Efficient Task Scheduling and Load Balancing in Fog Computing for Crucial Healthcare Through Deep Reinforcement Learning by Prashanth Choppara, Bommareddy Lokesh

“…In such simulations, diverse healthcare scenarios, ranging from continuous patient monitoring systems to crucial emergency response applications, are recreated, providing a rich framework for testing the real-time processing capabilities of the model. This algorithm, DRL, has been fine-tuned and extensively implemented in these scenarios to show how the algorithm controls and optimizes tasks and their urgency in accordance with resource demand. …”
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Natural gas bi-level demand response strategies considering incentives and complexities under dynamic pricing by Huibin Zeng, Jie Zhou, Hongbin Dai

“…The upper-level model uses dynamic gas pricing to guide users in adjusting consumption, thereby reducing peaks, filling valleys, and optimizing resource allocation. The lower-level model considers factors like weather and heating, creating incentives to boost user participation and flexibility. …”
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Visual analytics of urban epidemic situation development and dynamic regulation by Song WANG, Shijie CHEN, Hanglin LI, Xiaohui LI, Qiongfang FENG, Huijie WANG

“…In order to solve the reasonable allocation of limited medical resources for patients, a visual platform for dynamic control of cabin hospital was built based on the dataset of the COVID-19 epidemic in Wuhan cabin hospital, which integrates multi-source data such as public opinion, space-time trajectory and science popularization.The droppingwater diagram was introduced to dynamically monitor the overall situation of the cabin hospital.The theme model and sentiment dictionary were used to extract the emotional features of the masses, the WordStream were used to visually present the trend of urban public opinion.The path planning algorithm based on hospital load capacity were built for treatment route, the population-oriented science information of access to cabin hospital and the urban recovery model can enhance anti-epidemic confidence.The system is helpful to realize the reasonable allocation of human and material resources, timely guide the emotional trend of the masses, pay attention to the change of public opinion after the release of new polices/decisions, and slow down the phenomenon of patients gathering.The function and effectiveness of the system was verified by the analysis of several cases.…”
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Kneeliverse: A universal knee-detection library for performance curves by Mário Antunes, Tyler Estro, Pranav Bhandari, Anshul Gandhi, Geoff Kuenning, Yifei Liu, Carl Waldspurger, Avani Wildani, Erez Zadok

“…These points represent optimal trade-offs between cost and performance, facilitating efficient decision-making and resource allocation. However, accurately determining the knees and elbows in curves poses a significant challenge. …”
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A Distributed Low-Degree-of-Freedom Aerial Target Localization Method Based on Hybrid Measurements by Xiaoshuang Jiao, Jinming Chen, Lifeng Jiang, Weiping Li, Xiaochao Yang, Weiwei Wang, Jun Zhang

“…Simulation results validate the significant advantages of the proposed algorithm in enhancing localization accuracy, reducing system costs, and optimizing resource allocation. …”
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Tasmanian devil whale optimization (TDWO) is introduced for secure video transmission in 5G networks. by Fengren Lin, Minrong Lu

“…The experimental results prove that the TDWO resource allocation algorithm yields significant experimental outcomes, with throughput, bit error rate (BER), QoE and fitness values of 25.557 Mbps, 0.021, 18.332 and 0.013, respectively.…”
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A Wind Power Density Forecasting Model Based on RF-DBO-VMD Feature Selection and BiGRU Optimized by the Attention Mechanism by Bixiong Luo, Peng Zuo, Lijun Zhu, Wei Hua

“…Accurate prediction of wind power density (WPD) holds significant practical importance for wind farms, grid operators, and the entire wind power industry, as it facilitates informed decision-making, optimized resource allocation, and enhanced system performance. …”
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Fault aware task scheduling in cloud using min-min and DBSCAN by S.M.F D Syed Mustapha, Punit Gupta

“…The proposed algorithm considers high execution enabled powerful data centers with least fault probability during resource allocation which reduces the probability of fault and increases the tolerance. …”
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Distributed congestion control strategy using network utility maximization theory in VANET by Guozhen TAN, Guodong HAN, Fuxin ZHANG, Nan DING, Mingjian LIU

“…Cooperative vehicle safety system (CVSS) rely on periodical beacons to track neighboring vehicles.High traffic density often causes channel congestion,seriously damaging the performance of CVSS.Existing congestion control strategies aim to ensure the performance in network layer,without considering the service requirements of vehicles in different driving contexts.To solve the problem,a distributed congestion control strategy using network utility maximization (NUM) theory was proposed.First of all,the NUM model for channel resource allocation was introduced.A utility function reflecting vehicle’s safety requirements was proposed in the model.Then under the condition of fixed transmit powers,a optimization problem of channel resource allocation was proposed.Lastly,to solve the optimization problem,a distributed congestion control algorithm named utility-based rate congestion control (UBRCC) algorithm was designed,the algorithm worked out the optimal beaconing rate by updating vehicle’s congestion price,realizing the resource allocation according to vehicle’s safety requirements.Simulation results validate that UBRCC algorithm can efficiently control channel congestion,reduce transmission delay,ensure reliable data transmission and satisfies the requirements of safety applications.…”
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An Optimal Transmission Strategy for Joint Wireless Information and Energy Transfer in MIMO Relay Channels by Dingcheng Yang, Xiaoxiao Zhou, Lin Xiao

“…It is focused on the precoder design and resource allocation strategies for the system to allocate the resources among the nodes in decode-and-forward (DF) mode. …”
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Multi-Stage Data-Driven Framework for Customer Journey Optimization and Operational Resilience by Tzu-Chien Wang, Ruey-Shan Guo, Chialin Chen, Chia-Kai Li

