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Spectrum Allocation Method Based on Modified Social Spider Optimization Algorithm

Spectrum Allocation Method Based on Modified Social Spider O...

引用

computing, Communication, Perception and Quantum technology (CCPQT), international conference on

作者： Jiabei Han Hongyuan Gao Yaqing Zheng Altam Mohammed Xiaoyuan Gu College of Information and Communication Engineering Harbin Engineering University Harbin China

ISBN: (数字)9798331528386

ISBN: (纸本)9798331528393

In this paper, modified social spider optimization algorithm (MSSO) has been proposed to solve the spectrum allocation problem in Macrocell/Femtocell heterogeneous network. As an integer programming problem, existing intelligence algorithms show some disadvantages in the convergence rate and accuracy in such spectrum allocation task in heterogeneous network. Besides we consider the user demand satisfaction rate when modeling the system throughput. From the simulation results, MSSO has excellent optimization performance for high-dimensional integer programming problems.

关键词： Integer programming Accuracy Quantum computing Simulation Quantum mechanics Throughput Heterogeneous networks Resource management Optimization convergence

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A Trust Evaluation Based Attribute-Based Access Control Model for Smart Home 9

A Trust Evaluation Based Attribute-Based Access Control Mode...

引用

9th international conference on Big Data computing and Communications, BigCom 2023

作者： Tao, Yufan Zeng, Fanping University of Science and Technology of China Key Laboratory of Wireless-Optical Communications Chinese Academy of Sciences School of Computer Science and Technology Hefei China

ISBN: (纸本)9798350331240

In modern society, smart home has become a part of people's daily life. The smart home will generate a large amount of data to be shared inside and outside the home which is sensitive and private. Therefore, access control plays a critical role in the many issues that need to be addressed in the smart home. However, there is still a lack of an access control scheme suitable for the smart home to ensure the security of users' data. Attribute-based access control(ABAC) is a flexible authorization model. It controls whether there is permission to operate objects through the attributes of entities, operation types, and related environments, but it cannot resist malicious attacks within the environment. Therefore, in this paper, we propose a trust-based ABAC model, which is extended on the basis of the ABAC model and applies the trust model to the smart home ABAC model to achieve fine-grained and effective access control. Our model can not only use the trust model to monitor the behavior deviation of devices in smart homes, and establish trust relationships between devices, but also control the permissions of each device. Finally, we test the trust model on the ns-3 platform and simulate the access control model on the small real Internet of Things(IoT). The results show that the trust model has good accuracy, convergence, and anti-attack, and the ABAC model has good applicability in smart home scenarios. © 2023 IEEE.

关键词： Automation

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Image Segmentation Algorithm in Complex Environment Based on Improved SOLOV2 12

Image Segmentation Algorithm in Complex Environment Based on...

引用

12th international conference of Information and Communication technology, ICTech 2023

作者： Zhou, RenJian Zheng, LiaoMo Ren, ChuLan Wang, ShiYu Shenyang University of Chemical Technology Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Shenyang Casnc Technology Co. Ltd. Shenyang China

ISBN: (纸本)9798350332919

Previous industrial robots combined with vision systems in target grasping tasks have high requirements for the task environment. Once the vision system is confronted with a target object in a complex environment such as stacking, tilting and occlusion, it will be difficult to provide the robot control system with the correct target pose information due to the lack of target feature information, which is not conducive to the dynamic configuration capability of the production system. This study proposes an industrial target image segmentation algorithm based on improved SOLOv2 to solve the problem of low accuracy of industrial target image segmentation caused by small targets as well as occlusion. Building upon the SOLOv2 model, the optimization of fusing multi-layer features within the mask feature branch is deaigned to integrate feature information from the deepest to the shallowest layers, in a layer-by-layer manner;the Mish activation function is selected in the upsampling process to enhance the model's ability to extract small target features in images while improving the generalization ability and convergence speed of the model;to mitigate the impact of occlusion on image segmentation, the weights of the loss function are adaptively adjusted based on the degree of occlusion. This allows for a reduction in the negative influence of occlusion on image segmentation, as the loss function is optimezed to account for varying degrees of occlusion. © 2023 IEEE.

关键词： Image segmentation

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A High-precision Direction Finding Method for Aerial Search and Rescue Targets Based on Optimized Firefly Particle Filtering

A High-precision Direction Finding Method for Aerial Search ...

