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An enhanced genetic algorithm for computation task offloading in MEC scenario

International Journal of Wireless and Mobile Computing 2023年第2期25卷 118-127页

作者： Zhao, Jiacheng Li, Wenzao Liu, Hantao Yu, Peizhen Li, Hanyun Wen, Zhan School of Communication Engineering Chengdu University of Information Technology Sichuan Chengdu China Chengdu University of Information Technology Sichuan Chengdu China Network and Data Security Key Lab of Sichuan Pro. University of Electronic Science and Technology of China Sichuan Chengdu China Educational Informationisation and Big Data Centre Education Department of Sichuan Province Sichuan Chengdu China

The explosive growth of Internet of Things (IoT) and 5G communication technologies has driven the increasing computing demands for wireless devices. Mobile edge computing in the 5G scenario is a promising solution for energy-efficient and low latency applications. However, due to limited bandwidth, the selection of appropriate computing tasks greatly affects the user experience and system performance. Under the wireless bandwidth constraint, the reasonable choice of offloading objects is an NP-hard problem. The genetic algorithm has a great ability to solve this problem, but the performance of the algorithm varies with different scenarios. This paper proposes a task offloading strategy based on an enhanced genetic algorithm for small-scale computing tasks with an ultra-dense terminal distribution. Numerical experiments show that the convergence speed and optimisation effect of the enhanced genetic algorithm are significantly improved compared to the conventional genetic algorithm. © 2023 Inderscience Enterprises Ltd.

关键词： Genetic algorithms

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An Ensemble of CapsNet and KSVM Using Feature Fusion: Application to COVID-19 Detection 3

An Ensemble of CapsNet and KSVM Using Feature Fusion: Applic...

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3rd International Conference on Pattern Recognition and Machine Learning, PRML 2022

作者： Aggrey, Esther Stacy E. B. Zhen, Qin Kodjiku, Seth Larweh Aidoo, Evans Fiasam, Linda Delali Mensah, Acheampong Edward University of Electronic Science & Technology of China School of Information & Software Engineering Chengdu China University of Electronic Science & Technology of China School of Information & Software Engineering Network and Data Security Key Lab of Sichuan Province Chengdu China Zhejiang Gongshang University School of Computer & Information Engineering Hangzhou China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

COVID-19 virus is a major worldwide pandemic that is growing at a fast pace throughout the world. The usual approach for diagnosing COVID-19 is the use of a real-time polymerase chain reaction (RT-PCR) based nucleic acid test. However, RT-PCR has lower sensitivity in the early phases of COVID-19 detection. Recent studies have indicated that X-ray images may be useful throughout the early detection of the virus. Human screening has been shown to be cost-effective, susceptible to mistakes, and time-demanding, which has sparked an interest in using Convolutional Neural networks (CNNs) to automate the process. CNNs, on the other hand, fail to view the exact placement of features as advantageous in medical imaging. Furthermore, for successful training and prediction, CNNs need a huge quantity of datasets. CNNs are rapidly reducing picture resolution, resulting in worsening accuracy in classification. We used newly created capsule networks (CapsNets) in our study to circumvent these disadvantages. The primary contribution is to improve the identification of SARS-CoV-2 with images obtained from X-ray by coupling capsule network with a kernel support vector machine (KSVM). The technique was evaluated using a publicly availab.e dataset, and the proposed model shows that the accuracy of the CapsNet-KSVM based model is improved by 94.6% accuracy, 95% sensitivity, and 98% specificity, which outperforms the traditional CNN and other existing ensemble models. The proposed CapsNet-KSVM based system can be employed to identify the presence of COVID-19 in the human body using X-ray images. © 2022 IEEE.

关键词： COVID-19

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network traffic detection based on part matching and section evolution of immune elements

Network traffic detection based on part matching and section...

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2020 International Conference on data Processing Algorithms and Models, ICDPAM 2020

作者： Liu, Caiming Zhang, Yan Xie, Chunming Wang, Deming Li, Zhonghua School of Artificial Intelligence Leshan Normal University Leshan614000 China Intelligent Network Security Detection and Evaluation Lab. Leshan Normal University Leshan61400 China Internet Natural Language Intelligent Processing Key Lab. of Education Department of Sichuan Province Leshan Normal University Leshan61400 China Scientific Research Department Leshan Normal University Leshan61400 China

Immune algorithms can improve the self-adaptability for network traffic detection. Classical immune algorithms dynamically evolve and match the whole content of immune elements. However, most features of network traffic are isomeric to each other. The general matching and evolution for the whole immune elements slow down the process of self-adaptability to an extent and reduce the accuracy of immune elements' evolution. In this paper, availab.e network data features are analysed for the network traffic detection. The section simulation method in the immune system is studied by creating a math method for part matching of immune elements which can evolve in parts. The process of section evolution for immune elements is inferred. A detection method for recognizing anomaly network traffic is proposed. Simulation experiments show that the self-adaptability of the proposed method to the real network environment is superior to that of traditional immune algorithms. © Published under licence by IOP Publishing Ltd.

