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A DRL-Based Intelligent Task Offloading and Caching Strategy for IIoT in MEC 6

A DRL-Based Intelligent Task Offloading and Caching Strategy...

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

作者： Deng, Yunfeng Peng, Anjie School of Computer Science and Technology Southwest University of Science and Technology Mianyang China

ISBN: (纸本)9798350353174

In IIoT (Industrial Internet of Things) systems, diverse smart devices generate various types of computational tasks, often with low latency requirements. Leveraging computation offloading techniques in MEC (Mobile Edge computing), these tasks can be sent to MEC servers for auxiliary computing. However, the dynamic changes in the environment and the increasing number of smart devices in the system lead to competition for the limited resources on the servers, making it crucial to develop rational offloading strategies. Motivated by this, to ensure efficient and stable operation of IIoT systems, we design intelligent offloading and task caching strategies for single-edge mobile networks to reduce the overall execution latency of intelligent tasks. This paper establishes a system model from three perspectives: task offloading, task caching, and task queue scheduling, and formulates a joint optimization problem for task offloading and task caching. We transform the interaction process of the system into a Markov decision process (MDP) and propose a low-latency scheduling strategy based on DRL (Deep Reinforcement Learning). At each time slot, this strategy collects dynamic environment information and reference task priority values to make offloading and caching decisions. Simulation results demonstrate that the proposed algorithm achieves rapid and stable convergence and effectively reduces the completion latency of intelligent tasks. © 2024 IEEE.

关键词： Industrial internet of things (IIoT)

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MSADeepLoc: Subcellular Localization Prediction Using MSA and Protein Language Model 7

MSADeepLoc: Subcellular Localization Prediction Using MSA an...

引用

7th international conference on Algorithms, computing and Artificial Intelligence, ACAI 2024

作者： Zhao, Wenhui Zhong, Yixin Cao, Yi He, Wenxing Zhao, Yaou Chen, Yuehui Artificial Intelligence Institute University of Jinan Jinan China School of Biological Science and Technology University of Jinan Jinan China

ISBN: (纸本)9798331529314

Protein subcellular localization (PSCL) prediction is a key research area in bioinformatics. However, most existing models have ignored the homology and diversity among protein sequences, which can reveal the functional sequence segments and indirectly reflect the structure of the protein. Consequently, the achieved performance is limited. To break through this limitation, we propose an end-to-end deep learning model for PSCL prediction, namely, MSADeepLoc. In this model, the protein features are extracted by a protein language model, the MSA Transformer. The MSA Transformer can learn the evolutionary conservation and variability among protein sequences from extensive multiple sequence alignments, effectively capturing and refining key biological information within the sequence structure. Additionally, we apply a pre-trained Conditional Generative Adversarial Network (CGAN) to solve the data imbalance issue. Extensive experiments demonstrate that our proposed MSADeepLoc outperforms the state-of-the-art models in the task of PSCL prediction. © 2024 IEEE.

关键词： Generative adversarial networks

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A Way of Optimization of Wireless Sensor Network using TSCH 4

A Way of Optimization of Wireless Sensor Network using TSCH

引用

4th IEEE international conference on Advance computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Aravind, A.R. Prajapati, Manju Arunkumar, E. Kumar, Rakesh Kumar, Himanshu Rao, S. P. V. Subba Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering India M.P Bhopal India Karpagam College of Engineering Department of Electronics and Communication Engineering India Gla University Department of Computer Engineering & Applications Mathura India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh India Sreenidhi Institute of Science and Technology Department of Electronics and Communication Engineering Hyderabad India