引用

2023 IEEE international conference on Signal Processing, Communications and computing, ICSPCC 2023

作者： Bai, Yankang Song, Yulong Zhao, Yuan Liu, Yangyang Dan, Zesheng Tang, Chengkai School of Electronics and Information Northwestern Polytechnical University Xi'an China Guangzhou Jingwei Tianteng Microelectronics Technology Co Ltd Xi'an China School of Information Engineering Xi'An Mingde Institute of Technology Xi'an China

ISBN: (纸本)9798350316728

Aviation search and rescue, as an important requirement for the development of military information construction, is of great significance for our military to carry out joint search and rescue to ensure the safety of personnel during wartime missions. Accurately and quickly obtaining the direction and location information of the target to be searched and rescued is the most crucial step in aviation search and rescue. However, in real-world search and rescue environments, airborne direction finding data often results in significant errors due to the obstruction of antennas by equipment such as landing gear and tires on the aircraft. This article proposes an optimized firefly particle filter algorithm that combines outlier detection ideas and obtains high-precision target direction and position information by integrating airborne GPS, airborne IMU, radio ranging data, and dual antenna direction finding data. The simulation results show that in complex environments, this algorithm outperforms other algorithms in terms of accuracy, convergence time, and applicability. © 2023 IEEE.

关键词： Optimization

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Joint Task Offloading and Resource Allocation for Integrated V2V and V2I Communication

Joint Task Offloading and Resource Allocation for Integrated...

引用

international conference on Information and Communication technology convergence (ICTC)

作者： Pyeongjun Choi Jeonghwan Kim Jeongho Kwak DGIST Daegu South Korea

ISBN: (数字)9798350364637

ISBN: (纸本)9798350364644

As autonomous driving technology becomes more sophisticated, VEC is in the limelight. However, it faces limitations in network conditions or the existence of RSU. In this paper, we propose a Lyapunov optimization-based algorithm that jointly optimizes both offloading policy and computing resources to minimize energy consumption while maintaining finite service time with the help of V2V and V2I communication.

关键词： Energy consumption Vehicle-to-infrastructure Vehicular ad hoc networks Information and communication technology Resource management Optimization Faces Autonomous vehicles convergence

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Towards Federated Learning on Fresh Datasets 20

Towards Federated Learning on Fresh Datasets

引用

20th IEEE international conference on Mobile Ad Hoc and Smart Systems, MASS 2023

作者： Wu, Chen Xiao, Mingjun Wu, Jie Xu, Yin Zhou, Jinrui Sun, He University of Science and Technology of China School of Data Science China University of Science and Technology of China School of Computer Science and Technology & Suzhou Institute for Advanced Research China Temple University Department of Computer and Information Sciences United States

ISBN: (纸本)9798350324334

Federated Learning (FL) is an emerging privacy-preserving distributed computing paradigm that enables numerous clients to collaboratively train machine learning models without the need for transmitting the private datasets of clients to the FL server. Unlike most existing research where the local datasets of clients are considered to be unchanging over time during the whole FL process, we consider such scenarios in this paper where the local datasets of clients need to be updated periodically, and the server can stimulate clients to use as fresh as possible datasets to train their local models. Our objective is to determine a client selection strategy to minimize the loss of global model for FL with a limited budget. To this end, we leverage the concept of Age of Information (AoI) to quantify the freshness of local datasets and theoretically analyze the convergence bound of our AoI-aware FL system. Based on the convergence bound, we formalize our problem as a restless multi-armed bandit problem. Then, we devise a Whittle's-Index-based Client Selection algorithm, called WICS, to tackle the client selection problem. Extensive simulations show that the proposed algorithm can reduce the training loss and improve learning accuracy compared to state-of-the-art algorithms. © 2023 IEEE.

关键词： Budget control

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Artificial Bee Colony Method with Guide-Based Adaptability for Traversing Areas of Search 6

Artificial Bee Colony Method with Guide-Based Adaptability f...

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6th international conference on Contemporary computing and Informatics, IC3I 2023

作者： Kolandaisamy, Raenu Rastogi, Ajay Adhana, Yatin Yuvaraj, K. Kumar, K. Sriram Birare, Komal Madhukar UCSI University Kuala Lumpur Malaysia Teerthankar Mahaveer University CCSIT Uttar Pradesh Moradabad India School of Basic Scieences & Technology IIMT University Uttar Pradesh Meerut India Karpagam Academy of Higher Education Department of Computer Science Tamilnadu Coimbatore India Karpagam Institute of Technology Department of Information Technology Tamilnadu Coimbatore India Vishwakarma Institute of Information Technology IT Maharashtra Pune India

ISBN: (纸本)9798350304480

One of the well-known strategies for swarm intelligence that is used to resolve a variety of quantitative issues and practical improvement issues is the fake colony of bee's program. Even so, ABC occasionally exhibits delayed convergence and excels at discovery but fails at utilization. We suggest an improved code dubbed the dynamic but instead global autonomous pollinator strategy according to worldwide best guidance in order to enhance the computational efficiency. In this article, we suggest a fresh approach to revising the observer beekeepers'answer. Our approach uses the finest option available at the time to increase abuse potential. In order to improve the worldwide integration, the existing inhabitants is also produced using a random system. Additionally, the program initially focuses on worldwide investigation before switching to extraction as it nears its conclusion. We test the effectiveness of our suggested approach on two different groups of issues: the chaos time - series data forecast using the Stochastic technique, and mathematical standard expressions. The suggested approach can reach better quality answers with quicker completion than the traditional Accounting or some other enhanced techniques, according to research observations. © 2023 IEEE.

