Machine learning methods for IoT and their Future Applications

Jindal, Mansi; Gupta, Jatin; Bhushan, Bharat

doi:10.1109/icccis48478.2019.8974551

Cited by 38 publications

(12 citation statements)

References 28 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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“…Extensive efforts have been made by using ML techniques to solve problems in different aspects of IoT, such as communication [5], privacy [6], and security [7] [8]. Sagduyu et al [7] utilised adversarial machine learning to attack and defend IoT networks.…”

Section: Related Workmentioning

confidence: 99%

Privacy-Preserving Federated Deep Learning for Cooperative Hierarchical Caching in Fog Computing

Min

et al. 2022

IEEE Internet Things J.

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Over the past few years, Fog Radio Access Networks (F-RANs) have become a promising paradigm to support the tremendously increasing demands of multimedia services, by pushing computation and storage functionalities towards the edge of networks, closer to users. In F-RANs, distributed edge caching among Fog Access Points (F-APs) can effectively reduce network traffic and service latency as it places popular contents at local caches of F-APs rather than the remote cloud. Due to the limited caching resources of F-APs and spatio-temporally fluctuant content demands from users, many cooperative caching schemes were designed to decide which contents are popular and how to cache them. However, these approaches often collect and analyse the data from Internet-of-Things (IoT) devices at a central server to predict the content popularity for caching, which raises serious privacy issues. To tackle this challenge, we propose a Federated Learning based Cooperative Hierarchical Caching scheme (FLCH), which keeps data locally and employs IoT devices to train a shared learning model for content popularity prediction. FLCH exploits horizontal cooperation between neighbour F-APs and vertical cooperation between the BaseBand Unit (BBU) pool and F-APs to cache contents with different degrees of popularity. Moreover, FLCH integrates a differential privacy mechanism to achieve a strict privacy guarantee. Experimental results demonstrate that FLCH outperforms five important baseline schemes in terms of the cache hit ratio, while preserving data privacy. Moreover, the results show the effectiveness of the proposed cooperative hierarchical caching mechanism for FLCH.

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“…Normally, the post-process of IoT data mainly focuses on two tasks: Classifications and regressions [20]. By exploiting the parallelism and low power consumption of FPGAs, MLoF offers a superior solution for these workloads.…”

Section: Machine Learning Algorithms Implementation On Low-cost Fpgasmentioning

confidence: 99%

MLoF: Machine Learning Accelerators for the Low-Cost FPGA Platforms

Ruiqi¹,

Zheng

et al. 2021

Applied Sciences

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In Internet of Things (IoT) scenarios, it is challenging to deploy Machine Learning (ML) algorithms on low-cost Field Programmable Gate Arrays (FPGAs) in a real-time, cost-efficient, and high-performance way. This paper introduces Machine Learning on FPGA (MLoF), a series of ML IP cores implemented on the low-cost FPGA platforms, aiming at helping more IoT developers to achieve comprehensive performance in various tasks. With Verilog, we deploy and accelerate Artificial Neural Networks (ANNs), Decision Trees (DTs), K-Nearest Neighbors (k-NNs), and Support Vector Machines (SVMs) on 10 different FPGA development boards from seven producers. Additionally, we analyze and evaluate our design with six datasets, and compare the best-performing FPGAs with traditional SoC-based systems including NVIDIA Jetson Nano, Raspberry Pi 3B+, and STM32L476 Nucle. The results show that Lattice’s ICE40UP5 achieves the best overall performance with low power consumption, on which MLoF averagely reduces power by 891% and increases performance by 9 times. Moreover, its cost, power, Latency Production (CPLP) outperforms SoC-based systems by 25 times, which demonstrates the significance of MLoF in endpoint deployment of ML algorithms. Furthermore, we make all of the code open-source in order to promote future research.

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Machine learning methods for IoT and their Future Applications

Cited by 38 publications

References 28 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

Privacy-Preserving Federated Deep Learning for Cooperative Hierarchical Caching in Fog Computing

MLoF: Machine Learning Accelerators for the Low-Cost FPGA Platforms

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