Distributed data mining based on actors for Internet of Things

Kholod, Ivan; Kuprianov, Mikhail; Petukhov, Ilya

doi:10.1109/meco.2016.7525698

Cited by 13 publications

(6 citation statements)

References 7 publications

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“…This technique is used for data prediction [218,219], activity recognition [220,221], and data classification [222,223]. Next, data mining appears with 89 documents, with distributed data mining [224], and applications such as event detection [225,226] as sub-techniques. Complex event processing is a method of tracking and analyzing streams of data surrounding events or anomalies and basing a conclusion from them [227].…”

Section: Software Processing Techniquesmentioning

confidence: 99%

Internet of Things: A Scientometric Review

et al. 2017

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Abstract:Internet of Things (IoT) is connecting billions of devices to the Internet. These IoT devices chain sensing, computation, and communication techniques, which facilitates remote data collection and analysis. wireless sensor networks (WSN) connect sensing devices together on a local network, thereby eliminating wires, which generate a large number of samples, creating a big data challenge. This IoT paradigm has gained traction in recent years, yielding extensive research from an increasing variety of perspectives, including scientific reviews. These reviews cover surveys related to IoT vision, enabling technologies, applications, key features, co-word and cluster analysis, and future directions. Nevertheless, we lack an IoT scientometrics review that uses scientific databases to perform a quantitative analysis. This paper develops a scientometric review about IoT over a data set of 19,035 documents published over a period of 15 years (2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016) in two main scientific databases (Clarivate Web of Science and Scopus). A Python script called ScientoPy was developed to perform quantitative analysis of this data set. This provides insight into research trends by investigating a lead author's country affiliation, most published authors, top research applications, communication protocols, software processing, hardware, operating systems, and trending topics. Furthermore, we evaluate the top trending IoT topics and the popular hardware and software platforms that are used to research these trends.

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Section: Software Processing Techniquesmentioning

confidence: 99%

Internet of Things: A Scientometric Review

et al. 2017

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“…If the increasing demands and rising volume of data is not identified and handled, then it will restrict us in various ways. 106 This section describes the applications of data mining (DM) techniques in the processes of data transfer, processing, and storage. There are various techniques that are used in processing, transmission, and storage of big data.…”

Section: Big Data Mining In 5g-driven Iot Environmentmentioning

confidence: 99%

“…Data mining techniques have proven to be useful in the analysis of gathered data 107,108 and improving IoT intelligence. 106…”

Section: Big Data Mining In 5g-driven Iot Environmentmentioning

confidence: 99%

Leveraging big data analytics in 5G‐enabled IoT and industrial IoT for the development of sustainable smart cities

Mukherjee

Gupta

Rawlley

et al. 2022

Trans Emerging Tel Tech

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There has been an exponential growth in the number of low‐cost heterogeneous sensor devices that are connected to the internet in the existing infrastructure of smart cities in the past decade. These sensors and actuator devices are employed in various industries to collect invaluable data that greatly impacts business decisions. State‐of‐the‐art research is being carried out to process the high‐volume data collected in high velocity streams with high variability in order to draw meaningful insights to cater to business needs in various domains. Big data analytics finds diversified opportunities in 5G‐enabled Internet of Things (IoT) and industrial IoT environment to study the data patterns and produce new results which provides big organizations a conducive environment to take informed decisions. This article presents an exhaustive investigation of the various applications and algorithms of the big data analytics in 5G‐driven IoT and industrial IoT systems with a detailed taxonomy of the existing analytical systems and also the challenges specific to the applications in an IoT environment. A holistic understanding on the importance of security and privacy of big data during the development of smart city infrastructure in high velocity streaming process has been explained for a broad understanding of the big data streaming processes. A thorough analysis of important data mining algorithms such as classification, association rule mining, clustering, and prediction methods for big IoT data from the recent literature also has been presented to provide a clear understanding of existing issues in handling big data in 5G‐enabled IoT for the development of sustainable smart city infrastructure.

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“…In addition, with the emerging advancements in UAVs with new application domains requiring huge datasets are often spatially-distributed while being supplied by numerous parties that can be mutually untrustworthy [24]. For instance, UAVs having IoT devices continuously sense the environment and may contribute their data so that a unified global model can be constructed.…”

Section: Introductionmentioning

confidence: 99%

A blockchain-based decentralized machine learning framework for collaborative intrusion detection within UAVs

Khan

et al. 2021

Computer Networks

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Distributed data mining based on actors for Internet of Things

Cited by 13 publications

References 7 publications

Internet of Things: A Scientometric Review

Internet of Things: A Scientometric Review

Leveraging big data analytics in 5G‐enabled IoT and industrial IoT for the development of sustainable smart cities

A blockchain-based decentralized machine learning framework for collaborative intrusion detection within UAVs

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