A deep learning and IoT-driven framework for real-time adaptive resource allocation and grid optimization in smart energy systems by Arvind R. Singh, M. S. Sujatha, Akshay D. Kadu, Mohit Bajaj, Hailu Kendie Addis, Kota Sarada

“…Abstract The rapid evolution of smart grids, driven by rising global energy demand and renewable energy integration, calls for intelligent, adaptive, and energy-efficient resource allocation strategies. Traditional energy management methods, based on static models or heuristic algorithms, often fail to handle real-time grid dynamics, leading to suboptimal energy distribution, high operational costs, and significant energy wastage. …”
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Toward Optimal Resource Allocation: A Multi-Agent DRL Based Task Offloading Approach in Multi-UAV-Assisted MEC Networks by Muhammad Naqqash Tariq, Jingyu Wang, Salman Raza, Mohammad Siraj, Majid Altamimi, Saifullah Memon

“…However, the growing number of UAVs and smart devices causing a major difficulty in the devising an effective scheme for the task offloading and resource allocation in multi-UAV-aided MEC networks. …”
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An Approach for Multi-Source Land Use and Land Cover Data Fusion Considering Spatial Correlations by Jing Yang, Yiheng Jiang, Qirui Song, Zheng Wang, Yang Hu, Kaiqiang Li, Yizhong Sun

“…The proposed multi-source land use data fusion method and its products can provide support and services for urban sustainable construction, resource management, and environmental monitoring and protection, demonstrating significant research value and importance.…”
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Analytical allocation of new construction methods based on future residential demand to modify residential construction efficiency plan by Majid Paziki, Mahdi Ravanshadnia

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Optimization Configuration Method of Energy Storage Considering Photovoltaic Power Consumption and Source-Load Uncertainty by Junjie Qiao, Xiaofang Meng, Weigang Zheng, Pengxue Huang, Tiefeng Xu, Yupeng Xu

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ELABORATION OF TASK CONTROL ALGORITHMS WITH PARALLEL COMPUTING IN CLUSTER COMPUTING SYSTEMS by Abdulmajed A. Al-khulaidi, Yury O. Chernyshev

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Research on intelligent computing offloading model based on reputation value in mobile edge computing by Jin QI, Hairong SUN, Kun GONG, Bin XU, Shunyi ZHANG, Yanfei SUN

“…Aiming at the problem of high-latency,high-energy-consumption,and low-reliability mobile caused by computing-intensive and delay-sensitive emerging mobile applications in the explosive growth of IoT smart mobile terminals in the mobile edge computing environment,an offload decision-making model where delay and energy consumption were comprehensively included,and a computing resource game allocation model based on reputation that took into account was proposed,then improved particle swarm algorithm and the method of Lagrange multipliers were used respectively to solve models.Simulation results show that the proposed method can meet the service requirements of emerging intelligent applications for low latency,low energy consumption and high reliability,and effectively implement the overall optimized allocation of computing offload resources.…”
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Deep reinforcement learning-based resource reservation algorithm for emergency Internet-of-things slice by Guolin SUN, Ruijie OU, Guisong LIU

“…Based on the requirements of ultra-low latency services for emergency Internet-of-things (EIoT) applications,a multi-slice network architecture for ultra-low latency emergency IoT was designed,and a general methodology framework based on resource reservation,sharing and isolation for multiple slices was proposed.In the proposed framework,real-time and automatic inter-slice resource demand prediction and allocation were realized based on deep reinforcement learning (DRL),while intra-slice user resource allocation was modeled as a shape-based 2-dimension packing problem and solved with a heuristic numerical algorithm,so that intra-slice resource customization was achieved.Simulation results show that the resource reservation-based method enable EIoT slices to explicitly reserve resources,provide a better security isolation level,and DRL could guarantee accuracy and real-time updates of resource reservations.Compared with four existing algorithms,dueling deep Q-network (DQN) performes better than the benchmarks.…”
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Designing a Multiobjective Human Resource Scheduling Model Using the Tabu Search Algorithm by Roghayeh Mirataollahi olya, Seyed Ahmad Shayannia, Mohammad Mehdi movahedi

