Resource Allocation In Systems Research Articles

Deep Learning Method for Real-Time Fire Detection System for Urban Fire Monitoring and Control

During urban fire incidents, real-time videos and images are vital for emergency responders and decision-makers, facilitating efficient decision-making and resource allocation in smart city fire monitoring systems. However, real-time videos and images require simple and embeddable models in small computer systems with highly accurate fire detection ratios. YOLOv5s has a relatively small model size and fast processing time with limited accuracy. The aim of this study is to propose a method that employs a YOLOv5s network with a squeeze-and-excitation module for image filtering and classification to meet the urgent need for rapid and accurate real-time screening of irrelevant data. In this study, over 3000 internet images were used for crawling and annotating to construct a dataset. Furthermore, the YOLOv5, YOLOv5x and YOLOv5s models were developed to train and test the dataset. Comparative analysis revealed that the proposed YOLOv5s model achieved 98.2% accuracy, 92.5% recall, and 95.4% average accuracy, with a remarkable processing speed of 0.009 s per image and 0.19 s for a 35 frames-per-second video. This surpasses the performance of other models, demonstrating the efficacy of the proposed YOLOv5s for real-time screening and classification in smart city fire monitoring systems.

Research on the coupling measurement of medical education and health resource allocation under the background of healthy China

Objective To analyze the coupling and coordination level of medical education and health resource allocation in China, and to provide scientific basis for promoting the high-quality development of medical education and the efficient allocation of health resources.MethodsBased on the panel data from 2011 to 2021, the coupling coordination degree model was used to measure the coupling coordination index of medical education and health resources in China. The spatial auto-correlation model was used to analyze the development status and distribution characteristics of the coupling coordination degree of the two systems. The kernel density estimation method was used to analyze the dynamic evolution trend of the coupling coordination of the two systems. The QR quantile regression model was used to explore the key factors affecting the coupling coordination degree of the two systems.ResultsDuring the observation period, the coupling coordination degree of the two systems increased from 0.393 to 0.465, with a growth rate of 18.3%. The coupling coordination degree between regions gradually decreased in the eastern-central and eastern-western regions, and there were still large differences between the central and western regions. The coupling coordination degree of the two systems in the region was significantly different in the eastern and western regions, and the central region was relatively similar. There is a positive spatial correlation between the provinces, and 25.81% of the provinces have transitions. Finally, the number of points in the first and third quadrants is higher than that in the second and fourth quadrants. In the process of dynamic distribution, the degree of polarization of the coupling coordination degree curve of the two systems is gradually weakened. Per capita GDP, residents ' income difference and population size are the positive and significant factors driving the coupling and coordinated development of the two systems.ConclusionThe coupling and coordination degree of the two systems of medical education and health resource allocation showed a stable upward trend during the observation period, and the global spatial positive correlation also gradually increased, showing the spatial agglomeration characteristics of ' high-high agglomeration ' and ' low-low agglomeration '. The spatial difference of coupling coordination degree shows a shrinking trend and develops towards equalization. The coupling coordination degree of the two systems is affected by social, economic and demographic factors to varying degrees. Therefore, it is necessary to innovate the coordinated development mechanism of the two systems, promote the two-way flow of medical education and health resource allocation in talents, technology and other elements, and then promote the coupling and coordinated development of the two systems.

Effects of climate change and deep fertilization on the growth and yield of winter wheat in the Loess Plateau of China.

