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Edge computing Task Offloading Based on Game Theory for Space-Air-Ground Integrated Network 6

Edge Computing Task Offloading Based on Game Theory for Spac...

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6th international conference on Communications, Information System and Computer Engineering, CISCE 2024

作者： Gao, Yuanyuan Liu, Jiayan Geng, Suiyan Zhao, Xiongwen Chen, Zhiyu Zhou, Hongxi North China Electric Power University State Key Laboratory of Alternate Electrical Power System with Renewable Energy Source Beijing102206 China State Grid Information & Telecommunication Branch Beijing100761 China

ISBN: (纸本)9798350353174

This study investigates an efficient computational resource offloading mechanism based on game theory for space-air-ground integrated network (SAGIN). In the system for the LEO satellite, Unmanned Aerial Vehicles (UAVs) and users, parameters of time delay and energy consumption are considered to improve overall system performance. Moreover, based on the equilibrium of multilayer game theory, the resource allocation and pricing algorithm are taken into account to maximize system profit. Furthermore, based game algorithm (BGA) is proposed and compared with other benchmark methods such as random task offloading (RTO) and all user's tasks offloading (AUTO) to UAV in the system for performance analysis. Results show that the proposed BGA exhibits good convergence and outperforms, and thus provide useful information for design of SAGIN. © 2024 IEEE.

关键词： Resource allocation

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YOLO-FL:Lightweight and Efficient Multivariate Fruit Freshness Evaluation and Recognition 12

YOLO-FL:Lightweight and Efficient Multivariate Fruit Freshne...

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12th international conference on Information Systems and computing technology

作者： Liang, Zaoqing Ning, Chenfei Chen, Haoqi Wang, Wenbao Zheng, Qingliao Li, Yin South China Agr Univ Coll Math & Informat Guangzhou Peoples R China Guangzhou Inst Software Guangzhou Inst Software GZIS Guangzhou Peoples R China

ISBN: (纸本)9798350379860;9798350379877

To achieve the rapid and accurate identification of the freshness of various fruit varieties in a complex environment, a fast detection model for multi-class fruit targets based on an improved YOLO, YOLO-FL (YOLO with fast and light), is proposed. This model is based on YOLOv5 and utilizes RMELAN to replace the traditional C3 structure, employing the efficient and lightweight design module RMELAN as the basic unit of the backbone feature extraction network to reduce the model's parameters and calculations, thereby accelerating the model's convergence speed;it optimizes the existing downsampling module and introduces the Down downsampling module, which helps to expand the receptive field and improve the performance and efficiency of the model, aiding the model to capture features of the image at higher levels. The Slide Loss function dynamically assigns different weights to simple and difficult samples, effectively addressing the problem of unbalanced sample classes in the dataset and focusing on the recognition of difficult samples. Experimental results on a homemade dataset containing 16 categories with 14,144 images at a resolution of 640x640 showed that the proposed model achieved a mAP@0.5 (mean Average Precision IoU=0.5) of 94.4%, an improvement of 4.3% over the benchmark model YOLOv5n's 90.1%, and reduced the GFLOPS (Giga Floating-point Operations Per Second) by 33% from 4.2 GFLOPs to 2.8 GFLOPs compared to the benchmark model. Conclusion: The YOLO-FL model can meet the requirements for identifying fruit varieties and freshness, and has improved misidentification and missed detection in complex scenarios.

关键词： fruit variety identification lightweight deep learning object detection

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Research on Digital Twin Modeling technology for Distribution Networks Integrating Multiple Digital Resources 3

Research on Digital Twin Modeling Technology for Distributio...

