Adaptive configuration of IoT applications in the fog infrastructure

Chouat, Houda; Abbassi, Imed; Graiet, Mohamed; Südholt, Mario

doi:10.1007/s00607-023-01191-9

Computing

2023

DOI: 10.1007/s00607-023-01191-9

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Adaptive configuration of IoT applications in the fog infrastructure

Houda Chouat

Imed Abbassi

Mohamed Graiet

et al.

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Cited by 4 publications

(1 citation statement)

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“…Intelligent creation of trusted edge data for distributed IoT; • Adaptive configuration of IoT applications in fog infrastructure; • Utilization of cryptographic schemes for secure edge processing, including server deployment in multi-user edge processing; • Intelligent management of resources in fog computing using RL, including multi-user placement of IoT services with QoS consideration [52][53][54][55][56][57][58][59][60][61][62].…”

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confidence: 99%

Autonomous Threat Response at the Edge Processing Level in the Industrial Internet of Things

Czeczot,

Rojek,

Mikołajewski

2024

Electronics

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Industrial Internet of Things (IIoT) technology, as a subset of the Internet of Things (IoT) in the concept of Industry 4.0 and, in the future, 5.0, will face the challenge of streamlining the way huge amounts of data are processed by the modules that collect the data and those that analyse the data. Given the key features of these analytics, such as reducing the cost of building massive data centres and finding the most efficient way to process data flowing from hundreds of nodes simultaneously, intermediary devices are increasingly being used in this process. Fog and edge devices are hardware devices designed to pre-analyse terabytes of data in a stream and decide in realtime which data to send for final analysis, without having to send the data to a central processing unit in huge local data centres or to an expensive cloud. As the number of nodes sending data for analysis via collection and processing devices increases, so does the risk of data streams being intercepted. There is also an increased risk of attacks on this sensitive infrastructure. Maintaining the integrity of this infrastructure is important, and the ability to analyse all data is a resource that must be protected. The aim of this paper is to address the problem of autonomous threat detection and response at the interface of sensors, edge devices, cloud devices with historical data, and finally during the data collection process in data centres. Ultimately, we would like to present a machine learning algorithm with reinforcements adapted to detect threats and immediately isolate infected nests.

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confidence: 99%

Autonomous Threat Response at the Edge Processing Level in the Industrial Internet of Things

Czeczot,

Rojek,

Mikołajewski

2024

Electronics

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AI enabled: a novel IoT-based fake currency detection using millimeter wave (mmWave) sensor

Niaz,

Zhang,

Khalid

et al. 2024

Computing

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Research on IoT Service Composition Optimization Based on Improved NSGA-II Algorithm

邱

2023

CSA

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Adaptive configuration of IoT applications in the fog infrastructure

Cited by 4 publications

References 30 publications

Autonomous Threat Response at the Edge Processing Level in the Industrial Internet of Things

Autonomous Threat Response at the Edge Processing Level in the Industrial Internet of Things

AI enabled: a novel IoT-based fake currency detection using millimeter wave (mmWave) sensor

Research on IoT Service Composition Optimization Based on Improved NSGA-II Algorithm

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