Global temperature is projected to rise continuously under climate change, negatively impacting the growth and yield of winter wheat. Optimizing traditional agricultural measures is necessary to mitigate potential winter wheat yield losses caused by future climate change. This study aims to explore the variations in winter wheat growth and yield on the Loess Plateau of China under future climate change, identify the key meteorological factors affecting winter wheat growth and yield, and analyze the differences in winter wheat yield and root characteristics under different fertilization depths. Meteorological data from 20 General Circulation Models were applied to drive the Decision Support System for Agrotechnology Transfer model, simulating the future growth characteristics of winter wheat under various fertilization depths. The Random Forest model was used to determine the relative importance of meteorological factors influencing winter wheat yield, root length density and leaf area index. The results showed that temperature and high emission concentration were primary factors influencing crop yield under future climate change. The temperature increase projected from 2021 to 2100 would be anticipated to shorten the phenology period of winter wheat by 2-16 days and reduce grain yield by 2.9-12.7% compared to the period from 1981 to 2020. Conversely, the root length density and root weight of winter wheat would increase by 1.2-10.9% and 0.2-24.1%, respectively, in the future, and excessive allocation of root system resources was identified as a key factor contributing to the reduction in winter wheat yield. Compared with the shallow fertilization treatment (N5), the deep fertilization treatments (N15 and N25) increased the proportion of roots in the deep soil layer (30-60 cm) by 2.7-10.2%. Because of the improvement in root structure, the decline in winter wheat yield under deep fertilization treatments in the future is expected to be reduced by 1.2% to 6.5%, whereas water use efficiency increases by 1.1% to 2.4% compared to the shallow fertilization treatment. The deep fertilization treatment can enhance the root structure of winter wheat and increase the proportion of roots in the deep soil layer, thereby effectively mitigating the decline in winter wheat yield under future climate change. Overall, optimizing fertilization depth effectively addresses the reduced winter wheat yield risks and agricultural production challenges under future climate change. © 2024 Society of Chemical Industry.

Development of hybrid sewing training-based dingo optimization algorithm for massive MIMO spectral efficiency maximization system

ABSTRACT A Base Station (BS) with several antenna elements serves a number of users in the frequency of the same time- slot in a huge multiuser “Multiple-Input Multiple-Output (MIMO)”. It has emerged as a viable method for the future generation of wireless networks due to its capacity to enhance both spectrum and efficiency of energy, even with straightforward linear processing like “Maximum Ratio Combining (MRC), Linear Minimum Mean Square Error (LMMSE) and Zero Forcing (ZF)”. In a power allocation scheduling method, only users who do not interfere with one another can change their power at any one time. These studies all take into account orthogonal channels. However, the “Spectral Efficiency (SE)” is reduced by the channels’ non-orthogonality. This encourages us to research how to maximize spectral efficiency in “Massive MIMO (M-MIMO)” systems by allocating power for both uplink and downlink transmission. Here, the Hybrid of Sewing Training and Dingo Optimization (HSTDO) is developed for executing the best resource allocation in the M-MIMO system to improve SE performance which helps to enhance the SE of the M-MIMO system. The simulation is carried out to compare the developed model with the conventional works to reveal the better spectral efficiency of the suggested algorithm.

Optimization Theory-Based Deep Reinforcement Learning for Resource Allocation in Ultra-Reliable Wireless Networked Control Systems

Optimization Theory-Based Deep Reinforcement Learning for Resource Allocation in Ultra-Reliable Wireless Networked Control Systems

Virtual power plant optimization with demand response and multi-complementary energy planning

Abstract To effectively address the increasing complexity of system operation optimization and resource allocation caused by the continuous enrichment of supply-side resources and the flexible variability of demand-side loads in the energy system, a robust optimization model for virtual power plants considering demand response and multi-energy complementarity is proposed. Handling renewable energy and load uncertainty in virtual power plants by building a two-stage robust optimisation model. The introduction of robust coefficients allows for flexible adjustment of the conservatism of the optimized scheduling. Results indicate that considering demand response and multi-energy complementarity can effectively enhance the system’s robustness and flexibility.

