Trustworthiness in Industrial IoT Systems Based on Artificial Intelligence

Lv, Zhihan; Han, Yang; Singh, Amit Kumar; Manogaran, Gunasekaran; Lv, Haibin

doi:10.1109/tii.2020.2994747

Cited by 250 publications

(142 citation statements)

References 29 publications

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“…Adaboost algorithm is one of the Boosting algorithms of artificial intelligence [16]. The basic principle of the algorithm is to rearrange the data.…”

mentioning

confidence: 99%

“…After N cycles, a new strong practitioner is formed by combining several weak practitioners and their corresponding weights. The emphasis of the Adaboost algorithm is to analyze the weight of the results of the first exercise, rearrange the samples, train the new samples to get many weak exercisers, and combine them into a strong exerciser to reduce the system error and improve the accuracy of the system [16][18] Wu Zhengjiang [19] and others combined Adaboost algorithm with BP neural network algorithm to solve the problem of easy paralysis and slow convergence speed of traditional BP algorithm under large-scale data, which accelerated the speed of system operation and increased the accuracy of the system. Rosa [20] and others proposed to introduce AdaBoost algorithm into the recognition of human walking posture.…”

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confidence: 99%

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Retracted: Analysis and Research of Artificial Intelligence Algorithms in GPS Data

Luo²,

Pan

et al. 2024

IEEE Access

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With the development of GPS technology, it has been gradually applied in engineering, science and technology and daily life. The requirement of positioning accuracy in these fields is also increasing. Therefore, this paper studies GPS coordinate conversion and positioning technology from the aspect of artificial intelligence algorithm to improve positioning accuracy. Aiming at the problem of coordinate transformation, a method of GPS coordinate transformation based on convolution neural network model is proposed. Firstly, the input GPS raw data is converted into unstructured images, then the model is used to learn the features of the data, and finally the converted coordinate data is output. Aiming at the problem that the traditional vehicle GPS navigation system is easy to be interfered by the external environment and is not conducive to the system tracking and positioning, AdaBoost algorithm is introduced into the navigation system to better solve the problem. GPS lock. The strong classifier can be obtained by iterative practice, and the filter value can be measured accurately during GPS interference. At the same time, the combination of Adaboost method and BP algorithm can help the filter process information and avoid information loss caused by GPS interference. In this way, not only the integrity of information can be guaranteed, but also the stability and accuracy of the system can be guaranteed. The results show that the coordinate transformation method based on convolution neural network has better conversion accuracy than the traditional method, and the navigation method based on Adaboost algorithm can improve the navigation accuracy and optimize the system performance.

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“…Adaboost algorithm is one of the Boosting algorithms of artificial intelligence [16]. The basic principle of the algorithm is to rearrange the data.…”

mentioning

confidence: 99%

mentioning

confidence: 99%

Retracted: Analysis and Research of Artificial Intelligence Algorithms in GPS Data

Luo²,

Pan

et al. 2024

IEEE Access

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“…Reliability is considered as one of the important parameters for designing the solutions for networking and Cyber Physical Systems 29 . The authors in Reference 30 introduce a hybrid partitioning approach for the dissemination of the emergency message to avoid the collision of packets and the issue of the hidden node problem.…”

Section: Related Workmentioning

confidence: 99%

A performance‐aware routing mechanism for flying ad hoc networks

Ali

Islam

Song

et al. 2020

Trans Emerging Tel Tech

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Flying ad hoc networks (FANETs) are visioned to unlock the potential of the Internet of Drones in various application scenarios like surveillance, agriculture, health care, disaster management, and rescue operations. As the unmanned aerial vehicles possess fast movements and frequent topological variations, selection of the next hop, self‐adaptation, and avoidance of dissemination loops have proven to be the challenging issues that have a considerable impact on the performance of a FANET. We propose a performance‐aware routing mechanism, G‐OLSR, for efficient communication and collaboration among the unmanned aerial vehicles in a FANET environment. The self‐adaptation of the network in case of any topological changes is considered in the proposed mechanism. Moreover, the proposed mechanism avoids the dissemination loops and it, consequently, improves the performance of the network. The simulation results show that the proposed mechanism performs better in terms of delay, message overhead, packet delivery ratio, and throughput than Optimized Link State Routing.

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“…The Internet of things is to exchange and communicate information between the network of a commodity and the Internet [25], and realize intelligent identification, positioning, tracking, monitoring and management through various information sensing devices such as radio frequency identification, infrared sensor, global positioning system, laser scanner according to the protocol. The system is based on the Internet of things RFID identification [26] and passive sensing technology [27], providing a unique identity tag for each emergency.…”

Section: A Information Collection Link Of Internet Of Thingsmentioning

confidence: 99%

Intelligent Evaluation System of Government Emergency Management Based on BP Neural Network

Jia

2020

IEEE Access

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With the deepening of the global economic community, various emergencies emerge in endlessly, and the risks gradually increase. People's lives and property are threatened, which also causes a great burden on the social economy. Hitherto unknown novel coronavirus events occurred in China after the outbreak of the new coronavirus in 2019. The emergency management system is not perfect, so we start to study and improve the deficiencies of the emergency management system, but it is still difficult to effectively prevent and deal with all kinds of sudden and frequent social problems. Therefore, this paper puts forward the research of intelligent evaluation system of government emergency management based on BP neural network. In this paper, an intelligent evaluation system of government emergency management based on Internet of things environment is established, and then the system is deepened by BP neural network algorithm to avoid the interference of human factors. An objective intelligent evaluation system of government emergency management is constructed and verified by an example. We applied the system in a province, and proved that the system has strong executive ability, outstanding big data computing ability, and can objectively evaluate and analyze the government emergency management. The operability and accuracy of the intelligent evaluation system are verified. The effective evaluation content provides a new idea and method for government emergency management. And then continuously improve the emergency management measures to achieve the effect of dealing with things smoothly without panic.

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Trustworthiness in Industrial IoT Systems Based on Artificial Intelligence

Cited by 250 publications

References 29 publications

Retracted: Analysis and Research of Artificial Intelligence Algorithms in GPS Data

Retracted: Analysis and Research of Artificial Intelligence Algorithms in GPS Data

A performance‐aware routing mechanism for flying ad hoc networks

Intelligent Evaluation System of Government Emergency Management Based on BP Neural Network

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