Intelligent Resource Allocation Research Articles

Intelligent resource allocation in wireless networks: Predictive models for efficient access point management

With the significant increase in mobile users connected to the wireless network, coupled with the escalating energy consumption and the risk of network saturation, the search for resource management has become paramount. Managing several access points throughout a whole region is hugely relevant in this context. Moreover, a wireless network must keep its Service Level Agreement, regardless of the number of connected users. With that in mind, in this work, we propose four prediction models that allow one to predict the number of connected users on a wireless network. Once the number of users has been predicted, the network resources can be properly allocated, minimizing the number of active access points. We investigate the use of Particle Swarm Optimization and Genetic Algorithms to hyper-parameterize a Multilayer Perceptron neural network and a Decision Tree. We evaluate our proposal using a campus-based wireless network dataset with more than 20,000 connected users. As a result, our model can considerably improve network performance by intelligently allocating the number of access points, thereby addressing concerns related to energy consumption and network saturation. The results have shown an average accuracy of 95.18%, managing to save network resources effectively.

Intelligent resource allocation in hybrid RF/LiFi networks via deep deterministic policy gradient based DRL mechanism

A hybrid radio frequency (RF) and light fidelity (LiFi) network combines the strengths of RF and LiFi technologies. RF offers broad coverage, while LiFi provides high data rates. As these technologies operate on non-interfering spectra, they can co-exist without interfering with each other. This setup not only boosts data rate but also makes the network more reliable, especially when physical obstacles might block signals. However, resource management in hybrid RF/LiFi networks is challenging because of the dynamic environment and the different characteristics of the two technologies. Efficient resource allocation maximizes the data rate in these networks. In this paper, we introduce a model-free deep reinforcement learning (DRL) approach to solve the resource allocation problem in hybrid RF/LiFi networks. Our DRL model is designed to handle real-world conditions, considering factors like blockages and user mobility. Unlike traditional methods that need extensive modeling and assumptions, our approach learns directly from interacting with the environment, making it highly adaptable and robust. Through simulations, it is observed that our method improves resource utilization and overall network performance, achieving a 62.8% increase in sum rate and a 42.8% improvement in optimal transmit power compared to conventional methods.

Key Factors Affecting the Success of Construction Multi Project Management

Simultaneous management of multiple projects has become a trend in the field of energy, oil and gas construction project management over the past decade. This situation is driven by the high market demand and need for fossil fuels, which also stresses project management discipline to improve its performance and efficiency in managing multiple projects simultaneously. Nevertheless, concurrent execution of multiple construction projects often leads to stretched the project durations and cost due to suboptimal resource management, which may become a boomerang and increase project costs. The objective of this study is to identify factors and indicators that contribute to the successful management of multiple projects. Although multi-project management has been extensively studied, there is still a dearth of studies analyzing the elements that make construction multi-project management successful, particularly in the energy, oil, and gas sectors. This research is a case study of an EPCIC company that uses multi-project management and operates in the oil and gas sector. Through purposive sampling techniques, a total of 58 executives from national oil and gas contractors with significant work experience participated in this study through a questionnaire survey. The PLS-SEM approach has revealed that the positive predictor factors influencing the success/performance of multi-project management are resource allocation, teamwork quality, multi-project management competency, and project manager assignment. Flexible and intelligent resource allocation management in multi-project cycles affected by uncertainty is a critical factor.

An intelligent resource allocation strategy with slicing and auction for private edge cloud systems

The convergence of transformative technologies, including the Internet of Things (IoT), Big Data, and Artificial Intelligence (AI), has driven private edge cloud systems to the forefront of research efforts. The access to massive terminals and the emergence of personalized services pose serious challenges for efficient resource management in power private edge cloud systems. To address the challenge of inequitable resource allocation in the private edge cloud, this work proposes an intelligent resource allocation strategy with a slicing and auction approach. By formalizing the resource allocation problem as a Mixed Integer Nonlinear Programming (MINLP) puzzle, the method transforms it into a hierarchical allocation challenge for Mobile Network Operators (MNOs), Mobile Virtual Network Operators (MVNOs), and power terminals. The proposed Multi-hop Progressive Auction Algorithm (MPAA) addresses the sliced resource allocation problem between MNOs and MVNOs. Furthermore, a Terminal Resource Allocation Strategy (TRAS) based on improved particle swarm optimization is proposed to solve the spectrum resource allocation problem between MVNOs and power terminals. Extensive simulation results show that the bidding overhead of MPAA is reduced by 6.12% and the average terminal satisfaction of TRAS is improved by about 1.3% compared to conventional methods, thus improving the utilization of wireless resources within the power AIoT.

