Article
Computer Science, Information Systems
Fahd N. Al-Wesabi, Imran Khan, Saleem Latteef Mohammed, Huda Farooq Jameel, Mohammad Alamgeer, Ali M. Al-Sharafi, Byung Seo Kim
Summary: This article introduces the importance of Device-to-Device (D2D) communication technology in the 5G mobile communication system and proposes an optimization method based on game-matching theory to improve resource allocation and utilization.
CMC-COMPUTERS MATERIALS & CONTINUA
(2022)
Article
Computer Science, Information Systems
Ahmed A. Rosas, Mona Shokair, M. Dessouky
Summary: This paper studies the joint consideration of power and channel allocation based on genetic algorithm for D2D underlaied cellular networks. The proposed approach demonstrates advantages in maximizing overall system utilization compared to other allocation schemes.
CMC-COMPUTERS MATERIALS & CONTINUA
(2022)
Article
Computer Science, Artificial Intelligence
Qing An, Shisong Wu, Jun Yu, Cuifen Gao
Summary: In this study, a constrained optimization problem with thousands of dimensions is formulated to model the resource allocation in a large-scale device-to-device communication system. The variable-coupling relationship in the developed model is analyzed and a novel algorithm called multi-modal mutation cooperatively coevolving particle swarm optimization is proposed to optimize the high-dimensional model. Experimental results demonstrate that the developed algorithm achieves accurate and robust optimization performance for different system scales, outperforming compared algorithms even for a system with 1000 cellular users and 300 D2D-pair users.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Computer Science, Information Systems
Fahad Ahmed Al-Zahrani, Imran Khan, Mahdi Zareei, Asim Zeb, Abdul Waheed
Summary: The next-generation wireless networks are expected to provide higher capacity, system throughput, and improved energy efficiency. Device to-device (D2D) communication is a key technology for high-rate transmission, but it may cause interference to existing cellular systems. Researchers need to focus on spectrum resource utilization and energy consumption in D2D communication.
CMC-COMPUTERS MATERIALS & CONTINUA
(2021)
Article
Chemistry, Analytical
Lu Zhao, Mingyue Zhou
Summary: This study proposes an algorithm for cognitive radio power allocation, which considers worst-case channel transmission model and robust model to improve fairness among cognitive users and effectively utilize channel resources. The algorithm has simple implementation, fast convergence, and good optimization results.
Article
Computer Science, Information Systems
Panduranga Ravi Teja, Pavan Kumar Mishra
Summary: This paper proposes a path selection and resource allocation method for multi-hop D2D communication. The path selection method is based on the Q learning model to calculate cumulative reward for selecting the best path. The resource allocation method consists of two stages – using the MMF method for resource allocation and applying the HPSOGWO method to optimize power distribution for maximizing system throughput. Compared to previous approaches, this research improves system consistency and implementation in all aspects.
COMPUTER COMMUNICATIONS
(2022)
Article
Engineering, Electrical & Electronic
Jiawei Su, Zhixin Liu, Yuan-ai Xie, Kai Ma, Hongyang Du, Jiawen Kang, Dusit Niyato
Summary: The study proposes a long-term robust resource allocation scheme to address the challenges of reliable and stable transmission of semantic information in high-dynamic vehicular networks. The scheme considers multiple performance indicators and introduces Bernstein approximation and Lyapunov optimization method to solve the resource allocation problem. Simulations show the trade-off relationship between user satisfaction, queue stability, and communication delay, as well as the necessity of considering channel uncertainty in high-speed mobile vehicular communication scenarios.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Computer Science, Information Systems
Adeel Iqbal, Ali Nauman, Riaz Hussain, Irfan Latif Khan, Ali Khaqan, Sana Shuja, Sung Won Kim
Summary: D2D communication is an essential part of 5G cellular networks, allowing close-proximity devices to establish direct communication with advantages such as reduced latency, high data rates, range extension, and cellular offloading. Efficient device discovery is the first step in establishing a D2D session, leading to efficient D2D communication. This work considers six distinct scenarios for initiating D2D communication, taking into account their merits, demerits, limitations, and optimization parameters. Based on the signal flow, D2D communication procedures including device discovery, resource allocation, and session teardown have been formulated for each scenario. Finally, latency evaluation based on propagation and processing delays is conducted for each scenario.
