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Building Efficient IoT Systems with Edge Computing Integration

International Journal of Cyber and IT Service Management · Vol. 4 No. 2 (2024) · hlm. 72–79

DOI
10.34306/ijcitsm.v4i2.163
ISSN
2808-554X, 2797-1325
Terbit
2024-10-02
Penerbit
Pandawan Sejahtera Indonesia
Bahasa
en
Lisensi
creativecommons.org/licenses/by/4.0/

Abstrak

The exponential growth of the Web of Things (IoT) is transforming businesses, connecting billions of devices that generate massive amounts of data. However, preparing this data at scale in real time poses significant challenges, including inactivity, transmission capacity constraints, and data blocking in centralized cloud systems. Edge computing has become an urgent solution. It allows data preparation to occur closer to the source, thereby improving operational productivity, reducing idle time, and optimizing transmission capacity. This shift toward local availability reduces the burden on centralized cloud systems, making IoT systems more responsive and robust. This article examines the integration of edge computing with IoT. It highlights the fundamental advances that have made this connection possible. Key applications, such as real-time analytics, vision support, and edge AI, describe how edge computing improves data processing and enhances independent decision-making at the device level. Additionally, we discuss how advances in hardware, orchestration techniques, and machine learning drive the development of edge-enabled IoT environments. By analyzing these current uses, we identify emerging trends that will shape future IoT systems, making them more adaptive, efficient, and resilient to changing data demands. This survey highlights the potential of edge computing to power next-generation IoT systems, providing important insights for businesses looking to support complete control of the devices involved.

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40 penulis · 149 tautan kepenulisan bersama · 59 lintas organisasi · menampilkan 40 yang paling banyak terhubung

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  • Universitas Raharja
  • Satya Wacana Christian University
  • Universitas Catur Insan Cendekia Cirebon
  • Indonesia Open University
  • Sunan Kalijaga State Islamic University Yogyakarta
  • Universitas Banten Jaya
  • Universitas Esa Unggul
  • Organisasi lain
  • Afiliasi tidak diketahui
  • Organisasi berbeda
  • Organisasi sama
  • Afiliasi tidak diketahui
  • Garis lebih tebal = lebih banyak artikel bersama · lingkaran lebih besar = lebih banyak artikel
Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
Qurotul AiniUniversitas Raharja23
Ninda LutfianiUniversitas Raharja12
Danny ManonggaSatya Wacana Christian University8
Irwan SembiringSatya Wacana Christian University8
Nuke Puji Lestari SantosoUniversitas Raharja6
Dwi ApriliasariAfiliasi tidak diketahui5
Alfiah KhoirunisaUniversitas Raharja4
Po Abas SunaryaUniversitas Raharja4
Fitra Putri OgandaUniversitas Raharja3
Sutarto WijonoSatya Wacana Christian University3
Shofiyul MillahUniversitas Raharja3
Chandra LukitaUniversitas Catur Insan Cendekia Cirebon2
Eka Purnama HarahapUniversitas Raharja2
Sudaryono SudaryonoUniversitas Banten Jaya2
Desy AprianiUniversitas Raharja2
Nur AzizahUniversitas Raharja2
Mohamad RakhmansyahUniversitas Raharja2
Ihsan Nuril HikamUniversitas Raharja2
Agung RizkyUniversitas Raharja2
Adam FaturahmanUniversitas Raharja2
Natasya Aprila YusufUniversitas Raharja2
Umi NarimawatiUniversitas Komputer Indonesia2
Joko SiswantoPoliteknik Keselamatan Transportasi Jalan2
Hindriyanto Dwi PurnomoSatya Wacana Christian University2
Lista MeriaUniversitas Esa Unggul2
Indri HandayaniUniversitas Raharja1
Sudaryono SudaryonoUniversitas Gadjah Mada1
Firman HanafiUniversitas Raharja1
Suwandi SuwandiUniversitas Catur Insan Cendekia Cirebon1
Chairun NasUniversitas Catur Insan Cendekia Cirebon1
Rochmawati RochmawatiUniversitas Raharja1
Eko SediyonoSatya Wacana Christian University1
Sri Yulianto Joko PrasetyoSatya Wacana Christian University1
Diana MarinaIPB University1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) https://iiast.iaic-publisher.org/ijcitsm/index.php/IJCITSM/oai, pertama di-harvest 6 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc)
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	<dc:title xml:lang="en">Building Efficient IoT Systems with Edge Computing Integration</dc:title>
	<dc:creator xml:lang="en">Hidayati, Dini</dc:creator>
	<dc:creator xml:lang="en">Andriyansah, Andriyansah</dc:creator>
	<dc:creator xml:lang="en">Cesna, Galih Putra</dc:creator>
	<dc:creator xml:lang="en">Fauzi, Ahmad Yadi</dc:creator>
	<dc:creator xml:lang="en">Apriliasari, Dwi</dc:creator>
	<dc:creator xml:lang="en">Rahardja, Untung</dc:creator>
	<dc:subject xml:lang="en">IoT</dc:subject>
	<dc:subject xml:lang="en">Edge Computing</dc:subject>
	<dc:subject xml:lang="en">Real-Time Data</dc:subject>
	<dc:subject xml:lang="en">Scalability</dc:subject>
	<dc:subject xml:lang="en">Latency</dc:subject>
	<dc:description xml:lang="en">The exponential growth of the Web of Things (IoT) is transforming businesses, connecting billions of devices that generate massive amounts of data. However, preparing this data at scale in real time poses significant challenges, including inactivity, transmission capacity constraints, and data blocking in centralized cloud systems. Edge computing has become an urgent solution. It allows data preparation to occur closer to the source, thereby improving operational productivity, reducing idle time, and optimizing transmission capacity. This shift toward local availability reduces the burden on centralized cloud systems, making IoT systems more responsive and robust. This article examines the integration of edge computing with IoT. It highlights the fundamental advances that have made this connection possible. Key applications, such as real-time analytics, vision support, and edge AI, describe how edge computing improves data processing and enhances independent decision-making at the device level. Additionally, we discuss how advances in hardware, orchestration techniques, and machine learning drive the development of edge-enabled IoT environments. By analyzing these current uses, we identify emerging trends that will shape future IoT systems, making them more adaptive, efficient, and resilient to changing data demands. This survey highlights the potential of edge computing to power next-generation IoT systems, providing important insights for businesses looking to support complete control of the devices involved.</dc:description>
	<dc:publisher xml:lang="en">Pandawan Sejahtera Indonesia</dc:publisher>
	<dc:date>2024-10-02</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://iiast.iaic-publisher.org/ijcitsm/IJCITSM/article/view/163</dc:identifier>
	<dc:identifier>10.34306/ijcitsm.v4i2.163</dc:identifier>
	<dc:source xml:lang="en">International Journal of Cyber and IT Service Management; Vol. 4 No. 2 (2024): October; 72–79</dc:source>
	<dc:source>2808-554X</dc:source>
	<dc:source>2797-1325</dc:source>
	<dc:source>10.34306/ijcitsm.v4i2</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://iiast.iaic-publisher.org/ijcitsm/IJCITSM/article/view/163/81</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2024 Dini Hidayati, Andriyansah Andriyansah, Galih Putra Cesna, Ahmad Yadi Fauzi, Dwi Apriliasari, Untung Rahardja</dc:rights>
	<dc:rights xml:lang="en">https://creativecommons.org/licenses/by/4.0/</dc:rights>
</oai_dc:dc>