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

Research output: Conference Article in Proceeding or Book/Report chapter › Article in proceedings › Research › 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.

Original language	English
Title of host publication	New Trends in Disruptive Technologies, Tech Ethics and Artificial Intelligence
Number of pages	7
Volume	1452
Publisher	Springer, Cham
Publication date	22 Jul 2023
Pages	312-319
ISBN (Print)	978-3-031-38343-4
ISBN (Electronic)	978-3-031-38344-1
DOIs	https://doi.org/10.1007/978-3-031-38344-1_30
Publication status	Published - 22 Jul 2023
Event	International Conference on Disruptive Technologies, Tech Ethics and Artificial Intelligence - Salamanca, Salamanca, Spain Duration: 21 Jun 2023 → 23 Jun 2023 https://link.springer.com/conference/dittet

Conference

Conference	International Conference on Disruptive Technologies, Tech Ethics and Artificial Intelligence
Location	Salamanca
Country/Territory	Spain
City	Salamanca
Period	21/06/2023 → 23/06/2023
Internet address	https://link.springer.com/conference/dittet

Series	Advances in Intelligent Systems and Computing
ISSN	2194-5357

Keywords

IoT
Edge devices
Deep learning
Machine Learning

Access to Document

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

2 Journal article
1 Book chapter

Hybrid Anomaly Detection Model on Trusted IoT Devices
Rosero, P., István, Z., Tözün, P. & Hernandez, W., 7 Feb 2023, In: IEEE Internet of Things Journal.
Research output: Journal Article or Conference Article in Journal › Journal article › Research › peer-review

Open Access
Smart Farming Robot for Detecting Environmental Conditions in a Greenhouse
Rosero, P., Gordillo, C. & Hernandez, W., 8 Jun 2023, In: IEEE Access. 11
Research output: Journal Article or Conference Article in Journal › Journal article › Research › peer-review

Open Access
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. (eds.). SCITEPRESS Digital Library, p. 103-109 6 p.
Research output: Conference Article in Proceeding or Book/Report chapter › Book chapter › Research › peer-review

Cite this

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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,

day = "22",

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

language = "English",

isbn = "978-3-031-38343-4",

volume = "1452",

series = "Advances in Intelligent Systems and Computing",

publisher = "Springer, Cham",

pages = "312--319",

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

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

url = "https://link.springer.com/conference/dittet",

}

Alvear, V, Rosero, P, Lopez, V & Peluffo, D 2023, Edge Artificial Intelligence for Internet of Things Devices: Open Challenges. in New Trends in Disruptive Technologies, Tech Ethics and Artificial Intelligence. vol. 1452, Springer, Cham, Advances in Intelligent Systems and Computing, pp. 312-319, International Conference on Disruptive Technologies, Tech Ethics and Artificial Intelligence, Salamanca, Spain, 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. Vol. 1452 Springer, Cham, 2023. p. 312-319 (Advances in Intelligent Systems and Computing).

Research output: Conference Article in Proceeding or Book/Report chapter › Article in proceedings › Research › peer-review

TY - GEN

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AU - Lopez, Vivian

AU - Peluffo, Diego

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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, Cham

T2 - International Conference on 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

Conference

Keywords

Access to Document

Fingerprint

Research output

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

Cite this