Proposing Encryption Selection Model for IoT Devices Based on IoT Device Design

Saleh, Matasem; Jhanjhi, N. Z.; Abdullah, Azween; Saher, Raazia

doi:10.23919/icact51234.2021.9370721

Cited by 8 publications

(3 citation statements)

References 41 publications

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“…However, it also faces many challenges [66]. To address these issues and get satisfactory security assurance in M2M communication networks, various authentication and data integrity tactics have been suggested that mainly depend on the efficacy of the lightweight cryptography in the security of IoT implementation model [67].…”

Section: Lightweight Cryptographymentioning

confidence: 99%

Types of Lightweight Cryptographies in Current Developments for Resource Constrained Machine Type Communication Devices: Challenges and Opportunities

et al. 2022

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Machine-type communication devices have become a vital part of the autonomous industrial internet of things and industry 4.0. These autonomous resource-constrained devices share sensitive data, and are primarily acquired for automation and to operate consistently in remote environments under severe conditions. The requirements to secure the sensitive data shared between these devices consist of a resilient encryption technique with affordable operational costs. Consequently, devices, data, and networks are made secure by adopting a lightweight cryptosystem that should achieve robust security with sufficient computational and communication costs and counter modern security threats. This paper offers in-depth studies on different types and techniques of hardware and software-based lightweight cryptographies for machine-type communication devices in machine-to-machine communication networks.

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Section: Lightweight Cryptographymentioning

confidence: 99%

Types of Lightweight Cryptographies in Current Developments for Resource Constrained Machine Type Communication Devices: Challenges and Opportunities

et al. 2022

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“…In recent years, China's advanced manufacturing industry has developed rapidly, and advanced concepts and technologies such as Intelligent Manufacturing in 2025 and Industrial Internet of Things are gradually merging into the manufacturing industry. Equipment in the production environment of industrial Internet of Things undertakes a large number of production tasks every day [1][2][3][4][5][6][7][8]. If all links of incoming materials, processing, assembly, packaging and logistics are highly automated, the equipment on the production line needs accurate operation, quick response and close networking cooperation, where the functional assistance of machine vision technology plays a key role [9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Research on Industrial Production Defect Detection Method Based on Machine Vision Technology in Industrial Internet of Things

Jia¹,

Wang²

2022

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The realization of automatic operation of production by the industrial Internet of Things needs the functional assistance of machine vision technology. Different from the recognition and detection of some known features, it is difficult to realize defect detection in machine vision applications. Therefore, this article studies the industrial production defect detection method based on machine vision technology in industrial Internet of Things. Firstly, in the second chapter, the images of industrial products collected by machine vision system are preprocessed and thinned to obtain more ideal detection accuracy and measurement accuracy. The methods of image binarization, morphological processing, thinning and burr elimination are given in detail. In the third chapter, product defect detection model is constructed based on U-Net network, and residual structure, hole convolution module, strip pooling module and attention mechanism module are introduced to optimize the network model. Experimental results verify the effectiveness of the model for product defect detection.

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“…The Internet of Things (IoT) is an innovation embedded with software, sensors, actuators, electronics, and network connectivity through which data can be collected and exchanged over the Internet. Artificial neural network (ANN) [1][2][3][4], software-defined networks (SDN) [5][6][7], and internet of things (IoT) [8][9][10][11][12][13][14] technologies find useful in different applications from the smart healthcare sector [15][16][17][18][19][20] to the satellite [21]. The exponential utilization of the Internet of Things (IoT) is expanding and is of ongoing interest as it is broadly utilized in numerous applications and devices like remote sensors, clinical devices, delicate home sensors, and other related IoT devices as shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

IoT Technology Enabled Heuristic Model With Morlet Wavelet Neural Network for Numerical Treatment of Heterogeneous Mosquito Release Ecosystem

Sabir

Nisar²,

Raja

et al. 2021

IEEE Access

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The utmost advancements of artificial neural networks (ANNs), software-defined networks (SDNs) and internet of things (IoT) technologies find beneficial in different applications of the smart healthcare sector. Aiming at modern technology's use in the future development of healthcare, this paper presents an advanced heuristic based on Morlet wavelet neural network for solving the mosquito release ecosystem in a heterogeneous atmosphere. The mosquito release ecosystem is dependent of six classes, eggs density, larvae density, pupae density, mosquitoes searching for hosts density, resting mosquito's density and mosquitoes searching for ovipositional site density. An artificial neural network with the layer structure of Morlet wavelet (MWNN) kernel is presented using the global and local search optimization schemes of genetic algorithm (GA) and active-set algorithm (ASA), i.e., MWNN-GA-ASA. The accurateness, reliability and constancy of the proposed MWNN-GA-ASA is established through comparative examinations with Adams method based numerical results to solve the proposed nonlinear system with matching of order 10 -06 to 10 -09 . The accuracy and convergence of the proposed MWNN-GA-ASA is certified using the statistical operators based on root mean square error (RMSE), Theil's inequality coefficient (T.I.C) and mean absolute deviation (MAD) operators.INDEX TERMS Mosquito release ecosystem, IoT, SDN, artificial neural networks, heuristic algorithm, Adams's method.

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Proposing Encryption Selection Model for IoT Devices Based on IoT Device Design

Cited by 8 publications

References 41 publications

Types of Lightweight Cryptographies in Current Developments for Resource Constrained Machine Type Communication Devices: Challenges and Opportunities

Types of Lightweight Cryptographies in Current Developments for Resource Constrained Machine Type Communication Devices: Challenges and Opportunities

Research on Industrial Production Defect Detection Method Based on Machine Vision Technology in Industrial Internet of Things

IoT Technology Enabled Heuristic Model With Morlet Wavelet Neural Network for Numerical Treatment of Heterogeneous Mosquito Release Ecosystem

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