IoT solution for monitoring indoor climate parameters in open space offices

Udrea, Ioana; Gheorghe, Viorel; Cartal, Laurentiu Adrian; Duminică, Despina; Petrache, Silviu; Apostolescu, Tudor Catalin; Kraus, Valeru Friedemann

doi:10.1051/e3sconf/202018002012

Cited by 11 publications

(8 citation statements)

References 6 publications

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“…While [1] is credited with coining the term "federated learning," the first description of its implementation can be found in [2]. Multiple devices work together to train a shared model in federated learning.…”

Section: Introductionmentioning

confidence: 99%

Federated Deep Learning Approaches for the Privacy and Security of IoT Systems

Alazzam

Alassery

Almulihi

2022

Wireless Communications and Mobile Computing

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Using federated learning, which is a distributed machine learning approach, a machine learning model can train on a distributed data set without having to transfer any data between computers. Instead of using a centralised server for training, the model uses data stored locally on the device itself. After that, the server uses this model to create a jointly trained model. Federated learning asserts that privacy is preserved because no data is sent. Botnet attacks are detected using on-device decentralised traffic statistics and a deep autoencoder. This proposed federated learning approach addresses privacy and security concerns about data privacy and security rather than allowing data to be transferred or relocated off the network edge. In order to get the intended results of a previously centralised machine learning technique while also increasing data security, computation will be shifted to the edge layer. Up to 98% accuracy is achieved in anomaly detection with our proposed model using features like MAC IP and source/destination/IP for training. Our solution outperforms a standard centrally managed system in terms of attack detection accuracy, according to our comparative performance analysis.

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Section: Introductionmentioning

confidence: 99%

Federated Deep Learning Approaches for the Privacy and Security of IoT Systems

Alazzam

Alassery

Almulihi

2022

Wireless Communications and Mobile Computing

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“…While Badotra et al [1] are credited with coining the term "Medical Applications", the first description of its implementation can be found in [2]. Multiple devices work together to train a shared model in Medical Applications.…”

Section: Introductionmentioning

confidence: 99%

Approaches to Federated Computing for the Protection of Patient Privacy and Security Using Medical Applications

Ahmed

Omer

Alshawwa

et al. 2022

Applied Bionics and Biomechanics

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Computing model may train on a distributed dataset using Medical Applications, which is a distributed computing technique. Instead of a centralised server, the model trains on device data. The server then utilizes this model to train a joint model. The aim of this study is that Medical Applications claims no data is transferred, thereby protecting privacy. Botnet assaults are identified through deep autoencoding and decentralised traffic analytics. Rather than enabling data to be transmitted or relocated off the network edge, the problem of the study is in privacy and security in Medical Applications strategies. Computation will be moved to the edge layer to achieve previously centralised outcomes while boosting data security. Study Results in our suggested model detects anomalies with up to 98 percent accuracy utilizing MAC IP and source/destination/IP for training. Our method beats a traditional centrally controlled system in terms of attack detection accuracy.

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“…With so many linked IoT devices, any poorly protected online device may impair remote and global Internet safety and resilience [83]. For instance, a malware-infected unprotected refrigerator or television (TV) in the US can send thousands of spam emails to the world via the homeowners' Wi-Fi network [14]. Furthermore, in a hyperconnected society, the capacity for these devices to function in our everyday activities without the need for Internet-enabled gadgets or systems is imperative.…”

Section: Iot Implementation Challengesmentioning

confidence: 99%

“…BIM is used to analyze models, identify conflicts, choose products, and conceptualize the entire project to enhance building projects' performance and quality. Additionally, BIM mainly supports the integrated project implementation concept (IPD) [13], a new approach to project delivery that integrates individuals, systems, business structures, and practices into collaborative processes to eliminate waste and enhance efficiency throughout the project's lifecycle [14]. This means that BIM has become a need for the design and administration of smart cities in the IoT and Big Data era.…”

Section: Introductionmentioning

confidence: 99%