Dynamic RIS partitioning in NOMA systems using deep reinforcement learning

The rapid evolution of wireless communication technologies necessitates innovative solutions to meet the increasing performance requirements of future networks, particularly in terms of spectral efficiency, energy efficiency, and computational efficiency. Reconfigurable Intelligent Surfaces (RIS) and Non-Orthogonal Multiple Access (NOMA) are emerging as promising technologies to enhance wireless communication systems. This paper explores the dynamic partitioning of RIS elements in NOMA systems using Deep Reinforcement Learning (DRL) to optimize resource allocation and overall system performance. We propose a novel DRL-based framework that dynamically adjusts the partitioning of RIS elements to maximize the achievable sum rate and ensure fair resource distribution among users. Our architecture leverages the flexibility of RIS to create an intelligent radio environment, while NOMA enhances spectral efficiency. The DRL model is trained online, adapting to real-time changes in the communication environment. Empirical results demonstrate that our approach closely approximates the performance of the optimal iterative algorithm (exhaustive search) while reducing computational time by up to 90 percent. Furthermore, our method eliminates the need for an offline training phase, providing a significant advantage in dynamic environments by removing the requirement for retraining with every environmental change. These findings highlight the potential of DRL-based dynamic partitioning as a viable solution for optimizing RIS-aided NOMA systems in future wireless networks.

Two-Tier Efficient QoE Optimization for Partitioning and Resource Allocation in UAV-Assisted MEC.

Unmanned aerial vehicles (UAVs) have increasingly become integral to multi-access edge computing (MEC) due to their flexibility and cost-effectiveness, especially in the B5G and 6G eras. This paper aims to enhance the quality of experience (QoE) in large-scale UAV-MEC networks by minimizing the shrinkage ratio through optimal decision-making in computation mode selection for each user device (UD), UAV flight trajectory, bandwidth allocation, and computing resource allocation at edge servers. However, the interdependencies among UAV trajectory, binary task offloading mode, and computing/network resource allocation across numerous IoT nodes pose significant challenges. To address these challenges, we formulate the shrinkage ratio minimization problem as a mixed-integer nonlinear programming (MINLP) problem and propose a two-tier optimization strategy. To reduce the scale of the optimization problem, we first design a low-complexity UAV partition coverage algorithm based on the Welzl method and determine the UAV flight trajectory by solving a traveling salesman problem (TSP). Subsequently, we develop a coordinate descent (CD)-based method and an alternating direction method of multipliers (ADMM)-based method for network bandwidth and computing resource allocation in the MEC system. Extensive simulations demonstrate that the CD-based method is simple to implement and highly efficient in large-scale UAV-MEC networks, reducing the time complexity by three orders of magnitude compared to convex optimization methods. Meanwhile, the ADMM-based joint optimization method achieves approximately an 8% reduction in shrinkage ratio optimization compared to baseline methods.

Modeling defensive resource allocation in multilayered systems under probabilistic and strategic risks.

Confronting the continuing risk of an attack, security systems have adopted target-hardening strategies through the allocation of security measures. Most previous work on defensive resource allocation considers the security system as a monolithic architecture. However, systems such as schools are typically characterized by multiple layers, where each layer is interconnected to help prevent single points of failure. In this paper, we study the defensive resource allocation problem in a multilayered system. We develop two new resource allocation models accounting for probabilistic and strategic risks, and provide analytical solutions and illustrative examples. We use real data for school shootings to illustrate the performance of the models, where the optimal investment strategies and sensitivity analysis are presented. We show that the defender would invest more in defending outer layers over inner layers in the face of probabilistic risks. While countering strategic risks, the defender would split resources in each layer to make the attacker feel indifferent between any individual layer. This paper provides new insights on resource allocation in layered systems to better enhance the overall security of thesystem.

Analyze the Stochastic Solid Fuzzy Transportation Problem with Mixed Constraints through Weibull Distribution

