Learning Platform for Smart City Application Development

Dobrilovic, Dalibor; Malić, Milan; Malic, Dusan

doi:10.7906/indecs.17.3.1

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…The transport layer components were designed on the basis and experience of the project 6th Sense, which started as a student project for the Texas Instruments competition TI Innovation Challenge 2016 [33]. The prototype sensor nodes evolved from the sensor nodes used in the 6th Sense project, with the incorporation of emerging IoT protocols, such as MQTT [34]. The model described in this paper presents the fusion of these two models, with the hierarchical architecture and heterogeneous connectivity technology based on the first model, and with the MQTT protocol, and messaging bus oriented architecture based on the second model, and with changes in the sensor data packet format.…”

Section: Wpan Sensor Nodesmentioning

confidence: 99%

A Model for Working Environment Monitoring in Smart Manufacturing

et al. 2021

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The growing application of smart manufacturing systems and the expansion of the Industry 4.0 model have created a need for new teaching platforms for education, rapid application development, and testing. This research addresses this need with a proposal for a model of working environment monitoring in smart manufacturing, based on emerging wireless sensor technologies and the message queuing telemetry transport (MQTT) protocol. In accordance with the proposed model, a testing platform was developed. The testing platform was built on open-source hardware and software components. The testing platform was used for the validation of the model within the presented experimental environment. The results showed that the proposed model could be developed by mainly using open-source components, which can then be used to simulate different scenarios, applications, and target systems. Furthermore, the presented stable and functional platform proved to be applicable in the process of rapid prototyping, and software development for the targeted systems, as well as for student teaching as part of the engineering education process.

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Section: Wpan Sensor Nodesmentioning

confidence: 99%

A Model for Working Environment Monitoring in Smart Manufacturing

et al. 2021

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Intrusion detection in the IoT data streams using concept drift localization

Chu,

Jin,

Qiao

et al. 2023

MATH

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<abstract><p>With the widespread application of smart devices, the security of internet of things (IoT) systems faces entirely new challenges. The IoT data stream operates in a non-stationary, dynamic environment, making it prone to concept drift. This paper focused on addressing the issue of concept drift in data streams, with a key emphasis on introducing an innovative drift detection method-ensemble multiple non-parametric concept localization detectors, abbreviated as EMNCD. EMNCD employs an ensemble of non-parametric statistical methods, including the Kolmogorov-Smirnov, Wilcoxon rank sum and Mann-Kendall tests. By comparing sample distributions within a sliding window, EMNCD accurately detects concept drift, achieving precise localization of drift points, and enhancing overall detection reliability. Experimental results demonstrated the superior performance of EMNCD compared to classical methods on artificial datasets. Simultaneously, to enhance the robustness of data stream processing, we presented an online anomaly detection method based on the isolation forest (iForest). Additionally, we proposedwhale optimization algorithm (WOA)-extreme gradient boosting (XGBoost), a drift adaptation model employing XGBoost as a base classifier. This model dynamically updates using drift points detected by EMNCD and fine-tunes parameters through the WOA. Real-world applications on the edge-industrial IoTset (IIoTset) intrusion dataset explore the impact of concept drift on intrusion detection, where IIoT is a subclass of IoT. In summary, this paper focused on EMNCD, introducing innovative approaches for drift detection, anomaly detection, and drift adaptation. The research provided practical and viable solutions to address concept drift in data streams, enhancing security in IoT systems.</p></abstract>

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Learning Platform for Smart City Application Development

Cited by 2 publications

References 11 publications

A Model for Working Environment Monitoring in Smart Manufacturing

A Model for Working Environment Monitoring in Smart Manufacturing

Intrusion detection in the IoT data streams using concept drift localization

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