引用

3rd international conference on Intelligent computing, Communication and convergence, ICI3C 2023

作者： Qiao, Junfeng Zhou, Aihua Peng, Lin Su, Hang Qian, Zhonghao Xu, Min Pan, Sen Yang, Pei State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology State Grid Smart Grid Research Institute Co. Ltd Jiangsu Nanjing210003 China Xuzhou Power Supply Company State Grid Jiangsu Electric Power Co. Ltd Jiangsu Xuzhou221005 China Nantong Power Supply Company State Grid Jiangsu Electric Power Co. Ltd Jiangsu Nantong226006 China

ISBN: (纸本)9798350343922

The sources of digital resources in the distribution network mainly include three aspects: equipment ontology, production operation, and management. The data is described in various forms and stored in various types of systems and data platforms. However, with the continuous development and upgrading of distribution network business, the fusion of diverse data resources to drive decision-making has become a current development trend. Therefore, how to integrate and apply diverse digital resources is the key to the current development of distribution networks. In response to this issue, the article proposes a digital twin modeling technique for distribution networks that integrates multiple digital resources. Firstly, build a cross platform data mapping system based on the production and operation data management rules of the distribution network, and then use data access and integration technology to integrate diverse data resources into a unified data platform. Utilize the unified data model of the power grid to align multiple data resources and map the aligned data resources to the digital twin model. Simultaneously defining data synchronization update interfaces to ensure real-time and accurate data in the digital twin model. Finally, a typical scenario application of the distribution network based on the digital twin model is carried out to verify the effectiveness and practicality of the model. © 2023 IEEE.

关键词： digital resources digital twin mapping distribution network digital twin model distribution network operation management integration of diverse digital resources

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An Enhanced Whale Optimization Algorithm for Task Scheduling in Cloud computing

An Enhanced Whale Optimization Algorithm for Task Scheduling...

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international conference on 6G Communications Networking and Signal Processing, SGCNSP 2023

作者： Srilatha, P. Ramadan, Ghazi Mohamad Kumar, T. M. Kiran Alekhya, Y. Pani, Alok Kumar Department of AI & DS Chaitanya Bharathi Institute of Technology Telangana Hyderabad India College of MLT Ahl Al Bayt University Karbala Iraq Department of CSE Siddaganga Institute of Technology Tumakuru India Maisammaguda Hyderabad India Bangalore India

ISBN: (纸本)9789819778751

Task Scheduling is the significant challenge in the environment of Cloud computing (CC) and has attention in numerous researchers in recent years with respect to attain cost effective computation and improve resource utilization. The existing algorithms has limitations of role and selection criteria of inertia weight was not considered. In this research, Enhanced Whale Optimization Algorithm (EWOA) is proposed for maximize effectiveness of task scheduling in CC. An inertia weight is implemented in WOA algorithm that enhances the convergence and accuracy of algorithm that helps in task scheduling effectiveness. The performance of proposed technique is estimated with performance measure of Makespan (ms), execution time (s) and resource utilization (%). The proposed method attained less execution time of 2304, 2537, 2765, 2983 and 3016 s for 200, 400, 600, 800 and 1000 number of tasks. The proposed method attained the superior results when compared with other existing algorithms like Ant Colony Optimization (ACO), Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cloud computing Inertia weight Resource utilization Task scheduling Whale optimization algorithm

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A Study of the Effect of Different Parameter Assignment Strategies on the Performance of ACLPSO 16

A Study of the Effect of Different Parameter Assignment Stra...

引用

16th international conference on Cyber-Enabled Distributed computing and Knowledge Discover, CyberC 2024

作者： Zhang, Yi Yu, Xiang Nanchang Institute of Technology School of Information Engineering Nanchang China

ISBN: (纸本)9798331506896

Adaptive comprehensive learning particle swarm optimization (ACLPSO) is a variant of particle swarm optimization (PSO) we previously proposed, which achieves a good balance between exploration and exploitation. In ACLPSO, the interval scaling coefficient s and the learning probability tradeoff coefficient v are crucial parameters influencing the convergence performance of the algorithm. ACLPSO requires manual setting of these two parameters to handle different benchmark functions, but there are no specific rules for assigning their values. This paper proposes different assignment strategies for s and v, allowing them to periodically change according to different function curves or adaptively adjust based on the change rate of global best fitness value, resulting in different combinations of s and v at different stages of the iteration, including the most appropriate combination of s and v at that stage. In this paper, these parameter assignment strategies are compared on different benchmark functions, and the experimental results show that five parameter assignment strategies can achieve the global optimum or a near optimum on certain benchmark functions. Although the final results of the five assignment strategies are overall not better than the ACLPSO under the most appropriate combination of s and v, this study lays the foundation for our subsequent research on ACLPSO to jump out of local optima. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Indoor Pedestrian-Following System by a Drone with Edge computing and Neural Networks: Part 1 - System Design 23

Indoor Pedestrian-Following System by a Drone with Edge Comp...