A DRL-based resource allocation for IRS-enhanced semantic spectrum sharing networks

Semantic communication and spectrum sharing are pivotal technologies in addressing the perennial challenge of scarce spectrum resources for the sixth-generation (6G) communication networks. Notably, scant attention has been devoted to investigating semantic resource allocation within spectrum sharing semantic communication networks, thereby constraining the full exploitation of spectrum efficiency. To mitigate interference issues between primary users and secondary users while augmenting legitimate signal strength, the introduction of Intelligent Reflective Surfaces (IRS) emerges as a salient solution. In this study, we delve into the intricacies of resource allocation for IRS-enhanced semantic spectrum sharing networks. Our focal point is the maximization of semantic spectral efficiency (S-SE) for the secondary semantic network while upholding the minimum quality of service standards for the primary semantic network. This entails the joint optimization of parameters such as semantic symbol allocation, subchannel allocation, reflective coefficients of IRS elements, and beamforming adjustment of secondary base station. Recognizing computational intricacies and interdependence of variables in the non-convex optimization problem formulated, we present a judicious approach: a hybrid intelligent resource allocation approach leveraging dueling double-deep Q networks coupled with the twin-delayed deep deterministic policy. Simulation results unequivocally affirm the efficacy of our proposed resource allocation approach, showcasing its superior performance relative to baseline schemes. Our approach markedly enhances the S-SE of the secondary network, thereby establishing its prowess in advancing the frontiers of semantic spectrum sharing (S-SE).

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.

An Overview of Demand Analysis and Forecasting Algorithms for the Flow of Checked Baggage among Departing Passengers

The research on baggage flow plays a pivotal role in achieving the efficient and intelligent allocation and scheduling of airport service resources, as well as serving as a fundamental element in determining the design, development, and process optimization of airport baggage handling systems. This paper examines baggage checked in by departing passengers at airports. The crrent state of the research on baggage flow demand is first reviewed and analyzed. Then, using examples of objective data, it is concluded that while there is a significant correlation between airport passenger flow and baggage flow, an increase in passenger flow does not necessarily result in a proportional increase in baggage flow. According to the existing research results on the influencing factors of baggage flow sorting and classification, the main influencing factors of baggage flow are divided into two categories: macro-influencing factors and micro-influencing factors. When studying the relationship between the economy and baggage flow, it is recommended to use a comprehensive analysis that includes multiple economic indicators, rather than relying solely on GDP. This paper provides a brief overview of prevalent transportation flow prediction methods, categorizing algorithmic models into three groups: based on mathematical and statistical models, intelligent algorithmic-based models, and combined algorithmic models utilizing artificial neural networks. The structures, strengths, and weaknesses of various transportation flow prediction algorithms are analyzed, as well as their application scenarios. The potential advantages of using artificial neural network-based combined prediction models for baggage flow forecasting are explained. It concludes with an outlook on research regarding the demand for baggage flow. This review may provide further research assistance to scholars in airport management and baggage handling system development.

Dynamic Adaptive Resource Allocation for Edge Computing in Big Data Analytics Using GBDT, DQN, and GA Algorithms

Edge computing in big data refers to processing and analysing data closer to its source, reducing latency and bandwidth usage. It leverages devices at the network edge to perform computations, making real-time analytics feasible. This distributed approach improves efficiency and enables faster decision-making, critical for applications like IoT, autonomous vehicles, and healthcare. The research proposes an innovative approach that harnesses three machine learning algorithms Gradient Boosting Decision Trees (GBDT), Deep Q-Network (DQN), and Genetic Algorithm (GA) to enable dynamic adaptive resource allocation within edge computing environments tailored for big data analytics. GBDT enhances classification accuracy by sequentially refining predictions through decision trees, accommodating heterogeneous data types and yielding high prediction accuracy crucial for dynamic edge environments. The GA evaluates resource allocation strategies represented as chromosomes within a population, selecting promising solutions as parents for the next generation and generating diverse offspring through crossover and mutation operations to discover optimal solutions. DQN facilitates intelligent resource allocation by iteratively refining Q-values based on experiences gathered during interactions with the environment, utilizing a neural network to determine optimal actions for a given state, thereby enhancing performance and efficiency in edge computing environments. This integrated approach ensures flexible resource allocation and fortified capabilities for big data analytics within edge computing environments. The research underscores GBDT as the most promising algorithm for resource allocation in edge computing environments, owing to its exceptional performance in resource utilization, scalability, and accuracy. This nuanced understanding of algorithmic behaviour in dynamic settings offers invaluable insights for optimizing resource allocation strategies, thereby enhancing the efficiency and effectiveness of edge computing systems in handling big data analytics tasks.