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LDPC Code based Pseudonym Scheme for Vehicular networks

LDPC Code based Pseudonym Scheme for Vehicular Networks

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Space-Air-Ground Computing (SAGC), International Conference on

作者： Jin Zhou Changsong Zheng Yuedi Li Hua Xu Zhiguang Qin Dajiang Chen Network and Data Security Key Lab of Sichuan Province UESTC Chengdu China Public Security Bureau of Sichuan Chengdu China School of Physics and Electronic Engineering Yancheng Teachers University Yancheng China

ISBN: (纸本)9781665430104

In this paper, an innovative pseudonym scheme based on Direct Anonymous Attestation (DAA) and Low-Density Parity-Check (LDPC) is proposed. For the sake of improving the efficiency of generating pseudonyms, the proposed scheme uses the LDPC code to encode the credential to produce more availab.e versions. To detect whether a vehicle's On-Board Unit (OBU) is compromised, the proposed scheme grants the Revocation Authority (RA) to check if the compromised OBU uses a revoked pseudonym. Compared with the existing pseudonym scheme, the proposed scheme has a higher level of security and lower computation overhead.

关键词： Parity check codes security

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network traffic detection based on part matching and section evolution of immune elements

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Journal of Physics: Conference Series 2021年第1期1774卷

作者： Caiming Liu Yan Zhang Chunming Xie Deming Wang Zhonghua Li School of Artificial Intelligence Leshan Normal University Leshan 614000 China Intelligent Network Security Detection and Evaluation Lab. Leshan Normal University Leshan 61400 China Internet Natural Language Intelligent Processing Key Lab. of Education Department of Sichuan Province Leshan Normal University Leshan 61400 China Scientific Research Department Leshan Normal University Leshan 61400 China

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Ciphertext-policy weighted attribute-based encryption scheme in cloud computing

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sichuan Daxue Xuebao (Gongcheng Kexue Ban)/Journal of sichuan University (Engineering Science Edition) 2013年第6期45卷 21-26页

作者： Liu, Xi-Meng Ma, Jian-Feng Xiong, Jin-Bo Li, Qi Zhang, Tao Zhu, Hui School of Telecommunications Eng. Xidian Univ. Xi'an 710071 China School of Computer Sci. and Technol. Xidian Univ. Xi'an 710071 China Network and Data Security Key Lab. of Sichuan Province Chengdu 611731 China

By analyzing existing ciphertext-policy attribute-based encryption scheme, ciphertext-policy weighted attribute-based encryption scheme was proposed. To solve the existing scheme without considering the attribute with weight, the concept of weight was adopted into ciphertext-policy attribute-based encryption scheme. Meanwhile, the security of ciphertext-policy weighted attribute-based encryption scheme was proved under the selective-weighted attribute model. Moreover, the ciphertext-policy weighted attribute-based encryption scheme was given which has effective application in secure cloud computing environment. Analysis showed that the present scheme is more appropriate for the cloud computing environment than the existing schemes.

关键词： Public key cryptography

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Efficient mobile IP registration in certificateless public key infrastructure

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sichuan Daxue Xuebao (Gongcheng Kexue Ban)/Journal of sichuan University (Engineering Science Edition) 2012年 44卷 135-139页

作者： Ma, Hua Liu, Xue Liu, Zhen-Hua Dong, Bo Dept. of Mathematics Xidian Univ. Xi'an 710071 China Network and Data Security Key Lab. of Sichuan Province Univ. of Electronic Sci. and Technol. of China Chengdu 611731 China

Based on non-certificate public key cryptography, a new mobile IP(MIP) registration protocol was proposed to minimize the registration time and enhance registration performance. The protocol could realize the authentication between HA and FA by using a short non-certificate signature scheme without bilinear pairings. Protocol parameters between HA and MN could be kept synchronised by reusing the initial values in the MIP registration. User anonymity could be achieved via a temporary identity transmitted by a mobile user, instead of its true identity. Random values were added to the registration messages of the mobile entity to prevent a possible replay attack. Compared with the exist protocols, the protocol could minimize the amount of calculation of HA and FA and shorten the length of the message. Moreover, it could guarantee the mutual authentications of each node, the secure distribution of secret keys and the integrity of the message.

关键词： Public key cryptography

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