This paper proposed a general formulation of the stochastic solid transportation problem (SSTP) with mixed constraints such as supply, demand and conveyance capacity taken as uncertain under stochastic environment, following the Weibull distribution (WD). The aim of this study is to minimize the transportation cost includes probabilistic constraints have inequalities of stochastic solid transportation problem (SSTP). SSTP with probabilistic constraints is represented as a chance constrained programming problem. Obtain alpha cut representation from cost coefficient of the fuzzy objective function. We have developed four models for stochastic solid transportation problem. The suggested models are demonstrated by taken as numerical example. A sensitivity analysis is performed to understand parameter’s sensitivity in the proposed model. Introduction: In system of transportation, goods are moved from various sources to destinations using different vehicles and organizational systems, involving both technology and human efforts. Efficient resource allocation in transportation system is crucial for industries and imprecision from factors like fluctuating demand, unreliable supply chains and unpredictable traffic. To address these complexities, advanced mathematical models are needed to manage stochasticity, fuzziness and mixed constraints. The study explores the stochastic solid fuzzy transportation problem with mixed constraint by utilizing the Weibull distribution to model uncertainties inherent in transportation systems. This research addresses the complexity introduced by stochastic variables and fuzzy parameters, particularly in situations where demand, supply and cost of transportation are not deterministic. Objectives: The aim of this study is to minimize the cost of transportation includes probabilistic constraints have inequalities of stochastic solid transportation problem (SSTP). Methods: Obtain alpha cut representation form the cost coefficient of the fuzzy objective function and four models are developed for stochastic solid transportation problem. These models are demonstrating by using a numerical example and a sensitivity analysis is conducted to understand the sensitivity of the parameters in the propose model. Results: Obtained optimal solutions for developed four models of SFSTPMC and sensitivity analysis shows that cost of transportation and flow of unit are sensitive to change in probabilities of demand. Improve transportation system by understanding sensitivity patterns that help decision maker choose appropriate supply availability probabilities. Conclusions: This study presented an approach for solving the SFSTPMC using the Weibull distribution for probabilistic constraints and fuzzy objective functions for transportation cost. Developed and optimized four models, focusing on stochastic parameters. Sensitivity analysis demonstrated the impact of these parameters on transportation cost and unit flow. The results validate the model’s effectiveness in practical resource allocation and decision making under uncertainty.

Enhanced Wireless Communication Optimization with Neural Networks, Proximal Policy Optimization and Edge Computing for Latency and Energy Efficiency

This research proposes a novel approach for efficient resource allocation in wireless communication systems. It combines dynamic neural networks, Proximal Policy Optimization (PPO), and Edge Computing Orchestrator (ECO) for latency-aware and energy-efficient resource allocation. The proposed system integrates multiple components, including a dynamic neural network, PPO, ECO, and a Mobile Edge Computing (MEC) server. The experimental methodology involves utilizing the NS-3 simulation platform to assess latency and energy efficiency in resource allocation within a wireless communication network, incorporating an ECO, MEC server, and dynamic task scheduling algorithms. It demonstrates a holistic and adaptable approach to resource allocation in dynamic environments, showcasing a notable reduction in latency for devices and tasks. Latency values range from 5 to 20 milliseconds, with corresponding resource utilization percentages varying between 80% and 95%. Additionally, energy-efficient resource allocation demonstrates a commendable reduction in energy consumption, with measured values ranging from 10 to 30 watts, coupled with efficient resource usage percentages ranging from 70% to 85%. These outcomes validate the efficacy of achieving both latency-aware and energy-efficient resource allocation for enhanced wireless communication systems. The proposed system has broad applications in healthcare, smart cities, IoT, real-time analytics, autonomous vehicles, and augmented reality, offering a valuable solution to optimize energy consumption, reduce latency, and enhance system efficiency in these industries.

Adaptive firefly algorithm for resource allocation and modified advanced encryption standard algorithm for hypervisor attack detection on cloud computing

Introduction: The advantages and wide-ranging applications of cloud computing have made it a prominent attention for scholars these days. The dispersed structure of cloud and its whole dependence on the internet for service delivery extant security problems. Methods: Hypervisor attack detection is carried out in this study using Modified Advanced Encryption Standard (MAES) system which ensures security prominently. It finds and detects the attacks earlier for secured VM migration. The proposed system includes main phases including system framework, load balancing, resource allocation and hypervisor attack detection via MAES algorithm. Initially, Over the duration of cloud computing, consider the quantity of tasks, VM, and cloud users. In this research, the MMH system is employed for load balancing to balance the total burden across the cloud. Tasks are moved from overloaded to underloaded nodes to attain load balancing. Next, the allocation of resources is carried out utilizing Adaptive Firefly (AF) optimization system which is used to select best resources optimally. It generates the best fitness values to choose the best resources. Results: It is also focused to improve the cost metric, computational complexity, throughput and VM performance in cloud. Then, to detect hypervisor attacks, the MAES method is employed. It specializes on offering enhanced security for cloud data and is employed to identify hypervisor and VM attackers. Conclusion: The findings produced the conclusion that the suggested MAES method superior to the current approaches according to throughput, computation cost, Mean Square Error (MSE) rate, and energy use.