“…In this research, considering the high importance of these issues, the problems of scheduling and allocation of manpower in a real place are solved. To this end, the metaheuristic Tabu search algorithm is used with the aim of minimizing the duration of activity and the presence of all manpower. …”
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A FPGA Accelerator of Distributed A3C Algorithm with Optimal Resource Deployment by Fen Ge, Guohui Zhang, Ziyu Li, Fang Zhou

“…In addition, the resource wastage problem caused by the distributed training characteristics of A3C algorithms and the resource allocation problem affected by the imbalance between the computational amount of inference and training need to be carefully considered when designing accelerators. …”
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Educational resources recommendation algorithm based on GMF-MLP-NeuMF prediction model by Rui Wang, Qiusheng Cui, Yezhi Xiao, Jinfeng Du, Ruicheng Yue

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Optimal EVs Charge Station Allocation Considering Residents Dispersion Using a Genetic Algorithm and Weighted K-Means by Rafael Monteagudo, Edgardo D. Castronuovo, Ramon Barber

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Optimizing spatial allocation schemes with a focus on carbon fixation services by the integration of GIS and a robust algorithmic approach by Eryan Guo, Jing He

“…The results confirm that under the constraints of forest classification management and age structure adjustment of artificial forests, different optimization scenarios gradually stabilize corresponding logging intensity and forest resources from year 40 onwards. By assigning weights to the net present value of wood and carbon sequestration in the objective function, this study explores the impact of social preferences on spatial allocation schemes for forest management. …”
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Algorithmic Approach of Majority Voting With Agents’ Inclusiveness for Facility Resource Matching by Peash Ranjan Saha, Salimur Choudhury, Kai Salomaa

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A traffic-awareness dynamic resource allocation scheme based on multi-objective optimization in multi-beam mobile satellite communication systems by YuanZhi He, YiZhen Jia, XuDong Zhong

“…Since the dynamic resource allocation problem is formulated as NP-hard, a new traffic-aware dynamic resource allocation (TADRA) algorithm is proposed. …”
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Spatially optimized allocation of water and land resources based on multi-dimensional coupling of water quantity, quality, efficiency, carbon, food, and ecology by Yingbin Wang, Haiqing Wang, Jiaxin Sun, Peng Qi, Wenguang Zhang, Guangxin Zhang

“…The COM-WL model integrates our improved genetic algorithm, PAEA-NSGAⅢ, with the landscape allocation model, GridLOpt. …”
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Research on the spatial allocation of fundamental medical facilities utilizing multi-objective optimization–a case study on Tianjin by Sheng Zhang, Yongsheng Ma, Juan Ren, Hui Liu, Lei Cui, Zizhao Fan

“…This paper developed a logical framework of “spatial accessibility-resource allocation-site optimization,” using Tianjin as a case study. …”
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Deep reinforcement learning-based resource joint optimization for millimeter-wave massive MIMO systems by LIU Qingli, LI Xiaoyu, LI Rui

“…The method was adopted in a three-stage strategy, firstly, an RF beamformer was constructed to reduce the hardware cost and total power consumption through a small number of RF chains; secondly, a baseband precoder was designed using the effective channel state information; and finally, a two-tier deep reinforcement learning architecture was designed and applied to realize dynamic discrete bandwidth and continuous power resource allocation. Experimental results show that the proposed joint optimization method significantly improves the throughput and energy efficiency of the system compared with the single-stage all-digital precoding and hybrid precoding equal resource allocation methods and the particle swarm optimization-based resource allocation algorithm.…”
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Joint Resource Allocation and Power Control in Rate-Splitting Multiple Access-Based Integrated Terrestrial and Non-Terrestrial Networks With HAP Assistance by Thong-Nhat Tran, Young Jeon, Heejung Yu, Taejoon Kim

“…The non-convex JRPS problem is reformulated as a linear program and solved using an iterative successive convex approximation algorithm that ensures local optimality. Additionally, a heuristic resource allocation and power control method is also proposed to provide an effective initialization for JRPS and to serve as a performance baseline. …”
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Deep Reinforcement Learning for Resource Constrained Multiclass Scheduling in Wireless Networks by Apostolos Avranas, Philippe Ciblat, Marios Kountouris

“…Our method can, for instance, achieve with 13% less power and bandwidth resources the same user satisfaction rate as a myopic algorithm using knapsack optimization.…”
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