
inferred← citeshttps://doi.org/10.1109/jsen.2020.3026647openalex-snapshot
On-Demand Efficient Clustering for Next Generation IoT Applications: A Hybrid NN Approach
inferred← citeshttps://doi.org/10.1109/peeec60561.2023.00151openalex-snapshot
Internet of Things Routing Algorithm Based on Fuzzy K-means Clustering of Big Data
inferred← citeshttps://doi.org/10.1109/smc.2019.8913969openalex-snapshot
Service discovery techniques in Internet of Things: a survey
inferred← citeshttps://doi.org/10.1145/3588340.3588488openalex-snapshot
Research on Feature Fusion Method of High Attribute Dimension Data in Internet of Things Based on Dividing Thoughts
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Research on Data Security Detection Algorithm in IoT Based on K-means
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CHALLENGES OF INFLUENCE DYNAMIC AND INTERACTIVE EVENTS ON RESOURCE DISCOVERY FUNCTIONALITY OUTSIDE OF DISTRIBUTED EXASCALE SYSTEMS
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CHALLENGES OF USING THE FUZZY APPROACH IN EXASCALE COMPUTING SYSTEMS
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