CMC-COMPUTERS MATERIALS & CONTINUA
(2023)
Article
Computer Science, Information Systems
Shahad Alyousif, Mohammed Dauwed, Rafal Nader, Mohammed Hasan Ali, Mustafa Musa Jabar, Ahmed Alkhayyat
Summary: The number of mobile devices accessing wireless networks is increasing rapidly due to the advancement of sensors and wireless communication technology. It is expected that mobile data traffic will continue to rise in the coming years. In order to meet the demands of the Internet of Things, smart homes, and more sophisticated applications, a new cellular network paradigm is being developed. Offloading computation to distant clouds or nearby mobile devices has improved the performance of mobile devices, and D2D collaboration can further reduce task delays. However, the variation in performance capabilities of edge nodes affects the task offloading performance. This paper proposes a new method for D2D communication by enabling edge nodes to exchange data samples, which reduces time delay and demonstrates better performance compared to traditional algorithms.
CMC-COMPUTERS MATERIALS & CONTINUA
(2023)
Article
Computer Science, Information Systems
Rasmus Liborius Bruun, C. Santiago Morejon Garcia, Troels B. Sorensen, Nuno K. Pratas, Tatiana Kozlova Madsen, Preben Mogensen
Summary: Decentralized cooperative resource allocation schemes are crucial for high reliability and high throughput data message exchanges in robotic swarms. Our proposed device sequential and group scheduling schemes, along with the control signaling design, significantly improve reliability and performance.
Article
Chemistry, Analytical
Ruijie Wang, Xun Wen, Fangmin Xu, Zhijian Ye, Haiyan Cao, Zhirui Hu, Xiaoping Yuan
Summary: Device-to-device (D2D) communication is a promising wireless communication technology that reduces the traffic load of the base station and improves spectral efficiency. The application of intelligent reflective surfaces (IRS) in D2D communication systems can further enhance throughput, but interference suppression becomes more complex due to new links. This paper proposes a low-complexity power and phase shift joint optimization algorithm based on particle swarm optimization (PSO) to address this challenge.
Article
Computer Science, Information Systems
Steffi Jayakumar, S. Nandakumar
Summary: Device to Device communication (D2D) is a promising technology in future wireless communication systems, and this paper proposes an RL-based resource allocation scheme to optimize system throughput and fairness. The RL method allows the system to learn from trial-and-error without prior knowledge, and expanding the observation space improves the accuracy of the learning algorithm. Simulation results demonstrate improvements in throughput, energy efficiency, spectrum efficiency, and fairness, compared to conventional methods.
Article
Telecommunications
S. Sreethar, N. Nandhagopal, S. Anbu Karuppusamy, M. Dharmalingam
Summary: In this study, a priority-based resource allocation method is introduced to enhance network performance and ensure quality of service by combining resources from unlicensed and licensed spectrum bands for D2D users in mixed traffic scenarios. The GTOA optimization framework effectively maximizes the utility functions of various users and achieves prioritized resource allocation by reducing energy utilization.
WIRELESS PERSONAL COMMUNICATIONS
(2022)
Article
Engineering, Multidisciplinary
Roopsi Rathi, Neeraj Gupta
Summary: Device to Device communication involves direct communication between two devices without routing through the cellular network, allowing for better resource utilization and reduced communication delay. However, resource allocation remains a major challenge, often leading to issues like interference and throughput problems. Various mathematical tools, including game theory, have been used to address these challenges, with a focus on highlighting progress made in resource allocation and identifying unresolved issues.
