5G network slicing algorithm based on prediction by Jiageng ZHANG, Min ZHU, Feng DU, Qi WANG, Jun LIU, Zhihai SUO, Li WANG

“…5G on line scenario needs short build time of network slice, it can use prediction algorithm to isolate network resource in advance to reduce the construction time of network slice.A 5G network slicing algorithm based on prediction was proposed, which used fourth square as cost function to predict the network resource requests with accepted accurate and lower complexity of algorithm.And then based on the prediction result, virtual nodes and links resource were allocated, and container was pulled up when network slice request arrives to network, the dynamic creation of network slices was completed.The simulation results show that the proposed algorithm can get 90% accurate of prediction, and reduce 50% construction time of network slice, and improve the network utilization ration，when new network slice reusing the original network slice.…”
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The Optimization of Supply–Demand Balance Dispatching and Economic Benefit Improvement in a Multi-Energy Virtual Power Plant within the Jiangxi Power Market by Tang Xinfa, Wang Jingjing, Wang Yonghua, Wan Youwei

“…The method takes into account the characteristics and uncertainties of renewable energy sources such as solar and wind energy, and incorporates advanced multi-objective optimization algorithms. …”
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Optimizing performance of WPCN based on whale optimization algorithm by Lina Yuan, Huajun Chen, Tinghong Chang, Ging Gong

“…Abstract This paper investigates the optimization issues of wireless powered communication (WPC) networks and proposes a technique that is grounded in the whale optimization algorithm (WOA) to address key optimization problems such as node deployment, energy allocation, power control, and multi-user access within the network. …”
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Resource Scheduling for Cloud Data Center Based on Data Mining in Smart Grid by Songtao Peng

“…Since the wide use of virtual technology,the resource use rate in cloud data center has been improved effectively than ever before.However,there is still a large space for improvement due to the resources usually are pre-started and pre-allocated by the user demand rather than the actual needs.In order to allocate available resource more accurately,two algorithms were proposed to meet the needs of the daily use in most of time.The available virtual resources would be arranged according the forecast using the algorithms of hierarchical composition of loading and the peek resources needs would be dynamic allocated using the algorithms of stochastic equilibrium and queuing theory.The results of experiment via the system based upon above theories show that the solution provides a kind of very effective advanced means for the optimal use of resources and energy saves.…”
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Resource Management Based on Security Satisfaction Ratio with Fairness-Aware in Two-Way Relay Networks by Jun Zhao, Zhaoming Lu, Xiangming Wen, Haijun Zhang, Shenghua He, Wenpeng Jing

“…We model the security resource management problem as a mixed integer programming problem, which is decomposed into three subproblems, distributed power allocation, distributed subchannel allocation, and distributed subchannel pairing, and then solved it in constraint particle swarm optimization (CPSO), binary CPSO (B_CPSO), and classic Hungarian algorithm (CHA) method, respectively. …”
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RFID‐Based Enhanced Resource Optimization for 5G/6G Network Applications by Stella N. Arinze, Augustine O. Nwajana

“…This algorithm dynamically managed key network resources, including spectrum allocation, load balancing, and energy consumption, ensuring efficient operation under varying network conditions. …”
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Exploration and Analysis of Resource Scheduling and Management Mode in Future Space TT＆C Network by Hong ZHANG, Jia XUE, Bo REN, Xiaojun DU, Meng XUE

“…The running mode and existing issues of current resource scheduling system operation model of TT＆amp;C network were discussed firstly.Then the development trends of future resource scheduling and management models were summarized, included integration of data transmission, automation of system operation, intelligence of algorithm allocation, coexistence of random access and pre-allocation.In the end, accorded to the management demands of integration, automation and intelligentization, the operating framework of the future resource scheduling model was put forward.After that, the key technologies which need to be broken through of the framework were researched deeply.And the further construction object of resource scheduling research was proposed in order to provided a reference for subsequent engineering applications.…”
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EdgeGuard: Decentralized Medical Resource Orchestration via Blockchain-Secured Federated Learning in IoMT Networks by Sakshi Patni, Joohyung Lee

“…EdgeGuard uses edge computing to improve system scalability and efficiency by offloading computational tasks from IoMT devices with limited resources. We have made several technological advances, including a lightweight blockchain consensus mechanism designed for IoMT networks, an adaptive edge resource allocation method based on reinforcement learning, and a federated learning algorithm optimized for medical data with differential privacy. …”
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Overhaul Scheduling Approach for Metro Vehicles Based on Genetic Algorithms by WANG Bingjing, LI Bingxin

“…In this scheduling approach, an evaluation function is applied to transform the train overhaul scheduling into the tasks to optimize the resource allocation for the overhaul process, and the resource allocation to the overhaul process is optimized leveraging the inherent parallelism and global optimization ability of the genetic algorithm, and the optimal chromosome is deemed as the final result of the overhaul scheduling. …”
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Exploring Virtual Machine Scheduling Algorithms: A Meta-Analysis by Salman Mahmood, Nor Adnan Yahaya

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Multi-Satellite Task Parallelism via Priority-Aware Decomposition and Dynamic Resource Mapping by Shangpeng Wang, Chenyuan Zhang, Zihan Su, Limin Liu, Jun Long