“…To address these limitations, this study proposes a multi-stage data-driven framework integrating latent Dirichlet allocation (LDA) for behavioral insights, deep learning for predictive modeling, and heuristic algorithms for adaptive decision-making. …”
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An interpretable machine learning model to predict hospitalizations by Hagar Elbatanouny, Hissam Tawfik, Tarek Khater, Anatoliy Gorbenko

“…Leveraging a comprehensive dataset sourced from the Mexican government, various supervised learning algorithms including Random Forest, Gradient Boosting, Support Vector Machine, K-Nearest Neighbors, and Multilayer Perceptron are trained and evaluated to discern factors contributing to hospitalizations. …”
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Optimal Operation Method for Microgrid with Wind/PV/Diesel Generator/Battery and Desalination by Qingfeng Tang, Nian Liu, Jianhua Zhang

“…The power supply mode of island microgrid with a variety of complementary energy resources is one of the most effective ways to solve the problem of future island power supply. …”
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Content-based dynamic superframe adaptation for Internet of Medical Things by Yousaf Zia, Arshad Farhad, Faisal Bashir, Kashif Naseer Qureshi, Ghufran Ahmed

“…This article presents a content-based dynamic superframe adaptation algorithm for the low-powered Internet of Medical Things devices to address the resource utilization challenges. …”
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Optimization Method for Network Awareness in Different Domains of LEO Communication Constellation by DI Hang, DONG Tao, LIU Zhihui, WEI Shuotong, ZHANG Qiwei, ZHANG Dingyun

“…The heterogeneous and time-varying characteristics of network resources in LEO communication constellation, as well as the diversities of service requirements, make it difficult to allocate network resources. …”
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Secure data offloading strategy for multi-UAV wireless networks based on minimum energy consumption by Gaofeng CUI, Yuanyuan XU, Shanghong ZHANG, Weidong WANG

“…To solve the problems of ground passive eavesdropping when ground users offload data to the multi-UAV(unmanned aerial vehicle) edge computing network, a secure data offloading strategy that minimized system energy consumption by jointly optimizing user matching and resource allocation was proposed.Considering the constraints of system delay, communication resources and computing resources, the probability of security interruption was used to restrict the security performance of the data offload process.By using block coordinate descent and successive convex approximation algorithm, the user transmission power, offload factor, UAV computing resource allocation and jamming power were jointly optimized.A pair-wise stable user matching algorithm was proposed to minimize the total energy consumption of UAV system.Simulation results demonstrate that the algorithm can realize the safe offloading of data, and has good performance in energy consumption and delay by comparing with the conventional strategies.…”
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Secure data offloading strategy for multi-UAV wireless networks based on minimum energy consumption by Gaofeng CUI, Yuanyuan XU, Shanghong ZHANG, Weidong WANG

“…To solve the problems of ground passive eavesdropping when ground users offload data to the multi-UAV(unmanned aerial vehicle) edge computing network, a secure data offloading strategy that minimized system energy consumption by jointly optimizing user matching and resource allocation was proposed.Considering the constraints of system delay, communication resources and computing resources, the probability of security interruption was used to restrict the security performance of the data offload process.By using block coordinate descent and successive convex approximation algorithm, the user transmission power, offload factor, UAV computing resource allocation and jamming power were jointly optimized.A pair-wise stable user matching algorithm was proposed to minimize the total energy consumption of UAV system.Simulation results demonstrate that the algorithm can realize the safe offloading of data, and has good performance in energy consumption and delay by comparing with the conventional strategies.…”
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Research on multi-attribute controller for virtual data domain based on software definition network by Jianmin Wang, Bo Zhang, Tongrang Fan, Zijian Fu, Wenbin Zhao

“…Simulation results show that compared with the shortest path mapping and greedy resource mapping algorithms, the virtual domain established by the network resource overhead–allocation algorithm can improve the resource utilization rate by 10% and reduce the intra-domain communication delay by 30%. …”
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User-centric energy efficiency fairness in backscatter-assisted wireless powered communication network by Yinghui YE, Liqin SHI, Guangyue LU

“…In order to address the unfair user-centric energy efficiency (EE) problem caused by channel difference in the backscatter-assisted wireless powered communication network,a resource allocation scheme was proposed.Firstly,a mixed integer nonconvex fractional programming problem was formulated to maximize the minimum user-centric EE,subject to the quality of service and energy-causality constraints.Based on the generalized fractional programming theory,the original problem was transformed into a mixed integer nonconvex subtraction problem.With the aid of the slack variable,the proof by contradiction,the auxiliary variable and the mixed integer nonconvex subtraction problem were further transformed into an equivalent convex problem.Finally,an iterative algorithm was proposed to obtain the optimal solutions.Computer simulations validated the quick convergence of the proposed iterative algorithm,and that the developed resource allocation scheme efficiently guarantees the fairness among users in terms of EE.…”
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Scheduling Jobs with Variable Job Processing Times on Unrelated Parallel Machines by Guang-Qian Zhang, Jian-Jun Wang, Ya-Jing Liu

“…m unrelated parallel machines scheduling problems with variable job processing times are considered, where the processing time of a job is a function of its position in a sequence, its starting time, and its resource allocation. The objective is to determine the optimal resource allocation and the optimal schedule to minimize a total cost function that dependents on the total completion (waiting) time, the total machine load, the total absolute differences in completion (waiting) times on all machines, and total resource cost. …”
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