AIN SHAMS ENGINEERING JOURNAL
(2021)
Article
Computer Science, Artificial Intelligence
Bosong Duan, Chuangqiang Guo, Hong Liu
Summary: This paper presents a new hybrid genetic-particle swarm optimization algorithm that combines particle swarm optimization and genetic algorithm through parallel architecture. It takes advantage of the efficiency of PSO and the global optimization ability of GA. The algorithm uses PSO initially and switches to GA when the global best value does not change, aiming to escape local optima. The proposed GPSO algorithm also incorporates adaptive features and multi-point crossover operation to enhance the optimization ability. Experimental results show that the proposed algorithm outperforms other algorithms in terms of finding optimal value, convergence speed, and time overhead.
Article
Computer Science, Artificial Intelligence
Jin Zhang, Zekang Bian, Shitong Wang
Summary: This study proposes a novel style linear k-nearest neighbor method to extract stylistic features using matrix expressions and improve the generalizability of the predictor through style membership vectors.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Qifeng Wan, Xuanhua Xu, Jing Han
Summary: In this study, we propose an innovative approach for dimensionality reduction in large-scale group decision-making scenarios that targets linguistic preferences. The method combines TF-IDF feature similarity and information loss entropy to address challenges in decision-making with a large number of decision makers.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Hegui Zhu, Yuchen Ren, Chong Liu, Xiaoyan Sui, Libo Zhang
Summary: This paper proposes an adversarial attack method based on frequency information, which optimizes the imperceptibility and transferability of adversarial examples in white-box and black-box scenarios respectively. Experimental results validate the superiority of the proposed method and its application in real-world online model evaluation reveals their vulnerability.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Jing Tang, Xinwang Liu, Weizhong Wang
Summary: This paper proposes a hybrid generalized TODIM approach in the Fine-Kinney framework to evaluate occupational health and safety hazards. The approach integrates CRP, dynamic SIN, and PLTSs to handle opinion interactions and incomplete opinions among decision makers. The efficiency and rationality of the proposed approach are demonstrated through a numerical example, comparison, and sensitivity studies.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Shigen Shen, Chenpeng Cai, Zhenwei Li, Yizhou Shen, Guowen Wu, Shui Yu
Summary: To address the damage caused by zero-day attacks on SIoT systems, researchers propose a heuristic learning intrusion detection system named DQN-HIDS. By integrating Deep Q-Networks (DQN) into the system, DQN-HIDS gradually improves its ability to identify malicious traffic and reduces resource workloads. Experiments demonstrate the superior performance of DQN-HIDS in terms of workload, delayed sample queue, rewards, and classifier accuracy.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Song Deng, Qianliang Li, Renjie Dai, Siming Wei, Di Wu, Yi He, Xindong Wu
Summary: In this paper, we propose a Chinese text classification algorithm based on deep active learning for the power system, which addresses the challenge of specialized text classification. By applying a hierarchical confidence strategy, our model achieves higher classification accuracy with fewer labeled training data.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Kaan Deveci, Onder Guler
Summary: This study proves the lack of robustness in nonlinear IF distance functions for ranking intuitionistic fuzzy sets (IFS) and proposes an alternative ranking method based on hypervolume metric. Additionally, the suggested method is extended as a new multi-criteria decision making method called HEART, which is applied to evaluate Turkey's energy alternatives.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Fu-Wing Yu, Wai-Tung Ho, Chak-Fung Jeff Wong
Summary: This research aims to enhance the energy management in commercial building air-conditioning systems, specifically focusing on chillers. Ridge regression is found to outperform lasso and elastic net regression when optimized with the appropriate hyperparameter, making it the most suitable method for modeling the system coefficient of performance (SCOP). The key variables that strongly influence SCOP include part load ratios, the operating numbers of chillers and pumps, and the temperatures of chilled water and condenser water. Additionally, July is identified as the month with the highest potential for performance improvement. This study introduces a novel approach that balances feature selection, model accuracy, and optimal tuning of hyperparameters, highlighting the significance of a generic and simplified chiller system model in evaluating energy management opportunities for sustainable operation. The findings from this research can guide future efforts towards more energy-efficient and sustainable operations in commercial buildings.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Xiaoyan Chen, Yilin Sun, Qiuju Zhang, Xuesong Dai, Shen Tian, Yongxin Guo