Long-Term Solar Power Generation Forecasting at Eastern West Large Scale Solar (LSS) Farm using Random Forest Regression (RFR) Method

Precise forecasting of power generation and demand is essential for effective resource allocation and energy trading in contemporary energy systems. Power forecasting accuracy has increased dramatically since Random Forest Regression (RFR) techniques were used. The study's primary objective is to forecast electricity generation in Malaysia's Eastern West region, with a concentration on solar energy. The research process entails gathering and examining pertinent factors, weather information, and historical power data. To evaluate the accuracy and predictive potential of RFR models, a specific power grid is used for training, validation, and testing. One of the anticipated results is the creation of an accurate model for power generation predictions, which will help to optimise energy operations and smoothly incorporate renewable sources. The paper examines the advantages, disadvantages, and best practices related to RFR-based power forecasting. The dataset, which spans the years 2019 to 2023, includes 30-minute interval records for the following variables: average output power, ambient temperature, PV module temperature, global horizontal irradiance, and wind speed. Using the RandomForestRegressor class from the scikit-learn library, the RFR model is implemented. In order to assess the model's overall fit, average deviation, and sensitivity to outliers, measures such as root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE) are used on the test set. The temperature, irradiance, and AC power output of PV modules are found to be strongly correlated.

Joint optimal allocation of regional water and land resources considering their mutual feed relationship

Water and land resources are indispensable prerequisites for the sustenance and advancement of human civilization. In recent decades, China has faced significant challenges in managing its water and land resources due to the intensifying competition resulting from rapid urbanization, industrialization, and population growth. The joint optimal allocation of water and land resources can effectively address these issues. However, there have been limited accomplishments in investigating the integrated water and land resources allocation system’s interaction with social, economic, and environmental development. This paper develops a novel joint optimal allocation model of regional water and land resources (JOAMRWLS). The model integrates water and land resources for integrated allocation, taking into account the mutual feed relationship between them, and thereby achieving comprehensive utilization and coordination of water and land resources within the region. Then a two-layer nesting algorithm based on successive approximation and nonlinear programming (SA-NLP) is proposed to solve the model, obtaining the regional optimal land use pattern and optimal water allocation scheme. Subsequently, this paper uses Luoyang City in Henan Province, China, as a case study to verify the proposed JOAMRWLS. The results indicate that the water volume and area of each land use type tend to stabilize after the fifth iteration of the calculation with a consistent water volume of 18.65 × 108 m3. Compared with the conventional optimization model of water resources (COMWR), the JOAMRWLS encompasses five cities experiencing water scarcity, whereas the latter includes twelve such cities. Furthermore, the economic benefit of the JOAMRWLS is 2.65 × 1011 CNY, surpassing that of the former at 2.56 × 1011 CNY, highlighting its superiority.

Economics of primary healthcare: cost estimation of clinical services at primary care facilities in the six countries of the Gulf Cooperation Council

ObjectiveWhile the Gulf Cooperation Council (GCC) countries have demonstrated a strong commitment to strengthening primary healthcare (PHC), the costs of delivering these services in this region remain relatively unexplored. Understanding...

