A Blockchain-Based Shamir’s Threshold Cryptography Scheme for Data Protection in Industrial Internet of Things Settings

Yu, Keping; Tan, Liang; Yang, Congling; Choo, Kim‐Kwang Raymond; Bashir, Ali Kashif; Rodrigues, Joel J. P. C.; Sato, Takuro

doi:10.1109/jiot.2021.3125190

Cited by 92 publications

(29 citation statements)

References 37 publications

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“…is solution point involves adopting the edge or fog computing architecture in conjunction with the IoT framework. e work of Yu et al [4] has stated this possibility numerous times with considerable arguments and evidence of this notion. It is one of the most notable results in this approach.…”

Section: Parallel Research Outcomes For Detecting Cyber Attacks Using...mentioning

confidence: 97%

Enhanced Secure Technique for Detecting Cyber Attacks Using Artificial Intelligence and Optimal IoT

Kumar¹,

Rahmath

Raju

et al. 2022

Security and Communication Networks

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The Internet of Things (IoT) is a broad term that refers to the collection of information about all of the items that are linked to the Internet. It supervises and controls the functions from a distance, without the need for human interaction. It has the ability to react to the environment either immediately or via its previous experiences. In a similar vein, robots may learn from their experiences in the environment that is relevant to their applications and respond appropriately without the need for human interaction. A greater number of sensors are being distributed across the environment in order to collect and evaluate the essential information. They are gaining ground in a variety of industries, ranging from the industrial environment to the smart home. Sensors are assisting in the monitoring and collection of data from all of the real-time devices that are reliant on all of the different types of fundamental necessities to the most advanced settings available. This research study was primarily concerned with increasing the efficiency of the sensing and network layers of the Internet of Things to increase cyber security. Due to the fact that sensors are resource-constrained devices, it is vital to provide a method for reacting, analysing, and transmitting data collected from the sensors to the base station as efficient as possible. Resource requirements, such as energy, computational power, and storage, vary depending on the kind of sensing devices and communication technologies that are utilised to link real-world objects together. Sensor networks' physical and media access control layers, as well as their applications in diverse geographical and temporal domains, are distinct from one another. Transmission coverage range, energy consumption, and communication technologies differ depending on the application requirements, ranging from low constraints to high resource enrich gadgets. This has a direct impact on the performance of the massive Internet of Things environment, as well as the overall network lifetime of the environment. Identifying and communicating matching items in a massively dispersed Internet of Things environment is critical in terms of spatial identification and communication.

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Section: Parallel Research Outcomes For Detecting Cyber Attacks Using...mentioning

confidence: 97%

Enhanced Secure Technique for Detecting Cyber Attacks Using Artificial Intelligence and Optimal IoT

Kumar¹,

Rahmath

Raju

et al. 2022

Security and Communication Networks

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show abstract

“…The light varies greatly in the enclosure environment. To further test the robustness of HE-Yolo to the luminance of light, the RGB image is converted into an HSV image [ 30 ], and the luminance coefficient α of 10 gradients is set and α ∈ {0.5, 0.6, 0.7 … 1.5}. 10 luminance coefficients are multiplied by the luminance V , respectively, and then converted into RGB images to obtain the testing set with 10 different luminances.…”

Section: Results and Analysismentioning

confidence: 99%

“…e light varies greatly in the enclosure environment. To further test the robustness of HE-Yolo to the luminance of light, the RGB image is converted into an HSV image [30], and the luminance coefficient α of 10 gradients is set and α ∈ 0.5, 0.6, 0.7 . .…”

Section: Analyzing the Influence Of Lighting Condition On The Recogni...mentioning

confidence: 99%

Efficient Detection Method of Pig-Posture Behavior Based on Multiple Attention Mechanism