ISBN: (纸本)9798350360165

Within ad hoc wireless networks, TimeSynchronized Channel Hop (TSCH) is a potential method for interference-free and collision-free communication, especially in the setting of wireless sensor networks, car networks, and networks containing robots or drones. Even though TSCH and its versions work well, they need centralized planning to keep the time-frequency sliding system in place. This leads to a restricted usage of time-frequency slots and slow convergence, especially when there is node change or movement. Using the idea of pulse-coupled oscillators, the protocol pairs distributed synchronization or desynchronization methods at the MAC layer to coordinate nodes to perform coordinated signal packet broadcasts over all available channels. Afterwards, peer-to-peer swap requests and acknowledgements between ongoing emitter in neighboring channels result in autonomous channel moving. Extensive evaluation against TSCH and the power source Successful It MAC (EM-MAC) protocol suggests that DT-SCS is a strong contender for centralized multi-channel MAC layer coordination due to its quick convergence, which refers strong connectivity, and high bandwidth utilization even in the midst of interference alongside hidden nodes. © 2024 IEEE.

关键词： Network layers

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Optimization of cloud computing task scheduling based on whale optimization algorithm with quasi-opposition-based and nonlinear factor 6

Optimization of cloud computing task scheduling based on wha...

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6th international conference on Internet of Things, Automation and Artificial Intelligence, IoTAAI 2024

作者： Xuan, Xiong Li, Shaobo Yu, Liya Wu, Fengbin Zhang, Xingxing An, Xue College of Computer Science and Technology Guizhou University State Key Laboratory of Public Big Data Guiyang550025 China Guizhou University State Key Laboratory of Public Big Data Guiyang550025 China Guizhou University College of Mechanical Engineering Guiyang550025 China

ISBN: (纸本)9798350386974

Task scheduling has a significant impact on the resource availability and operation cost of the system in cloud computing. In order to improve the efficiency of task execution in the cloud for a given computational resource, the improved whale optimization algorithm is applied to the multi-objective optimization model for cloud task scheduling. Specifically, the quasi-opposition-based learning strategy is added in the initiation phase to increase the diversity of the population. Meanwhile, a novel nonlinear convergence factor was introduced to augment the local search capability. Experimental results indicate that the improved whale optimization algorithm converges faster and searches with higher precision in finding the optimal task scheduling solution compared to existing meta-heuristic algorithms. In addition, the modified algorithm achieves superior system resource usage when dealing with both small and large tasks. © 2024 IEEE.

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Revamping Financial Management Strategies for the Digital Era via Augmented Blockchain Security Measures 2

Revamping Financial Management Strategies for the Digital Er...

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2nd IEEE international conference on Trends in Quantum computing and Emerging Business Technologies, TQCEBT 2024

作者： Patel, Chhaya Goel, Manjusha Kumavat, Prakashraj P. Patel, Mitesh J. Dodiya, Shakti B. Chakravarthi, M. Kalyan Sankalchand Patel University Sankalchand Patel College of Engineering Department of Business Management North Guj. Visnagar India Raj Kumar Goel Institute of Technology Department of Management Studies Ghaziabad India VIT-AP University School of Electronics Engineering AmaravathiPin Code: 522237 India

ISBN: (纸本)9798350384277

The convergence of blockchain technology with artificial intelligence is giving rise to a new standard for modern financial management. Through an examination of both the theoretical and practical aspects, this paper delves into the potential revolutionary possibilities of improved blockchain security measures. The method uses historical financial data, blockchain metrics, and AI models to provide a thorough foundation for empirical validation. Machine learning model accuracy and recall rates were enhanced, transaction processing times were shortened, and the number of fraud instances dropped significantly. Improving transactional efficiency, creating reliable predictive analytics, fortifying the financial ecosystem, and expanding financial services are all examples of real-world consequences. Experts, legislators, and scholars attempting to make sense of the intricate digital financial environment will find some useful pointers in the last section. The next stages include research into ethics, legislation changes, decentralised financing, empirical validation, and scalability. A more secure, efficient, and easily accessible international monetary system is the goal of this paper, which offers recommendations for enhancing blockchain security protocols. © 2024 IEEE.