关键词： Artificial Bee Colony (ABC) algorithm Chaos time-series data forecast Computational efficiency Experimental results and analysis Improved algorithm Navigating search spaces Numerical application Observer beekeepers Stochastic technique Swarm intelligence

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Poisoning Attack against Online Regression Learning with Maximum Loss for Edge Intelligence

Poisoning Attack against Online Regression Learning with Max...

引用

2022 international conference on computing, Communication, Perception and Quantum technology, CCPQT 2022

作者： Zhu, Yanxu Wen, Hong Zhang, Peng Han, Wen Sun, Fan Jia, Jia School of Aeronautics and Astronautics University of Electronic Science and Technology Chengdu China Beijing Special Engineering and Design Institute Beijing China

ISBN: (数字)9781665470209

ISBN: (纸本)9781665470209

Recent trends in the convergence of edge computing and artificial intelligence (AI) have led to a new paradigm of 'edge intelligence', which are more vulnerable to attack such as data and model poisoning and evasion of attacks. This paper proposes a white-box poisoning attack against online regression model for edge intelligence environment, which aim to prepare the protection methods in the future. Firstly, the new method selects data points from original stream with maximum loss by two selection strategies;Secondly, it pollutes these points with gradient ascent strategy. At last, it injects polluted points into original stream being sent to target model to complete the attack process. We extensively evaluate our proposed attack on open dataset, the results of which demonstrate the effectiveness of the novel attack method and the real implications of poisoning attack in a case study electric energy prediction application. © 2022 IEEE.

关键词： Regression analysis

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Communication-Efficient Federated Learning Based on Chained Secure Multiparty computing and Compressed Sensing 24

Communication-Efficient Federated Learning Based on Chained ...

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24th IEEE international conference on High Performance computing and Communications, 8th IEEE international conference on Data Science and Systems, 20th IEEE international conference on Smart City and 8th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Xiao, Di Jiang, Pengcen Li, Min College of Computer Science Chongqing University Chongqing China

ISBN: (纸本)9798350319934

Federal learning (FL), as a new technology with privacy protection capabilities, has been widely used. However, the protection capabilities of FL are limited and a lot of communication overhead is required, which is often limited by communication environments. In order to improve the privacy protection and reduce the communication burden, we combine secure multi-party computing(SMC) and compressed sensing(CS) to propose a new chained federated learning scheme called Chain-FL via CS (Chain-FL-CS). We first design a heuristic compression transmission strategy for the model based on CS so that the model can be compressed with a lower loss. Then we use a chained-SMC to perform lossless and secure aggregation of the calculation results. This technology mainly uses masking mechanism for communication. Compared with other schemes, ours has lower complexity. We have conducted experiments using a Deep Neural Network Model (DNN). Experimental results show that our scheme can guarantee the convergence of model under the premise of high transmission efficiency, and it has certain privacy protection capability. © 2022 IEEE.

关键词： Compressed sensing

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Skeleton Based Periodicity Analysis of Repetitive Actions 7

Skeleton Based Periodicity Analysis of Repetitive Actions

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7th IEEE international conference for convergence in technology, I2CT 2022

作者： Wedasingha, Nushara Samarasinghe, Pradeepa Seneviratne, Lasantha Puiatti, Alessandro Papandrea, Michela Dhanayaka, Dulangi Sri Lanka Institute of Information Technology Faculty of Computing Sri Lanka Switzerland Switzerland University of Colombo Faculty of Medicine Colombo Sri Lanka

ISBN: (纸本)9781665421683

This paper investigates the problem of detecting and recognizing repetitive actions performed by a human. Repetitive action analysis play a major role in detecting many behavioral disorders. In this work, we present a robust framework for detecting and recognizing repetitive actions performed by a human subject based on periodic and aperiodic action analysis. Our framework uses focal joints in the human skeleton for the analysis of repetitive actions which are substantiated by the principles of human anatomy and physiology. Using Non-deterministic Finite Automata (NFA) techniques, in this paper, we introduce a novel model to transform repetitive action count to differentiate the periodicity in human action. Experimental results on a dataset consisting of 371 video clips show that our algorithm outperforms the state-of-art (RepNet) [1] in simultaneous multiple repetitive action counts. Further, while the proposed model and RepNet give comparable results in counting periodic repetitive actions, our model performance surpass RepNet significantly on analysing non-periodic repetitive behavior. © 2022 IEEE.

关键词： Musculoskeletal system

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