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dc.contributor.authorFaheem, Muhammad
dc.contributor.authorFizza, Ghulam
dc.contributor.authorAshraf, Muhammad Waqar
dc.contributor.authorButt, Rizwan Aslam
dc.contributor.authorNgadi, Md. Asri
dc.contributor.authorGungor, Vehbi Cagri
dc.identifier.urihttps //
dc.descriptionThis research has been supported by the Universiti Teknologi Malaysia (UTM) , IDFUTM.J.10.01/13.14/1/128 (201801M10702) .en_US
dc.description.abstractSmart Grid Industry 4.0 (SGI4.0) defines a new paradigm to provide high-quality electricity at a low cost by reacting quickly and effectively to changing energy demands in the highly volatile global markets. However, in SGI4.0, the reliable and efficient gathering and transmission of the observed information from the Internet of Things (IoT)-enabled Cyberphysical systems, such as sensors located in remote places to the control center is the biggest challenge for the Industrial Multichannel Wireless Sensors Networks (IMWSNs). This is due to the harsh nature of the smart grid environment that causes high noise, signal fading, multipath effects, heat, and electromagnetic interference, which reduces the transmission quality and trigger errors in the IMWSNs. Thus, an efficient monitoring and real-time control of unexpected changes in the power generation and distribution processes is essential to guarantee the quality of service (QoS) re-quirements in the smart grid. In this context, this paper de-scribes the dataset contains measurements acquired by the IMWSNs during events monitoring and control in the smart grid. This work provides an updated detail comparison of our proposed work, including channel detection, channel assign-ment, and packets forwarding algorithms, collectively called CARP [1] with existing G-RPL [2] and EQSHC [3] schemes in the smart grid. The experimental outcomes show that the dataset and is useful for the design, development, testing, and validation of algorithms for real-time events monitoring and control applications in the smart grid. (C) 2021 The Authors. Published by Elsevier Inc.en_US
dc.description.sponsorshipUniversiti Teknologi Malaysia (UTM) IDFUTM.J.10.01/13.14/1/128 (201801M10702)en_US
dc.subjectInternet of thingsen_US
dc.subjectWireless sensor networksen_US
dc.subjectMultichannel wireless sensor networken_US
dc.subjectSmart griden_US
dc.subjectIndustry 4.0en_US
dc.titleBig Data acquired by Internet of Things-enabled industrial multichannel wireless sensors networks for active monitoring and control in the smart grid Industry 4.0en_US
dc.contributor.departmentAGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.contributor.institutionauthorFaheem, Muhammad
dc.contributor.institutionauthorGungor, Vehbi Cagri
dc.identifier.volumeVolume 35en_US
dc.relation.journalDATA IN BRIEFen_US
dc.relation.publicationcategoryMakale - Uluslararası - Editör Denetimli Dergien_US

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