关键词： Decentralized finance

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Optimized Deployment technology for Heterogeneous Multi-base Sonar System Based on The IHS-ABC Algorithm 14

Optimized Deployment Technology for Heterogeneous Multi-base...

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14th IEEE international conference on Signal Processing, Communications and computing, ICSPCC 2024

作者： Sheng, Xueli Liu, Manxin Tang, Lei Ji, Guoliang Wang, Yan Miao, Hongbo Harbin Engineering University College of Underwater Acoustic Engineering National Key Laboratory of Underwater Acoustic Technology Key Laboratory for Polar Acoustics and Application Ministry of Education Harbin China

ISBN: (纸本)9798350366556

In addressing the optimization deployment issues of heterogeneous multi-base sonar systems, this paper establishes a detection performance evaluation model for heterogeneous multi-base sonar systems in complex underwater environments and proposes a hybrid algorithm combining improved harmony search (IHS) and artificial bee colony (ABC) algorithm called IHS-ABC. This algorithm draws from the concepts of the IHS algorithm, constructs a source memory, employs a full-dimensional probability selection strategy, and modifies the scouting mechanism. The precision and convergence speed of the algorithm are validated using classic benchmark functions. The algorithm is applied to optimize the deployment of heterogeneous multi-base sonar systems in various marine environments under the criterion of maximum coverage. Simulation results indicate that the optimized detection coverage significantly increased, achieving the goal of optimized deployment and enhancing detection performance. © 2024 IEEE.

关键词： Sonar

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Rapid prediction of atmospheric pollutants in local region through optimized RBF methods 3

Rapid prediction of atmospheric pollutants in local region t...

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3rd international conference on Intelligent computing and Next Generation Networks, ICNGN 2024

作者： Li, Jianyang Li, Gang Wang, Ming School of electronics & information Zhenjiang Institute of Technology Zhenjiang China School of Management Hefei University of Technology Hefei China E-schooling center Zhenjiang Institute of Technology Zhenjiang China

ISBN: (纸本)9798331529222

Addressing the problem that multiple complex factors affect the emission and accumulation of Main Air Pollutants in local region, the optimized RBF (Radial Basis Function) neural networks methods are suggesting to find local atmospheric pollutants concentration and its relationship with real-time tracking in this paper. Firstly it acquires meteorological conditions, such as complex ground conditions and multiple- emission sources at the scale of local areas and residential areas;secondly RBF connection mechanism is reconstructed to effectively utilize the linear information between input and output neurons, improve the generalization performance of the network, and quickly construct high-precision self-organizing RBF neural networks;thirdly an efficient and accurate pollutant concentration evaluation model is constructed to evaluate the concentration of pollutants in local region using emission data predicted by energy economic models. Experimental results indicate that our RBF mechanism is efficient for it can not only maintain the stability of the network output with a compact structure in the entire training process where no global knowledge is required to be maintained, but also has superior generalization ability and faster convergence speed which are bounded by the number of neighbors with respect to the total time for satisfying online prediction. ©2024 IEEE.

关键词： Radial basis function networks

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Design of intelligent slag removal system in power plant based on image recognition

Design of intelligent slag removal system in power plant bas...

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2024 international conference on Algorithms, High Performance computing, and Artificial Intelligence, AHPCAI 2024

作者： Fu, Changliang Zhang, Yongbin Xie, Xiaodong Zong, Zhiqiang Wei, Xiao Wang, Xinyue Beijing guodian futong science and technology development co. ltd. Tainjin China

ISBN: (纸本)9781510685840

The accurate identification of large and medium slag in power plant slag transport system has an important impact on the safety, efficiency, environmental protection and economic benefits of power plant. The timely processing of large slag can effectively improve the operation efficiency and maintenance cost of power plant, and with the rapid development of image recognition technology, the recognition of large slag in power plant slag transport system is gradually intelligent. In this paper, based on CenterNet image recognition algorithm, through the adaptive spatial feature fusion module is added in the backbone network, and combined with the feature of the pyramid networks structure. The low-level feature information is fused with the high-level feature information. The loss function is replaced by the distributed focus loss, and the effectiveness of the system is verified by experiments. The results show that the system has good convergence, can accurately identify the size and position of large slag block, can respond to the slag removal command quickly, improve the operation efficiency of the power plant slag delivery system, reduce the manual maintenance cost, and improve the operation safety of the power plant. © 2024 SPIE.