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23rd international conference on Control, Automation and Systems, ICCAS 2023

作者： Ryu, In-Chan Ham, Jung-Il Park, Jun-Oh Joeng, Jae-Woo Kim, Sung-Chang Ahn, Hyo-Sung School of Mechanical Engineering Gwangju Institute of Science and Technology Gwangju61005 Korea Republic of Electronics and Telecommunications Research Institute Edge Computing Application Service Research Section Gwangju61011 Korea Republic of

ISBN: (纸本)9788993215274

As the drone market continues to expand, the need for accurately determining a drone's position and orientation using a camera in GPS-denied environments becomes increasingly critical. This paper aims to achieve precise position and attitude data by incorporating SLAM to provide visual measurements for EKF, thereby ensuring the stability of drone operations. An experiment was conducted to execute commands from the ground control PC using the map created as a result of SLAM. The primary tools used for this purpose included the Pixhawk Orange, Jetson Nano, and the ZED-Mini camera. The research showcases the effectiveness of these tools and methods in enhancing indoor drone functionality. © 2023 ICROS.

关键词： Extended Kalman filters

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Lightweight GWO-LSTM-Based Land Cover Classification using Remote Sensing Images

Lightweight GWO-LSTM-Based Land Cover Classification using R...

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2023 international conference on Evolutionary Algorithms and Soft computing Techniques, EASCT 2023

作者： Regan, D. Ravindran, Gobinath Senthil, P. Rani, M. Jamuna Chakraborty, Subhra Siddartha Institute of Science and Technology Ece Puttur India Sr University Department of Civil Engineering Warangal India S.A. Engineering College Department of Information Technology Chennai India Sona College of Technology Department of Ece Salem India Nitte Meenakshi Institute of Technology Department of Ece Bengaluru India

ISBN: (纸本)9798350313413

The classification of land use and land cover (LULC) using remote sensing data is essential for many environmental models and land-use inventories. A lightweight deep learning classifier is implemented in this research to improve the performance of LULC classification, assisting in the prediction of declining environmental quality, haphazard elements, wildlife habitat, and so on. LULC classification is evaluated using Eurosat dataset and it uses algorithms like the Haralick texture features, histogram of the oriented gradient, and local Gabor binary pattern histogram sequence for feature extraction. The Grey Wolf Optimization (GWO) method is applied to select the best features, offering fast convergence tares and ease of implementation. A Long Short-Term Memory (LSTM) network is then used to categorize the LULC. The research outcomes show that the GWO method with an LSTM classifier efficiently differentiates the classification of LULC in terms of 99.80% accuracy, 98.99% precision, and 99.53% recall when compared to the Deep SHAP, HFEL-CCGSA, Human group-based PSO with LSTM and IMO-mLSTM. © 2023 IEEE.

关键词： and Remote sensing images Haralick texture feature Histogram of oriented gradient Land cover optimization Land use

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Computationally Efficient Numerical Analysis of Rotavirus Epidemic Model 4

Computationally Efficient Numerical Analysis of Rotavirus Ep...