“…First, we introduce a graph theoretic model to represent the task dependency and priority relationships explicitly, combined with a novel algorithm for task decomposition. Meanwhile, we construct a resource allocation model based on game theory and combine it with deep reinforcement learning to achieve resource mapping in a dynamic environment. …”
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Computation task offloading algorithm and system level simulation for vehicles by Qicheng ZENG, Yuxuan SUN, Sheng ZHOU

“…With the development of autonomous driving and vehicular network,more and more vehicles will have powerful computing capabilities and connection with each other via wireless network.These computing resources can not only be applied to automatic driving,but also provide a wide range of edge computing services.Aiming at the task offloading among vehicles,a distributed task offloading algorithm based on online learning was proposed to minimize the average offloading delay.Furthermore,a system-level simulation platform was built to evaluate the impact of vehicle density and number of tasks on the average offloading delay in both highway and urban scenarios.The results provide a reference for the resource allocation and deployment of task offloading in different traffic situations.…”
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Investigating of Machine Learning Based Algorithms for Liver Cirrhosis Prediction by Young Choi, Eunsung Oh

“…Beyond medicine, the integration of machine learning algorithms in liver cirrhosis prediction could have broader socio-economic implications, including enhanced resource allocation and healthcare delivery optimization.…”
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Energy consumption optimization scheme in UAV-assisted MEC system based on optimal SIC order by Wei JI, Xuxin YANG, Fei LI, Ting LI, Yan LIANG, Yunchao SONG

“…In uplink non-orthogonal multiple access (NOMA)-based unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system, the successive interference cancellation (SIC) order of NOMA became a bottleneck limiting the transmission performance of task offload in uplink link.To reduce the energy consumption of the system, the SIC order was discussed and the optimal SIC order based on channel gain and task delay constraint was proposed.The optimization problem of minimizing the system energy consumption was proposed based on the optimal SIC order while satisfying the constraints of the given task delay of the device, the maximum transmit power constraint of the device, and the UAV trajectory.Since the problem was a complex non-convex problem, an alternating optimization method was adopted to solve the optimization problem to achieve power allocation and UAV trajectory optimization.A low-complexity algorithm based on matching theory was proposed to obtain the optimal device grouping in different time slots.Simulation results show that the optimal SIC order can realize smaller system energy consumption under the same task delay constraint compared with other SIC order, the proposed low-complexity device grouping algorithm can obtain the optimal device grouping.…”
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Energy consumption optimization scheme in UAV-assisted MEC system based on optimal SIC order by Wei JI, Xuxin YANG, Fei LI, Ting LI, Yan LIANG, Yunchao SONG

“…In uplink non-orthogonal multiple access (NOMA)-based unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system, the successive interference cancellation (SIC) order of NOMA became a bottleneck limiting the transmission performance of task offload in uplink link.To reduce the energy consumption of the system, the SIC order was discussed and the optimal SIC order based on channel gain and task delay constraint was proposed.The optimization problem of minimizing the system energy consumption was proposed based on the optimal SIC order while satisfying the constraints of the given task delay of the device, the maximum transmit power constraint of the device, and the UAV trajectory.Since the problem was a complex non-convex problem, an alternating optimization method was adopted to solve the optimization problem to achieve power allocation and UAV trajectory optimization.A low-complexity algorithm based on matching theory was proposed to obtain the optimal device grouping in different time slots.Simulation results show that the optimal SIC order can realize smaller system energy consumption under the same task delay constraint compared with other SIC order, the proposed low-complexity device grouping algorithm can obtain the optimal device grouping.…”
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An Integrated Optimization Method for Resource-Constrained Schedule Compression Under Uncertainty in Construction Projects by Firas Takleef, Omar Ayadi, Faouzi Masmoudi

“…An integrated solution that considers the shortening of scheduling and the planning of resource integration was conceived. The proposed method allocates the resources and the execution mode costs effectively in order to minimize the project duration and the cost of construction activities. …”
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(IoT) Network intrusion detection system using optimization algorithms by Luo Shan

“…In multi-class attack identification scenarios, the framework mitigates feature confusion between rare attacks (e.g., user-to-root attacks) and normal traffic through adaptive feature weight allocation. This study further validates the potential of swarm intelligence algorithms in the field of IoT security, offering a novel methodological foundation for efficient threat detection in resource-constrained environments.…”
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Application of the Random Forest Algorithm for Accurate Bipolar Disorder Classification by Miguel Suárez, Ana M. Torres, Pilar Blasco-Segura, Jorge Mateo

“…Its ability to reduce diagnostic delays, improve classification accuracy, and optimize resource allocation make it a promising tool for integrating artificial intelligence into psychiatric care. …”
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From algorithms to negotiations: Why health diplomacy must adapt. by Brian Li Han Wong, Garry Aslanyan, Warisa Panichkriangkrai, Ricardo Baptista Leite, Jemilah Mahmood, Anders Nordström

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Random Regret-Minimization Model for Emergency Resource Preallocation at Freeway Accident Black Spots by Siliang Luan, Qingfang Yang, Wei Wang, Zhongtai Jiang, Ruru Xing, Ruijuan Chu

“…At the same time, we propose a three-stage random regret-minimization (RRM) model to minimize the regret value of the attribute of overall response time, cost, and demand, which allocates limited emergency resources to more likely to happen accident spots. …”
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