A distributed deep learning approach with mobile edge computing for next generation IoT networks security

Shailendra Rathore; Pradip Kumar Sharma; Heena Rathore

doi:10.1109/WCONF58270.2023.10235095

A distributed deep learning approach with mobile edge computing for next generation IoT networks security

Shailendra Rathore, Pradip Kumar Sharma, Heena Rathore

Department of Cybersecurity and Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

109 Downloads (Pure)

Abstract

Along with recent development in Next Generation IoT, the Deep Learning (DL) has become a promising paradigm to perform various tasks such as computation and analysis. Many security researchers have proposed distributed DL supporting DL task at the IoT device level to deliver low latency and high accuracy. However, due to limited computing capabilities of IoT devices, distributed DL is failed to maintain Quality-of-service demand in practical IoT applications. To this end, BlockDeepEdge, a Blockchain-based Distributed DL with Mobile Edge Computing (MEC) is proposed where MEC supports the lightweight IoT devices by delivering computing operations to them at the edge of the network. The blockchain provide a secure, decentralized and P2P interaction among IoT devices and MEC server to carryout distributed DL operation.

Original language	English
Title of host publication	2023 World Conference on Communication & Computing (WCONF)
Subtitle of host publication	proceedings
Publisher	IEEE
Number of pages	3
ISBN (Electronic)	9798350311204, 9798350311181, 9798350311198
ISBN (Print)	9798350322767
DOIs	https://doi.org/10.1109/WCONF58270.2023.10235095
Publication status	Published - 4 Sept 2023
Event	2023 World Conference on Communication & Computing - Kalinga University, Raipur, India Duration: 14 Jul 2023 → 16 Jul 2023 https://wconf.in/

Conference

Conference	2023 World Conference on Communication & Computing
Abbreviated title	WCONF 2023
Country/Territory	India
City	Raipur
Period	14/07/23 → 16/07/23
Internet address	https://wconf.in/

Keywords

IoT
5G
Distributed deep learning
Mobile edge computing
Blockchain

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10.1109/WCONF58270.2023.10235095

Rathore_ADistributedDeepLearningApproach_Accepted_2023Accepted author manuscript, 490 KB

Cite this

@inproceedings{2df51a9f28b440039f60d4ec21538814,

title = "A distributed deep learning approach with mobile edge computing for next generation IoT networks security",

abstract = "Along with recent development in Next Generation IoT, the Deep Learning (DL) has become a promising paradigm to perform various tasks such as computation and analysis. Many security researchers have proposed distributed DL supporting DL task at the IoT device level to deliver low latency and high accuracy. However, due to limited computing capabilities of IoT devices, distributed DL is failed to maintain Quality-of-service demand in practical IoT applications. To this end, BlockDeepEdge, a Blockchain-based Distributed DL with Mobile Edge Computing (MEC) is proposed where MEC supports the lightweight IoT devices by delivering computing operations to them at the edge of the network. The blockchain provide a secure, decentralized and P2P interaction among IoT devices and MEC server to carryout distributed DL operation.",

author = "Shailendra Rathore and Sharma, \{Pradip Kumar\} and Heena Rathore",

note = "{\textcopyright}2023 IEEE Data availability statement: Not present.; 2023 World Conference on Communication \& Computing, WCONF 2023 ; Conference date: 14-07-2023 Through 16-07-2023",

year = "2023",

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doi = "10.1109/WCONF58270.2023.10235095",

language = "English",

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booktitle = "2023 World Conference on Communication \& Computing (WCONF)",

publisher = "IEEE ",

url = "https://wconf.in/",

}

TY - GEN

T1 - A distributed deep learning approach with mobile edge computing for next generation IoT networks security

AU - Rathore, Shailendra

AU - Sharma, Pradip Kumar

AU - Rathore, Heena

PY - 2023/9/4

Y1 - 2023/9/4

N2 - Along with recent development in Next Generation IoT, the Deep Learning (DL) has become a promising paradigm to perform various tasks such as computation and analysis. Many security researchers have proposed distributed DL supporting DL task at the IoT device level to deliver low latency and high accuracy. However, due to limited computing capabilities of IoT devices, distributed DL is failed to maintain Quality-of-service demand in practical IoT applications. To this end, BlockDeepEdge, a Blockchain-based Distributed DL with Mobile Edge Computing (MEC) is proposed where MEC supports the lightweight IoT devices by delivering computing operations to them at the edge of the network. The blockchain provide a secure, decentralized and P2P interaction among IoT devices and MEC server to carryout distributed DL operation.

AB - Along with recent development in Next Generation IoT, the Deep Learning (DL) has become a promising paradigm to perform various tasks such as computation and analysis. Many security researchers have proposed distributed DL supporting DL task at the IoT device level to deliver low latency and high accuracy. However, due to limited computing capabilities of IoT devices, distributed DL is failed to maintain Quality-of-service demand in practical IoT applications. To this end, BlockDeepEdge, a Blockchain-based Distributed DL with Mobile Edge Computing (MEC) is proposed where MEC supports the lightweight IoT devices by delivering computing operations to them at the edge of the network. The blockchain provide a secure, decentralized and P2P interaction among IoT devices and MEC server to carryout distributed DL operation.

U2 - 10.1109/WCONF58270.2023.10235095

DO - 10.1109/WCONF58270.2023.10235095

M3 - Conference contribution

SN - 9798350322767

BT - 2023 World Conference on Communication & Computing (WCONF)

PB - IEEE

T2 - 2023 World Conference on Communication & Computing

Y2 - 14 July 2023 through 16 July 2023

ER -

A distributed deep learning approach with mobile edge computing for next generation IoT networks security

Abstract

Conference

Keywords

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