Vehicular-Computational Resource Geofencing: Efficient Spatiotemporal Uncertainty Estimation by Ghada Afifi, Bassem Mokhtar

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Edge computing smart grid resource scheduling algorithm based on reinforcement learning by Jinghang YU, Yichen ZHAO, Hu SONG

“…A smart grid is a power network capable of intelligent management and optimization.Network virtualization technology can effectively improve the resource utilization and reliability of smart grids and meet the differentiated needs of different users.In the case of limited resources, traditional virtual network embedding algorithms cannot dynamically adjust the allocation and mapping of virtual resources according to the resource usage and user needs of the power system.To solve this problem, edge computing and virtualization technology was combined and a virtual network resource scheduling algorithm based on reinforcement learning was introduced.The simulation results show that the proposed virtual resource scheduling algorithm is better than the other three scheduling algorithms in improving power grid reliability and resource utilization.…”
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Hybrid Models for Forecasting Allocative Localization Error in Wireless Sensor Networks by Guo Li, Hongyu Sheng

“…This study presents a machine learning-based approach to forecast Allocative Localization Error (ALE) in Wireless Sensor Networks (WSNs), addressing challenges such as dynamic network topologies and resource constraints. …”
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Spectrum-efficient user grouping and resource allocation based on deep reinforcement learning for mmWave massive MIMO-NOMA systems by Minghao Wang, Xin Liu, Fang Wang, Yang Liu, Tianshuang Qiu, Minglu Jin

“…Moreover, a deep deterministic policy gradient (DDPG)-based power resource allocation algorithm is designed to avoid the performance loss caused by power quantization and improve the system’s achievable sum-rate. …”
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Collaborative Sensing-Aware Task Offloading and Resource Allocation for Integrated Sensing-Communication- and Computation-Enabled Internet of Vehicles (IoV) by Bangzhen Huang, Xuwei Fan, Shaolong Zheng, Ning Chen, Yifeng Zhao, Lianfen Huang, Zhibin Gao, Han-Chieh Chao

“…To solve this mixed-integer nonlinear programming (MINLP) problem, we design a two-stage iterative optimization algorithm that decomposes the original optimization problem into a task offloading subproblem and a resource allocation subproblem, which are solved iteratively. …”
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Joint Radio Resource Allocation and Base Station Location Selection in OFDMA Based Private Wireless Access Networks for Smart Grid by Peng Du, Yuan Zhang

“…Specifically, the combination of power control based resource allocation algorithm and PSO based location selection algorithm is recommended.…”
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Resource Allocation in an Underwater Communication Network: The Stackelberg Game Power Control Method Based on a Non-Uniform Pricing Mechanism by Xiangjie Luo, Hui Wang

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Satellite- and Cache-Assisted UAV: A Joint Cache Placement, Resource Allocation, and Trajectory Optimization for 6G Aerial Networks by Dinh-Hieu Tran, Symeon Chatzinotas, Bjorn Ottersten

“…In this context, we aim to maximize the minimum achievable throughput per ground user (GU) by jointly optimizing cache placement, the UAV’s resource allocation, and trajectory while cache capacity and flight time are limited. …”
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Joint Optimization of Resource Allocation and Task Offloading Strategies in Multi-Cell Dynamic MEC Systems Using Multi-Agent DRL by Yuntao Hu, Ming Chen, Haowen Sun, Yinlu Wang, Yihan Cang

“…The energy consumption minimization is achieved by jointly optimizing the task offloading proportions, transmit power allocations, and computational resource distributions while ensuring the overall deadline constraints and the minimum processing size requirements in each scheduling cycle. …”
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Efficient resource allocation for D2D-enabled social IoT networks: A tripartite and time-scale optimization approach by Saurabh Chandra, Rajeev Arya, Maheshwari Prasad Singh

“…The problem is solved in two stages: a tripartite graph-based resource allocation stage and a time-scale optimization stage. …”
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DCOOL: Dynamic computation offloading and resource allocation for LEO satellite-assisted edge computing in a ground-space integrated framework by Jeonghwan Kim, Jeongho Kwak

“…To this end, this paper introduces a dynamic computation offloading and resource allocation framework in the LEO satellite MEC architecture. …”
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A Multi-Objective Dynamic Resource Allocation Model for Search and Rescue and First Aid Tasks in Disaster Response by Employing Volunteers by Emine Nisa Kapukaya, Sule Itir Satoglu

“…Pre-disaster planning and post-disaster dynamic resource allocation including material and human resources is essential. …”
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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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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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DROVE: An Algorithm for Spatial and Temporal Disaggregation of On-road Vehicle Emission Inventories by Carlos M. González, Carlos D. Gómez, Beatriz H. Aristizábal

“…Hence, exercises for estimating atmospheric pollutant emissions are developed only in a macro-scale way, without allocating in space and time the emissions. In this study, we present a new computational algorithm named DROVE, for the disaggregation of on-road vehicle emissions in space and time. …”
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Multi-domain optical network virtual technology based on SDN by Shidong LIU, Pan WANG

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Edge-LLM Inference With Cost-Aware Layer Allocation and Adaptive Scheduling by Sama Habibi, Ozgur Ercetin

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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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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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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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