A survey on application of machine learning for Internet of Things

Cui, Laizhong; Yang, Shu; Chen, Fei; Ming, Zhong; Lu, Nan; Qin, Jing

doi:10.1007/s13042-018-0834-5

Cited by 219 publications

(113 citation statements)

References 65 publications

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“…Vehicle detection using colour features, vehicle tracking using Kalman filter and vehicle counting with the help of Raspberry Pi 3, open CV and C++ is explained in [3]. Survey of machine learning-based different IoT application in traffic engineering, security with challenges and open issues is explained in [5,9]. Face and fatigue detection using Haar descriptors with the help of Raspberry Pi board, USB camera, Open CV libraries, and Python is explained in [10].…”

Section: Literature Surveymentioning

confidence: 99%

Canny Edge Detection Based Real-Time Intelligent Parking Management System

Trivedi¹,

Devi²,

Dhara³

2020

SJSUT.ST

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Real-time traffic monitoring and parking are very important aspects for a better social and economic system. Python-based Intelligent Parking Management System (IPMS) module using a USB camera and a canny edge detection method was developed. The current situation of real-time parking slot was simultaneously checked, both online and via a mobile application, with a message of Parking "Available" or "Not available" for 10 parking slots. In addition, at the time entering in parking module, gate open and at the time of exit parking module, the gate closes automatically using servomotor and sensors. Results are displayed in figures with the proposed method flow chart.

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Section: Literature Surveymentioning

confidence: 99%

Canny Edge Detection Based Real-Time Intelligent Parking Management System

Trivedi¹,

Devi²,

Dhara³

2020

SJSUT.ST

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“…Cui et al [11] present an overview of the industrial applications of machine learning in the domain of IoT, in particular, the use of machine learning to improve the efficiency of smart meter operation.…”

Section: Related Workmentioning

confidence: 99%

Deployment framework for the Internet of water meters using computer vision on ARM platform

Santiago

Álvares

2020

AIS

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This article presents the conception of a new method developed mainly in Python to automate the reading process of water meters with an analog display using computer vision and machine learning. A camera captures the consumption value in the water meter, and the yielded image undergoes image processing until the digits are detected and isolated. Then the digits are passed into an SVM machine-learning model that carries out a high accuracy OCR. The software is executed over an ARM platform running Linux. The data resultant from the automated metering, such as the device identification number, event date and time in UTC, consumption value, volume and time variations, flow, and display image, are locally stored and transmitted to a cloud server through VPN in a Wi-Fi and cellular network connection, or by SMS, enabling a remote supervision. Thereby, the automatic metering method features a new way to perform predictive analysis and management of water and meters proactively and can be replicated for digital-display water meters, as well as extended to handle automatic metering on electricity and gas meters as well.

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“…There are several machine learning algorithms investigated in related work [27]. In this work, we utilize machine learning to select the appropriate encryption technique based on the execution context.…”

Section: Full or Partial Encryption Decision X I Dmentioning

confidence: 99%

Smart offloading technique for CP-ABE encryption schemes in constrained devices

2020

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Nowadays, we are attending to the wide proliferation of IoT devices in various computing environments. Actually, this trend is the natural way to extend the M2M communication. However, these devices are facing serious resource constraints. To mitigate this problem, the huge amount of the exchanged data is collected from these devices for being stored and processed by the Cloud. Furthermore, since the Cloud is honest but curious, it may reveal personal information about users' habits owning these devices and can even lead to user profiling. Consequently, security mechanisms should be deployed at different levels to preserve data privacy so that only authorized users can gain access to the smallest piece of data according to the collection purpose. By assuming that the Cloud-is-honest-but-curious, we can not provide full or similar access to different untrusted Cloud services. Therefore, we should define the relevant privacy level implementing the data access control for each Cloud service according to some criteria. In this context, CP-ABE is a promising solution addressing this problem. This novel attribute-based public key encryption system provides a flexible fine-grained access control to data for any data requestor. However, performing all the related cryptographic operations on such devices is practically infeasible because of the resource constraints. For alleviating all the computation burden on these resource-limited devices, several schemes have been proposed. In this work, we propose a smart offloading technique that switches dynamically from full encryption to partial encryption according to a wise decision strategy considering the available resources and some crucial parameters like the number of attributes and the size of the data being encrypted. The relevant decision is based on a machine learning algorithm. To the best of our knowledge, this is the first paper proposing an adaptive CP-ABE scheme for constrained device optimizing the overall available resources.

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A survey on application of machine learning for Internet of Things

Cited by 219 publications

References 65 publications

Canny Edge Detection Based Real-Time Intelligent Parking Management System

Canny Edge Detection Based Real-Time Intelligent Parking Management System

Deployment framework for the Internet of water meters using computer vision on ARM platform

Smart offloading technique for CP-ABE encryption schemes in constrained devices

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