Machine Learning for IoT

Firouzi, Farshad; Farahani, Bahar; Ye, Fangming; Barzegari, Mojtaba

doi:10.1007/978-3-030-30367-9_5

Cited by 11 publications

(10 citation statements)

References 5 publications

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“…The simulation is performed in Orange data analytics tool [14] on a machine of 8GB RAM and a Core-i3 processor. The supervised models considered for this simulation are RF, kNN, NN, SVM, Tree, NB, AB, and LR [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] for the selection of the best model using the performance metrics like AUC, CA, F1, precision and recall. The performance metrics can also be referred from [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30].…”

Section: Resultsmentioning

confidence: 99%

“…The supervised models considered for this simulation are RF, kNN, NN, SVM, Tree, NB, AB, and LR [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] for the selection of the best model using the performance metrics like AUC, CA, F1, precision and recall. The performance metrics can also be referred from [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30]. However, we mainly focus on the CA of the models for selecting the best model for the proposed system to classify the device category.…”

Section: Resultsmentioning

confidence: 99%

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A Cloud Based Framework for Identification of IoT Devices at Smart Home Using Supervised Machine Intelligence Model

Bhoi

2022

IJAEML

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Purpose: Identification of Internet of Thing (IoT) devices in smart home is the most important function for a local server/controller to administer and control the home smoothly. The IoT devices continuously send and receive requests, acknowledgments, packets, etc. for efficient data communication and these communication patterns need to be classified. Design/Methodology/Approach: Therefore, to run the smart home smoothly, in this work a framework using cloud computing is proposed to identify the correct IoT device communicating with the local server based on supervised machine learning. The best supervised machine intelligence model will be installed at the local server to classify the devices on the basis of data communication patterns. Findings/Result: Simulation is performed using Orange 3.26 data analytics tool by considering an IoT devices data communication dataset collected from Kaggle data repository. From the simulation results it is observed that Random Forest (RF) shows better performance than existing supervised machine learning models in terms of classification accuracy (CA) to classify the IoT devices with high accuracy. Originality/Value: A cloud based framework is proposed for a smart home to identify the correct IoT device communicating with the local server based on supervised machine learning. Based on the data communication pattern of the IoT devices, an IoT device is accurately identified. Paper Type: Methodology Paper.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

A Cloud Based Framework for Identification of IoT Devices at Smart Home Using Supervised Machine Intelligence Model

Bhoi

2022

IJAEML

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“…AI and IoT have also been blending, and several businesses have already adopted AI in their IoT applications [20]. While IoT deals with data collection and device management, AI simulates smart behavior and enables automation, intelligence, reasoning, planning, and perception.…”

Section: Introductionmentioning

confidence: 99%

“…In general, the fusion of AI and IoT, known as the Artificial/Augmented Intelligence of Things (AIoT), empowers data management and analytics, enhances human-machine interactions, and improves the efficiency of IoT. The IoT's highest potential can only be achieved through combination with AI -including, Machine Learning (ML), Deep Learning (DL), augmented intelligence, and big data analytics -that continues to remove the barriers and obstacles found in conventional IoT models [21], [22], [23], [20], [24], [25], [26]. The following are some of the most common benefits of merging these two disruptive streams [27], [28], [15], [29], [30]: (1) The precise value of IoT is specified by its analysis step and AI can wring insights from the generated data unlocking IoT potentials.…”

Section: Introductionmentioning

confidence: 99%

Fusion of IoT, AI, Edge–Fog–Cloud, and Blockchain: Challenges, Solutions, and a Case Study in Healthcare and Medicine

Firouzi

Jiang

Chakrabarty

et al. 2023

IEEE Internet Things J.

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The digital transformation is characterized by the convergence of technologies -from the Internet of Things (IoT) to Edge-Fog-Cloud computing, Artificial Intelligence (AI), and Blockchain -in multiple dimensions blurring the lines between the physical and digital worlds. Although these innovations have evolved independently over time, they are increasingly becoming more intertwined, driving the development of new business models. With more adaptation, embracement, and development, we are witnessing a steady convergence and fusion of these technologies resulting in an unprecedented paradigm shift that is expected to disrupt and reshape the next generation systems in vertical domains in a way that the capabilities of the technologies are aligned in the best possible way to complement each other. Despite the fact that the convergence of the four technologies can potentially tackle the main shortcomings of existing systems, its adoption is still in its infancy phase, suffering from several issues, such as the absence of consensus towards any reference models or best practices. This paper provides a comprehensive insight into the fusions of these paradigms by discussing a blend of topics addressing all the importation aspects from design to deployment. We will begin this paper by providing an in-depth discussion on the main requirements, state-of-the-art reference architectures, applications, and challenges. Following this, we will present a reference architecture and a case study on privacy-preserving stress monitoring and management to better elaborate on the corresponding details and considerations.

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“…The AI technology has many algorithms for solving the classification, regression, and clustering problems. The algorithms mostly used in machine learning (ML) [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27] are supervised, unsupervised, and hybrid. So, ML is also an important component of the proposed design where the classifier installed in the cloud will detect the actual disease.…”

Section: Introductionmentioning

confidence: 99%

A Machine Intelligent Framework for Detection of Rice Leaf Diseases in Field Using IoT Based Unmanned Aerial Vehicle System

Bhoi¹,

K²,

Thinakaran³

2022

STAIQC

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Rice is an important food in our day-to-day life. It has rich sources of carbohydrates that are highly essential for body growth and development. Rice is an important crop in agriculture, where it enhances a country’s economy. However, if rice plants arediseased and not monitored regularly then the crop in the field is wasted and it reduces the proper production rate. Therefore, there should be a mechanism which regularly monitors the crop in a field to detect any disease to rice plant. In this paper, a framework is proposed for identification of rice leaf disease using IoT based Unmanned Aerial Vehicle (UAV) system. Here, the UAV monitors an entire field, capture the images and sends the images to the machine intelligent cloud for detection of rice leaf diseases. The cloud is installed with a proposed stacking classifier that classify the diseased rice plant images received from UAV into different categories. The dataset of these rice leaf diseases is collected from Kaggle source. The performance of the stacking classifier installed at the cloud is evaluated using Python based Orange 3.26 tool. It is observed form the results that stacking classifier outperforms the conventional machine learning models in detecting the actual disease with a classification accuracy (CA) of 86.7%.

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Machine Learning for IoT

Cited by 11 publications

References 5 publications

A Cloud Based Framework for Identification of IoT Devices at Smart Home Using Supervised Machine Intelligence Model

A Cloud Based Framework for Identification of IoT Devices at Smart Home Using Supervised Machine Intelligence Model

Fusion of IoT, AI, Edge–Fog–Cloud, and Blockchain: Challenges, Solutions, and a Case Study in Healthcare and Medicine

A Machine Intelligent Framework for Detection of Rice Leaf Diseases in Field Using IoT Based Unmanned Aerial Vehicle System

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