Toward a Monitoring System Based on IoT Devices for Smart Buildings

Elhaloui, Loubna; Elfilali, Sanaa; Tabaa, Mohamed; Benlahmar, El Habib

doi:10.1007/978-981-15-6048-4_25

Cited by 6 publications

(2 citation statements)

References 7 publications

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“…These contracts, enabled by blockchain technology, offer versatile applications with significant implications. They enhance security, trust, and efficiency in diverse domains such as healthcare [3], [4], banking and finance [5], IoT [6], secure data sharing [7], supply chain [8], business [9], [10], education [11], software development methodologies [12], [13], security risk management [14], [15], and legal frameworks [16].…”

Section: Introductionmentioning

confidence: 99%

SCEditor: A Graphical Editor Prototype for Smart Contract Design and Development

Hsain,

Laaz,

Mbarki

2024

IJACSA

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In recent years, particularly with the Ethereum blockchain's advent, smart contracts have gained significant interest as a means of regulating exchanges among multiple parties via code. This surge has prompted the emergence of various smart contract (SC) programming languages, each possessing distinct philosophies, grammatical structures, and components. Consequently, developers are increasingly involved in SC programming. However, these languages are platform specific, implying that a transition to another platform necessitates the use of different languages. Additionally, developers require a certain level of control over SCs to address encountered bugs and ensure maintenance. To address these developer-centric challenges, this paper presents SCEditor, a novel Eclipse Sirius-based prototype editor designed for the visualization, design, and creation of SCs. The editor proposes a means of standardizing the usage of SC programming languages through the incorporation of graphical syntax and a metamodel conforming to Model-Driven Engineering (MDE) principles and SC construction rules to generate an abstract SC model. The efficacy of this editor is demonstrated through testing on a voting SC written in Vyper and Solidity languages. Furthermore, the editor holds potential for future exploitation in model transformation and code generation for various SC languages.

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Section: Introductionmentioning

confidence: 99%

SCEditor: A Graphical Editor Prototype for Smart Contract Design and Development

Hsain,

Laaz,

Mbarki

2024

IJACSA

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“…In our previous work [3], we presented the IoT system model of a smart building, to allow users to control, identify and access smart devices, thanks to the shared and exchanged data by different network protocols. It, therefore, becomes necessary to be able to secure these various objects.…”

Section: Introductionmentioning

confidence: 99%

Machine learning for internet of things classification using network traffic parameters

Elhaloui¹,

Filali²,

Benlahmar³

et al. 2023

IJECE

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With the growth of the internet of things (IoT) smart objects, managing these objects becomes a very important challenge, to know the total number of interconnected objects on a heterogeneous network, and if they are functioning correctly; the use of IoT objects can have advantages in terms of comfort, efficiency, and cost. In this context, the identification of IoT objects is the first step to help owners manage them and ensure the security of their IoT environments such as smart homes, smart buildings, or smart cities. In this paper, to meet the need for IoT object identification, we have deployed an intelligent environment to collect all network traffic traces based on a diverse list of IoT in real-time conditions. In the exploratory phase of this traffic, we have developed learning models capable of identifying and classifying connected IoT objects in our environment. We have applied the six supervised machine learning algorithms: support vector machine, decision tree (DT), random forest (RF), k-nearest neighbors, naive Bayes, and stochastic gradient descent classifier. Finally, the experimental results indicate that the DT and RF models proved to be the most effective and demonstrate an accuracy of 97.72% on the analysis of network traffic data and more particularly information contained in network protocols. Most IoT objects are identified and classified with an accuracy of 99.21%.

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