关键词： Detection and tracking algorithms Intelligence systems Target recognition Convolution Image processing Feature fusion Safety Infrared radiation Combustion Thermal modeling

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Achieving Cost Efficiency and Load Sharing in Power Systems: The Superiority of Adaptive BAT Algorithm

Achieving Cost Efficiency and Load Sharing in Power Systems:...

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2024 international conference on Communication, computing and Internet of Things, IC3IoT 2024

作者： Reddy, M. Ram Prasad Kumar, D. Shashi Sanjay, Sree Ram Kumar, P Manoj Bharath, Y Sai G.Pullaiah College Of Engineering And Technology Department Of Eee Andhra Pradesh Kurnool India

ISBN: (纸本)9798350352689

This research paper presents a novel approach to solving optimization problems using the BAT Algorithm with parameter tuning, local mechanism escaping, and adaptive control. The proposed algorithm is evaluated using the IEEE 30-bus system as a test system. The BAT Algorithm is a nature-inspired metaheuristic algorithm that mimics the echolocation behavior of bats. By incorporating parameter tuning, the algorithm's performance is enhanced by fine-tuning the algorithm's key parameters, such as loudness and pulse rate. The local mechanism escaping mechanism allows the algorithm to explore new regions of the search space, promoting diversification and avoiding local optima. The algorithm's performance is evaluated in terms of solution quality, convergence rate, and robustness. Comparative analysis is conducted by comparing the proposed algorithm with other well-known optimization *** superior performance of the proposed BAT algorithm with parameter tuning, local mechanism escaping, and adaptive control in results. The algorithm exhibits strong convergence and exploration abilities, leading to high-quality solutions for the optimization problem. Furthermore, the algorithm demonstrates its effectiveness in solving the IEEE 30-bus system optimization problem, showcasing its potential for real-world applications in power systems and related domains. The findings demonstrate the algorithm's capability to outperform other state-of-the-art optimization algorithms, including DE, ADE, Cuckoo Search, EADE, TLBO, among others. © 2024 IEEE.

关键词： Optimization

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Competitive Swarm Optimizer Based on Individual Learning Mechanism 5

Competitive Swarm Optimizer Based on Individual Learning Mec...

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5th international conference on High Performance Big Data and Intelligent Systems, HDIS 2023

作者： Tang, JingQi Li, Wei Wang, Lei School of Computer Science and Engineering Xi’an University of Technology Xi’an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an China

ISBN: (纸本)9798350394160

In view of the disadvantages of the traditional competitive swarm optimizer (CSO), such as falling into local minimization or poor convergence accuracy, this paper proposed an enhanced CSO algorithm called competitive swarm optimizer based on individual learning mechanism (ILCSO). Firstly, the individual selection rate and the learning rate are designed to dynamically select the winner and loser. The losers are updated by the precise competitive learning strategy to improve the algorithm exploitation ability. Secondly, the mutation strategy based on performance improvement is introduced, which improves the search ability of algorithm. The proposed algorithm effectively balances the local search and global exploration with the individual learning mechanism, and improves the probability of finding the global optimal solution. Finally, ILCSO is compared with six classical meta-heuristic algorithms on CEC2014 benchmark functions. The Wilcoxon rank-sum test is used to demonstrate that the ILCSO is effective. Experimental results and statistical analysis show that ILCSO has higher convergence speed and convergence accuracy. ©2023 IEEE.

关键词： Swarm intelligence

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