Huang

Wang

et al. 2022

Computational Intelligence and Neuroscience

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Due to the low detection precision and poor robustness, the traditional pig-posture and behavior detection method is difficult to apply in the complex pig captivity environment. In this regard, we designed the HE-Yolo (High-effect Yolo) model, which improves the Darknet-53 feature extraction network and integrates DAM (Dual attention mechanism) of channel attention mechanism and space attention mechanism, to recognize the posture behaviors of the enclosure pigs in real-time. First, the pig data set is clustered and optimized by the K-means algorithm to obtain a new anchor frame size. Second, the DSC (Depthwise separable convolution) and h-switch activation function are innovatively introduced into the Darknet-53 feature extraction network, and the C-Res (Contrary residual structure) unit is designed to build Darknet-A feature extraction network, so as to avoid network gradient explosion and ensure the integrity of feature information. Subsequently, DAM integrating the spatial attention mechanism and the channel attention mechanism is established, and it is further combined with the Incep-abate module to form DAB (Dual attention block), and HE-Yolo is finally built by Darknet-A and DAB. A total of 2912 images of 46 enclosure pigs are divided into the training set, the verification set, and the test set according to the ratio of 14 : 3:3, and the recognition performance of HE-Yolo is verified according to the parameters of the precision P, the recall R, the AP (i.e., the area of P-R curve) and the MAP (i.e., the average value of AP). The experiment results show that the AP values of HE-Yolo reach 99.25%, 98.41%, 94.43%, and 97.63%, respectively, in the recognition of four pig-posture behaviors of standing, sitting, prone and sidling of the test set. Compared with other models such as Yolo v3, SSD, and faster R–CNN, the mAP value of HE-Yolo is increased by 5.61%, 4.65%, and 0.57%, respectively, and the single-frame recognition time of HE-Yolo is only 0.045 s. In the recognition of images with foreign body occlusion and pig adhesion, the mAP values of HE-Yolo are increased by 4.04%, 4.94%, and 1.76%, respectively, while compared with other models. Under different lighting conditions, the mAP value of HE-Yolo is also higher than that of other models. The experimental results show that HE-Yolo can recognize the pig-posture behaviors with high precision, and it shows good generalization ability and luminance robustness, which provides technical support for the recognition of pig-posture behaviors and real-time monitoring of physiological health of the enclosure pigs.

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“…At present, food shortage is one of the most important problems faced by many regions in the world ( 1 ). This phenomenon is generally reflected in two aspects ( 2 ). For one thing, there is still room for improvement in current agricultural technology, which makes grain yield fail to meet the expectations ( 3 ).…”

Section: Introductionmentioning

confidence: 99%

Deep Data Analysis-Based Agricultural Products Management for Smart Public Healthcare

Yan

Zhang

et al. 2022

Front. Public Health

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Agricultural is an indispensably public healthcare industry for human beings at any time and smart management of it is of great significance. Since substantial technical advance relies on long-term efforts and continuous progress, reasonably scheduling the distribution of agricultural products acts as a key aspect of smart public healthcare. The most intuitive factor affecting the distribution of agricultural products is its dynamic price. Forecasting price fluctuations in advance can optimize the distribution of agricultural products and pave the way to smart public healthcare. Most researchers study the prices of various agricultural products separately, without considering the interaction of different agricultural products in the time dimension. This study introduces a typical deep learning model named graph neural network (GNN) for this purpose and proposes deep data analysis-based agricultural products management for smart public healthcare (named GNN-APM for short). The highlight of GNN-APM is to take latent correlations among multiple types of agricultural products into consideration when modeling evolving rules of price sequences. A case study is set up with the use of real-world data of the agricultural products market. Simulative results reveal that the designed GNN-APM functions well.

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A Blockchain-Based Shamir’s Threshold Cryptography Scheme for Data Protection in Industrial Internet of Things Settings

Cited by 92 publications

References 37 publications

Enhanced Secure Technique for Detecting Cyber Attacks Using Artificial Intelligence and Optimal IoT

Enhanced Secure Technique for Detecting Cyber Attacks Using Artificial Intelligence and Optimal IoT

Efficient Detection Method of Pig-Posture Behavior Based on Multiple Attention Mechanism

Deep Data Analysis-Based Agricultural Products Management for Smart Public Healthcare

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