Edge Artificial Intelligence for Internet of Things Devices: Open Challenges

Vanessa  Alvear; Paul Rosero; Vivian Lopez; Diego Peluffo

doi:10.1007/978-3-031-38344-1_30

Edge Artificial Intelligence for Internet of Things Devices: Open Challenges

Vanessa Alvear
, Paul Rosero
, Vivian Lopez
, Diego Peluffo

Data-intensive Systems and Applications

Publikation: Konference artikel i Proceeding eller bog/rapport kapitel › Konferencebidrag i proceedings › Forskning › peer review

Abstract

The advent of the Internet of Things (IoT) has witnessed a rapid proliferation of interconnected devices capable of sensing, collecting, and exchanging vast amounts of data. This data deluge has created a pressing need for efficient processing and analysis at the edge of the network, leading to the emergence of Edge Artificial Intelligence (AI) as a promising solution. Edge AI enables intelligent decision-making and real-time analytics on IoT devices, reducing the latency and bandwidth requirements associated with cloud-based processing. This paper aims to provide a comprehensive overview of the open challenges in deploying Edge AI for IoT devices. We identify and discuss key technical and practical challenges that hinder the widespread adoption and optimal utilization of Edge AI in the IoT ecosystem.

Originalsprog	Engelsk
Titel	New Trends in Disruptive Technologies, Tech Ethics and Artificial Intelligence
Antal sider	7
Vol/bind	1452
Forlag	Springer
Publikationsdato	22 jul. 2023
Sider	312-319
ISBN (Trykt)	978-3-031-38343-4
ISBN (Elektronisk)	978-3-031-38344-1
DOI	https://doi.org/10.1007/978-3-031-38344-1_30
Status	Udgivet - 22 jul. 2023
Begivenhed	Disruptive Technologies, Tech Ethics and Artificial Intelligence - Salamanca, Salamanca, Spanien Varighed: 21 jun. 2023 → 23 jun. 2023 https://link.springer.com/conference/dittet

Konference

Konference	Disruptive Technologies, Tech Ethics and Artificial Intelligence
Lokation	Salamanca
Land/Område	Spanien
By	Salamanca
Periode	21/06/2023 → 23/06/2023
Internetadresse	https://link.springer.com/conference/dittet

Navn	Advances in Intelligent Systems and Computing
ISSN	2194-5357

Emneord

IoT
Edge devices
Deep learning
Machine Learning

Adgang til dokumentet

10.1007/978-3-031-38344-1_30

Hybrid Anomaly Detection Model on Trusted IoT Devices
Rosero, P., István, Z., Tözün, P. & Hernandez, W., 7 feb. 2023, I: IEEE Internet of Things Journal. 10, 12
Publikation: Artikel i tidsskrift og konference artikel i tidsskrift › Tidsskriftartikel › Forskning › peer review

Åben adgang
Fil
Smart Farming Robot for Detecting Environmental Conditions in a Greenhouse
Rosero, P., Gordillo, C. & Hernandez, W., 8 jun. 2023, I: IEEE Access. 11
Publikation: Artikel i tidsskrift og konference artikel i tidsskrift › Tidsskriftartikel › Forskning › peer review

Åben adgang
Efficient Lightweight Cryptography Algorithm in IoT Devices with Real-time Criteria
Rosero, P. & Alvear, V., 22 apr. 2022, Proceedings of the 7th International Conference on Internet of Things, Big Data and Security. Bastieri, D. & Willis, G. (red.). SCITEPRESS Digital Library, s. 103-109 6 s.
Publikation: Konference artikel i Proceeding eller bog/rapport kapitel › Bidrag til bog/antologi › Forskning › peer review

Citationsformater

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title = "Edge Artificial Intelligence for Internet of Things Devices: Open Challenges",

abstract = "The advent of the Internet of Things (IoT) has witnessed a rapid proliferation of interconnected devices capable of sensing, collecting, and exchanging vast amounts of data. This data deluge has created a pressing need for efficient processing and analysis at the edge of the network, leading to the emergence of Edge Artificial Intelligence (AI) as a promising solution. Edge AI enables intelligent decision-making and real-time analytics on IoT devices, reducing the latency and bandwidth requirements associated with cloud-based processing. This paper aims to provide a comprehensive overview of the open challenges in deploying Edge AI for IoT devices. We identify and discuss key technical and practical challenges that hinder the widespread adoption and optimal utilization of Edge AI in the IoT ecosystem.",

keywords = "IoT, Edge devices, Deep learning, Machine Learning, IoT, Edge devices, Deep learning, Machine Learning",

author = "Vanessa Alvear and Paul Rosero and Vivian Lopez and Diego Peluffo",

year = "2023",

month = jul,

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doi = "10.1007/978-3-031-38344-1\_30",