Summary: In this study, a method for dynamically non-destructive grasping of thin-skinned fruits is proposed. It utilizes a multi-modal depth fusion convolutional neural network for image processing and segmentation, and combines the evaluation mechanism of optimal grasping stability and the forward-looking non-destructive grasp control algorithm. The proposed method greatly improves the comprehensive performance of grasping delicate fruits using flexible hands.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Yuxuan Yang, Siyuan Zhou, He Weng, Dongjing Wang, Xin Zhang, Dongjin Yu, Shuiguang Deng
Summary: The study proposes a novel model, POIGDE, which addresses the challenges of data sparsity and elusive motives by solving graph differential equations to capture continuous variation of users' interests. The model learns interest transference dynamics using a time-serial graph and an interval-aware attention mechanism, and applies Siamese learning to directly learn from label representations for predicting future POI visits. The model outperforms state-of-the-art models on real-world datasets, showing potential in the POI recommendation domain.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
S. Karthika, P. Rathika
Summary: The widespread development of monitoring devices in the power system has generated a large amount of power consumption data. Storing and transmitting this data has become a significant challenge. This paper proposes an adaptive data compression algorithm based on the discrete wavelet transform (DWT) for power system applications. It utilizes multi-objective particle swarm optimization (MO-PSO) to select the optimal threshold. The algorithm has been tested and outperforms other existing algorithms.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Jiaqi Guo, Haiyan Wu, Xiaolei Chen, Weiguo Lin
Summary: In this study, an adaptive SV-Borderline SMOTE-SVM algorithm is proposed to address the challenge of imbalanced data classification. The algorithm maps the data into kernel space using SVM and identifies support vectors, then generates new samples based on the neighbors of these support vectors. Extensive experiments show that this method is more effective than other approaches in imbalanced data classification.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Qiumei Zheng, Linkang Xu, Fenghua Wang, Yongqi Xu, Chao Lin, Guoqiang Zhang
Summary: This paper proposes a new semantic segmentation network model called HilbertSCNet, which combines the Hilbert curve traversal and the dual pathway idea to design a new spatial computation module to address the problem of loss of information for small targets in high-resolution images. The experiments show that the proposed network performs well in the segmentation of small targets in high-resolution maps such as drone aerial photography.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Mojtaba Ashour, Amir Mahdiyar
Summary: Analytic Hierarchy Process (AHP) is a widely applied technique in multi-criteria decision-making problems, but the sheer number of AHP methods presents challenges for scholars and practitioners in selecting the most suitable method. This paper reviews articles published between 2010 and 2023 proposing hybrid, improved, or modified AHP methods, classifies them based on their contributions, and provides a comprehensive summary table and roadmap to guide the method selection process.
APPLIED SOFT COMPUTING
(2024)
Review
Computer Science, Artificial Intelligence
Gerardo Humberto Valencia-Rivera, Maria Torcoroma Benavides-Robles, Alonso Vela Morales, Ivan Amaya, Jorge M. Cruz-Duarte, Jose Carlos Ortiz-Bayliss, Juan Gabriel Avina-Cervantes
Summary: Electric power system applications are complex optimization problems. Most literature reviews focus on studying electrical paradigms using different optimization techniques, but there is a lack of review on Metaheuristics (MHs) in these applications. Our work provides an overview of the paradigms underlying such applications and analyzes the most commonly used MHs and their search operators. We also discover a strong synergy between the Renewable Energies paradigm and other paradigms, and a significant interest in Load-Forecasting optimization problems. Based on our findings, we provide helpful recommendations for current challenges and potential research paths to support further development in this field.
APPLIED SOFT COMPUTING
(2024)