Load-aware continuous-time optimization for multi-agent systems: toward dynamic resource allocation and real-time adaptability

In the realm of next-generation mobile communication networks, characterized by dynamic and evolving workloads, the efficient resource allocation becomes paramount for achieving optimal performance. This paper addresses the intricate challenges of multiagent resource management through the integration of stochastic learning automata and continuous-time optimization techniques, presenting an innovative solution for real-time adaptability and dynamic load balancing. The central aim is to achieve optimal values for local time-varying cost functions while taking into account resource constraints and the feasibility of local allocation. In the context of multi-agent resource allocation systems, where conditions vary over time, the algorithm exhibits a dynamic adaptability, continuously adjusting to the evolving optimal solutions tailored for each agent. Utilizing stochastic learning automata, the algorithm effectively addresses the time-varying load-balancing function in response to dynamic user demands. Furthermore, we introduced an scalable solution for mobility robustness optimization based on deep reinforcement learning (DRL-MRO). This method dynamically identifies network-wide optimal parameter values across the entire network to accommodate diverse mobility patterns, ensuring the effectiveness of load balancing parameters that seamlessly adapt to the dynamic configuration of the network. The overarching goal is to maintain a consistent quality of service level for each agent, thereby enhancing the overall system performance. Simulation outcomes unequivocally affirm the superior performance of the proposed load balancing approach, rooted in continuous-time optimization and stochastic learning automata, surpassing existing schemes across diverse system configurations.

Blocklength Allocation and Power Control in UAV-Assisted URLLC System via Multi-agent Deep Reinforcement Learning

Integration of unmanned aerial vehicles (UAVs) with ultra-reliable and low-latency communication (URLLC) systems can improve the real-time communication performance for various industrial internet of things (IIoT) applications. Designing an intelligent resource allocation system is one of the challenges posed by an energy-constrained UAV communication system. Therefore, we formulate a sum rate maximization problem, subject to the UAVs’ energy by optimizing the blocklength allocation and the power control jointly in the uplink UAV-assisted URLLC systems, in which the probabilistic channel model between UAV and users is adopted. The problem is difficult to solve due to the non-convex objective function and the energy constraints, and also challenging to make fast decision in the complex communication environment. Thus, we propose a deep reinforcement learning (DRL)-based scheme to optimize the blocklength allocation and power control jointly. First, transform the original problem into the multi-agent reinforcement learning process, where each subcarrier is regarded as the agent that optimizes its individual blocklength allocation and power control. Then, each agent makes the intelligent decision to obtain the maximum reward value depending on the weighted segmented reward function, which is related to the UAV energy consumption and user rates to improve the rate performance. Finally, the simulation results show that the proposed scheme outperforms the benchmark schemes and has the stable convergence in different settings, such as the learning rate, the error probability, the subcarrier spacing, and the number of users.

Distributed and Coordinated Model Predictive Control for Channel Resource Allocation in Cooperative Vehicle Safety Systems

Distributed and Coordinated Model Predictive Control for Channel Resource Allocation in Cooperative Vehicle Safety Systems

A Lyapunov-Based Approach to Joint Optimization of Resource Allocation and 3-D Trajectory for Solar-Powered UAV MEC Systems

A Lyapunov-Based Approach to Joint Optimization of Resource Allocation and 3-D Trajectory for Solar-Powered UAV MEC Systems

Multi-objective optimization of solar resource allocation in radial distribution systems using a refined slime mold algorithm

The integration of distributed generation resources in power systems offers various advantages, such as peak load management and reduced transmission line congestion. However, it also introduces challenges related to voltage stability. This paper presents a novel multi-objective model for optimizing the allocation of solar resources in radial distribution systems. The model aims to achieve an optimal voltage profile, minimize losses, and maximize penetration levels. To address the conflicting nature of these objectives, a refined multi-objective slime mold algorithm (MOSMA) is proposed. This algorithm demonstrates exceptional capabilities in finding Pareto fronts, avoiding local optima, and effectively solving multi-objective problems compared to other optimization methods. Additionally, the corrected social hierarchy method is integrated to enhance performance. The proposed method is evaluated using a standard system under various operational conditions, showing superior results in terms of maintaining an acceptable voltage profile and significantly reducing losses. The study reveals that while losses decrease for penetration levels ranging from low to medium, they start to increase for levels exceeding 100 %. Notably, the proposed method achieves approximately 12 % system efficiency improvement, as measured by the voltage profile, at a penetration level of 300 %. These findings highlight the effectiveness of the proposed method, even at high penetration levels, surpassing other optimization approaches based on the inverse generation distance parameter.