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4th international conference on computing, Mathematics and Engineering Technologies, iCoMET 2023

作者： Rafiq, Muhammad Sarwar Ehsan, Muhammad Nisar, Asfar Abbas, Samra University of Central Punjab Faculty of Science and Technology Lahore Pakistan University of Central Punjab Faculty of It&cs Lahore Pakistan

ISBN: (纸本)9798350335316

In this manuscript, a mathematical model of a rotavirus infection integrating the vaccinated class is numerically analyzed. Efficient numerical analysis of an epidemic model includes three main features positivity, boundedness, and dynamical consistency. These characteristics have been observed by using various numerical techniques. Standard finite difference scheme, Euler's, RK-4 is widely used to solve non-linear mathematical models. Unfortunately, these schemes have some limitations and do not preserve the essential features of the mathematical model. A competitive non-standard finite difference (NSFD) scheme is proposed to discuss the dynamics of rotavirus in a population. The proposed scheme exhibits the true behavior of the rotavirus disease and shows a good agreement with the theoretical findings. Moreover, the impact of vaccines on the rotavirus dynamics has also been studied. © 2023 IEEE.

关键词： convergence of numerical methods

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Game Theory Applications in AI 4

Game Theory Applications in AI

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4th international conference on Advancement in Electronics and Communication Engineering, AECE 2024

作者： Rathore, Rachna Sharma, Pranay Sharma, Umesh Chandra Jenasamanta, Abhinash Shilpa, S. Sahu, Praveen Kumar Jiwaji University Dept. of Mathematics Gwalior India Madhav University Dept. of Cse Sirohi India Mangalayatan University Dept. of Cea UP Aligarh India Usha Martin University Dept. of Computing and Information Technology Jharkhand Ranchi India Dhanalakshmi University Dept. of Cse Coimbatore India Lingayas Vidyapeeth Dept. of Agriculture Faridabad India

ISBN: (纸本)9798350364729

The convergence of artificial intelligence (AI) and game theory presents intriguing research and application opportunities. This overview explores the fundamental concepts of game theory and its influence on AI systems and techniques. Various AI applications of game theory are examined, including mechanism design, auctions, adversarial machine learning, reinforcement learning, and multi-agent systems. Additionally, we delve into game-theoretic methods utilized in AI, such as cooperative game theory, Nash equilibria computation, and the minimax algorithm. Through case studies and comparative analysis, we assess the strengths, weaknesses, current challenges, and future directions in this field. The aim of this review is to illuminate the synergies between game theory and artificial intelligence. © 2024 IEEE.

关键词： Adversarial machine learning

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ADAPTIVE STOCHASTIC GRADIENT ALGORITHM FOR BLACK-BOX MULTI-OBJECTIVE LEARNING 12

ADAPTIVE STOCHASTIC GRADIENT ALGORITHM FOR BLACK-BOX MULTI-O...

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12th international conference on Learning Representations, ICLR 2024

作者： Ye, Feiyang Lyu, Yueming Wang, Xuehao Zhang, Yu Tsang, Ivor W. Department of Computer Science and Engineering Southern University of Science and Technology China Australian Artificial Intelligence Institute University of Technology Sydney Australia Centre for Frontier AI Research Agency for Science Technology and Research Singapore Institute of High Performance Computing Agency for Science Technology and Research Singapore Shanghai Artificial Intelligence Laboratory China School of Computer Science and Engineering Nanyang Technological University Singapore

Multi-objective optimization (MOO) has become an influential framework for various machine learning problems, including reinforcement learning and multitask learning. In this paper, we study the black-box multi-objective optimization problem, where we aim to optimize multiple potentially conflicting objectives with function queries only. To address this challenging problem and find a Pareto optimal solution or the Pareto stationary solution, we propose a novel adaptive stochastic gradient algorithm for black-box MOO, called ASMG. Specifically, we use the stochastic gradient approximation method to obtain the gradient for the distribution parameters of the Gaussian smoothed MOO with function queries only. Subsequently, an adaptive weight is employed to aggregate all stochastic gradients to optimize all objective functions effectively. Theoretically, we explicitly provide the connection between the original MOO problem and the corresponding Gaussian smoothed MOO problem and prove the convergence rate for the proposed ASMG algorithm in both convex and non-convex scenarios. Empirically, the proposed ASMG method achieves competitive performance on multiple numerical benchmark problems. Additionally, the state-of-the-art performance on the black-box multi-task learning problem demonstrates the effectiveness of the proposed ASMG method. © 2024 12th international conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Multiobjective optimization

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