A secure authenticated and key exchange scheme for fog computing

Chen, Chien‐Ming; Huang, Yanyu; Wang, Kevin K H; Kumari, Saru; Wu, Mu-En

doi:10.1080/17517575.2020.1712746

Cited by 118 publications

(88 citation statements)

References 51 publications

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“…Based on the above discussion, the comparison on security among [35], [27], [21], [17], [22] and the proposed protocol is described in Table 6. We can notice the protocol can provide more security services than others.…”

Section: Other Security Discussionmentioning

confidence: 99%

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Quantum-Resistant Lightweight Authentication and Key Agreement Protocol for Fog-Based Microgrids

2021

IEEE Access

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A microgrid is a flexible and localized grid that can disconnect from the main grid and operate independently on the traditional and/or renewable resource. It also can work with the main grid, and even provide power to the main grid when it has excess power to help relieve the pressure of energy. The flow of power between main grid and microgrid is monitored by smart meter, which kept the end user's privacy information and his/her energy usage in real time. Hence, ensuring the privacy and security of these information is a considerable challenge. In this paper, we investigate the relationship between microgrids and the main grid, and design a quantum-resistant lightweight authentication and key agreement (AKA) protocol based on fog computing. The proposed protocol provides the mutual authentication and generates a session key between a smart meter and a fog node. More importantly, it can resist against quantum computing attacks, as the security of our scheme relies on the hardness of the learning parity with noise (LPN) problem, an NP-complete problem. Finally, we evaluate the performance of our proposed protocol and prove that our protocol is more lightweight and efficient.

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Section: Other Security Discussionmentioning

confidence: 99%

“…In 2019, Ma et al [16] improved Jia et al's [15] scheme and proposed a new novel scheme to support Vehicular Ad hoc Network (VANET) by eliminating the need for bilinear pairings. Meanwhile, in 2020, Chen et al [17] pointed out the Jia et al's scheme is vulnerable to an ephemeral secret leakage attack.…”

Section: Related Workmentioning

confidence: 99%

Quantum-Resistant Lightweight Authentication and Key Agreement Protocol for Fog-Based Microgrids

2021

IEEE Access

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“…Step1: The traffic flow data collected by ground monitors are denoised by wavelet analysis in order to remove the interference signals. In the process of data monitoring, some encryption technologies [23,24,[29][30][31][32]…”

Section: B Application Of Qga-lvq Neural Network In Short-term Traffmentioning

confidence: 99%

Application of Quantum Genetic Optimization of LVQ Neural Network in Smart City Traffic Network Prediction

Zhang

Wang

et al. 2020

IEEE Access

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Accurate prediction of traffic flow in urban networks is of great significance for smart city management. A short-term traffic flow prediction algorithm of Quantum Genetic Algorithm-Learning Vector Quantization (QGA-LVQ) neural network is proposed to forecast the changes of traffic flow. Different from BP neural network, Learning Vector Quantization (LVQ) neural network is of simple structure, easy implementation and better clustering effect. Utilizing the global optimization ability of Quantum Genetic Algorithm (QGA), it is combined with LVQ neural network to overcome some shortcomings of LVQ neural network, including sensitive to initial weights and prone to local minima. In order to test the convergence ability and the timeliness of QGA-LVQ neural network in short-term traffic flow, some contrast experiments are performed. Experimental simulation results show that, QGA-LVQ neural network obtains excellent prediction results in prediction accuracy and convergence speed. Besides, compared with GA-BP neural network and wavelet neural network, QGA-LVQ neural network performs better in short-term traffic flow prediction. INDEX TERMS QGA, LVQ neural network, short-term traffic flow prediction, global optimization.

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“…At present, with the rapid development of computer technology [2][3][4][5][6], intelligent computing is one of the important methods of intelligent science, and it is also a cutting-edge subject of information technology. The intelligent computing technologies and new methods developed in recent years have been widely used in many disciplines, and have achieved fruitful results in military, financial engineering, nonlinear system optimization, knowledge engineering, and computer-aided medical diagnosis.…”

Section: Introductionmentioning

confidence: 99%

A Novel Pigeon-Inspired Optimization Based MPPT Technique for PV Systems

et al. 2020

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The conventional maximum power point tracking (MPPT) method fails in partially shaded conditions, because multiple peaks may appear on the power–voltage characteristic curve. The Pigeon-Inspired Optimization (PIO) algorithm is a new type of meta-heuristic algorithm. Aiming at this situation, this paper proposes a new type of algorithm that combines a new pigeon population algorithm named Parallel and Compact Pigeon-Inspired Optimization (PCPIO) with MPPT, which can solve the problem that MPPT cannot reach the near global maximum power point. This hybrid algorithm is fast, stable, and capable of globally optimizing the maximum power point tracking algorithm. Therefore, the purpose of this article is to study the performance of two optimization techniques. The two algorithms are Particle Swarm Algorithm (PSO) and improved pigeon algorithm. This paper first studies the mechanism of multi-peak output characteristics of photovoltaic arrays in complex environments, and then proposes a multi-peak MPPT algorithm based on a combination of an improved pigeon population algorithm and an incremental conductivity method. The improved pigeon algorithm is used to quickly locate near the maximum power point, and then the variable step size incremental method INC (incremental conductance) is used to accurately locate the maximum power point. A simulation was performed on Matlab/Simulink platform. The results prove that the method can achieve fast and accurate optimization under complex environmental conditions, effectively reduce power oscillations, enhance system stability, and achieve better control results.

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A secure authenticated and key exchange scheme for fog computing

Cited by 118 publications

References 51 publications

Quantum-Resistant Lightweight Authentication and Key Agreement Protocol for Fog-Based Microgrids

Quantum-Resistant Lightweight Authentication and Key Agreement Protocol for Fog-Based Microgrids

Application of Quantum Genetic Optimization of LVQ Neural Network in Smart City Traffic Network Prediction

A Novel Pigeon-Inspired Optimization Based MPPT Technique for PV Systems

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