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volume = "1452",

series = "Advances in Intelligent Systems and Computing",

publisher = "Springer",

pages = "312--319",

booktitle = "New Trends in Disruptive Technologies, Tech Ethics and Artificial Intelligence",

address = "Germany",

note = "Disruptive Technologies, Tech Ethics and Artificial Intelligence, DiTTET ; Conference date: 21-06-2023 Through 23-06-2023",

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Alvear, V, Rosero, P, Lopez, V & Peluffo, D 2023, Edge Artificial Intelligence for Internet of Things Devices: Open Challenges. i New Trends in Disruptive Technologies, Tech Ethics and Artificial Intelligence. bind 1452, Springer, Advances in Intelligent Systems and Computing, s. 312-319, Disruptive Technologies, Tech Ethics and Artificial Intelligence, Salamanca, Spanien, 21/06/2023. https://doi.org/10.1007/978-3-031-38344-1_30

Edge Artificial Intelligence for Internet of Things Devices: Open Challenges. / Alvear, Vanessa ; Rosero, Paul; Lopez, Vivian et al.
New Trends in Disruptive Technologies, Tech Ethics and Artificial Intelligence. Bind 1452 Springer, 2023. s. 312-319 (Advances in Intelligent Systems and Computing).

Publikation: Konference artikel i Proceeding eller bog/rapport kapitel › Konferencebidrag i proceedings › Forskning › peer review

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T1 - Edge Artificial Intelligence for Internet of Things Devices: Open Challenges

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AU - Lopez, Vivian

AU - Peluffo, Diego

PY - 2023/7/22

Y1 - 2023/7/22

N2 - The advent of the Internet of Things (IoT) has witnessed a rapid proliferation of interconnected devices capable of sensing, collecting, and exchanging vast amounts of data. This data deluge has created a pressing need for efficient processing and analysis at the edge of the network, leading to the emergence of Edge Artificial Intelligence (AI) as a promising solution. Edge AI enables intelligent decision-making and real-time analytics on IoT devices, reducing the latency and bandwidth requirements associated with cloud-based processing. This paper aims to provide a comprehensive overview of the open challenges in deploying Edge AI for IoT devices. We identify and discuss key technical and practical challenges that hinder the widespread adoption and optimal utilization of Edge AI in the IoT ecosystem.

AB - The advent of the Internet of Things (IoT) has witnessed a rapid proliferation of interconnected devices capable of sensing, collecting, and exchanging vast amounts of data. This data deluge has created a pressing need for efficient processing and analysis at the edge of the network, leading to the emergence of Edge Artificial Intelligence (AI) as a promising solution. Edge AI enables intelligent decision-making and real-time analytics on IoT devices, reducing the latency and bandwidth requirements associated with cloud-based processing. This paper aims to provide a comprehensive overview of the open challenges in deploying Edge AI for IoT devices. We identify and discuss key technical and practical challenges that hinder the widespread adoption and optimal utilization of Edge AI in the IoT ecosystem.

KW - IoT

KW - Edge devices

KW - Deep learning

KW - Machine Learning

KW - IoT

KW - Edge devices

KW - Deep learning

KW - Machine Learning

U2 - 10.1007/978-3-031-38344-1_30

DO - 10.1007/978-3-031-38344-1_30

M3 - Article in proceedings

SN - 978-3-031-38343-4

VL - 1452

T3 - Advances in Intelligent Systems and Computing

SP - 312

EP - 319

BT - New Trends in Disruptive Technologies, Tech Ethics and Artificial Intelligence

PB - Springer

T2 - Disruptive Technologies, Tech Ethics and Artificial Intelligence

Y2 - 21 June 2023 through 23 June 2023

ER -

Edge Artificial Intelligence for Internet of Things Devices: Open Challenges

Abstract

Konference

Emneord

Adgang til dokumentet

Fingeraftryk

Publikation

Hybrid Anomaly Detection Model on Trusted IoT Devices

Smart Farming Robot for Detecting Environmental Conditions in a Greenhouse

Efficient Lightweight Cryptography Algorithm in IoT Devices with Real-time Criteria

Citationsformater