Grids of agents for computer and telecommunication network management

Assunção, Marcos Dias de; Koch, Fernando; Westphall, Carlos Becker

doi:10.1002/cpe.820

Cited by 7 publications

(2 citation statements)

References 13 publications

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“…We will also look into security issues and intend to investigate the use of Intrusion Detection [20,21,22] and others to safeguard the IoT control environment. Finally, we seek to include methods of Computational Intelligence, Machine Learning, and Distributed AI [23,24] to estimate calculations, considering parameters RFIDbased presence identification, exit and entry times, environment conditions, the average waiting queue time variation, and others.…”

Section: Discussionmentioning

confidence: 99%

Autonomic Management of Power Consumption with IoT and Fog Computing

Sampaio,

Koch,

Westphall

et al. 2021

Preprint

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We introduce a system for Autonomic Management of Power Consumption in setups that involve Internet of Things (IoT) and Fog Computing. The Central IoT (CIoT) is a Fog Computing based solution to provide advanced orchestration mechanisms to manage dynamic duty cycles for extra energy savings. The solution works by adjusting Home (H) and Away (A) cycles based on contextual information, like environmental conditions, user behavior, behavior variation, regulations on energy and network resources utilization, among others. Performance analysis through a proof of concept present average energy savings of 58.4%, reaching up to 61.51% when augmenting with a scheduling system and variable long sleep cycles (LS). However, there is no linear relation increasing LS time and more savings. The significance of this research is to promote autonomic management as a solution to develop more energy efficient buildings and smarter cities, towards sustainable goals.

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Section: Discussionmentioning

confidence: 99%

Autonomic Management of Power Consumption with IoT and Fog Computing

Sampaio,

Koch,

Westphall

et al. 2021

Preprint

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“…There is a cohort of research looking into how to improve IDS towards quick detection of malicious or unauthorised actions [19], [20], and intelligent management methods of Distribute Computing [6], [21]. In general, an IDS encompasses:…”

Section: Background and Related Workmentioning

confidence: 99%

Autonomic Intrusion Response in Distributed Computing using Big Data

Vieira¹,

Koch²,

Sobral³

et al. 2018

Preprint

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We introduce a method for Intrusion Detection based on the classification, understanding and prediction of behavioural deviance and potential threats, issuing recommendations, and acting to address eminent issues. Our work seeks a practical solutions to automate the process of identification and response to Cybersecurity threats in hybrid Distributed Computing environments through the analysis of large datasets generated during operations. We are motivated by the growth in utilisation of Cloud Computing and Edge Computing as the technology for business and social solutions. The technology mix and complex operation render these environments target to attacks like hijacking, man-in-the-middle, denial of service, phishing, and others. The Autonomous Intrusion Response System implements innovative models of data analysis and context-aware recommendation systems to respond to attacks and self-healing. We introduce a proof-of-concept implementation and evaluate against datasets from experimentation scenarios based on public and private clouds. The results present significant improvement in response effectiveness and potential to scale to large environments.

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