EXPLORING THE RELATIONSHIP BETWEEN PLANT HEIGHT AND YIELD-CONTRIBUTING ATTRIBUTES OF WHEAT IN DROUGHT CONDITIONS

Plant height is the most dynamic yield-affecting trait which has strong genetic and phenological associations with yield performance in wheat. The objective of the study was to explore the effect of plant height on spike yield by investigating the interrelationship between height and yield components of ninety wheat genotypes under a drought environment. Different statistical procedures including correlation, regression, path coefficient, principle component, biplot, and cluster analysis were performed to dissect the associative role of height and yield components in setting the yield potential of wheat plants. High positive correlation and direct effects are seen for spike yield from seed weight (r = 0.73) and seed number (r = 0.60) with a participatory contribution of 58% and 40%, respectively. But plant height is negatively associated with thousand seed weight (-0.22*) and spike yield (-0.15) in such a way that each additional unit (1cm) of height reduces 0.11 g thousand seed weight and 0.06 g spike yield due to negative regression. PC1 and PC2 associate height and yield components respectively and both types of traits moderately oppose each other for simultaneous improvement. Highly tall and short plants were observed with low yield potential under water-limited resources due to strong negative effects either on seed weight or number. An increase in height favors seed number but reduces enough seed weight to cause comparative losses. Intelligent allocation of plant resources to height or spike defines the plant yield potential. Plants with moderate height ranging from 74-83 cm were seen with the best yield potential among five different classes of height. Genotypes HM729, HM829, and HM644 belonged to this class and their better yield potential was due to the balance ratio of seed weight and number. This work would help direct our breeding programs for the improvement of the height-oriented yield potential of wheat.

Optimizing Waste Collection Efficiency by NodeMCU enabled Ultrasonic Trash Can Surveillance and ThingSpeak Dashboards

The use of inflexible route schedules for garbage collection results in inadequate waste disposal, posing environmental risks and incurring high operational expenses, including fuel costs and landfill fees, even when the waste containers are not completely full. This study utilizes a cost-effective Internet of Things (IoT) monitoring system to facilitate dynamic routing. The aim is to enhance the efficiency and frequency of waste collection by optimizing it based on real-time fill levels, rather than following fixed cycles. The prototype employs an ultrasonic distance sensor in conjunction with a NodeMCU ESP8266 micro-controller to detect the fill level of a trash can. The collected data is then transmitted to the ThingSpeak cloud platform. Real-time visualization dashboards display live data on waste levels in different containers, providing guidance for collection scheduling. The estimated scope entails conducting circuit simulation using Proteus, optimizing PCB layout, and developing an integrated IoT prototype connected to ThingSpeak for IoT analytics. Ultimately, integrating cost-effective IoT edge sensors such as ultrasonic ranging with cloud analytics dashboards greatly enhances effectiveness, sustainability, and intelligent allocation of resources in public trash collection. This results in economic and environmental advantages by reducing landfill overflow through the digitalization of smart cities.

Intelligent Resource Allocation and Task Offloading Model for IoT Applications in Fog Networks: A Game-Theoretic Approach

Fog computing is an emerging paradigm that allows IoT devices and applications to process their data at the network’s edge. However, fog servers have limited computational resources compared to the cloud system; therefore, they cannot accommodate the ever-increasing computational demand from IoT devices. In such a challenging environment, IoT users’ decision to offload their tasks to the fog node or the central cloud server is affected by the environment’s current dynamics as other IoT users are also competing to maximize their resource utilization. In this paper, we propose a computational model that considers energy consumption and transmission latency as decision parameters for task offloading of IoT applications. Second, we model the competition as a game where IoT devices’ decision for the optimal distribution of tasks is captured in a joint optimization problem for energy consumption and latency. Third, we propose a decentralized task distribution algorithm where the players learn to update their strategy based on other players’ actions. We also prove that the solution of the proposed algorithm converges to Nash equilibrium (NE). Finally, we carry out extensive evaluations and compare our computational model and results with existing studies.

Harvesting cognitive radio networks using artificial intelligence

The utilization of Artificial Intelligence (AI) in leveraging Cognitive Radio Networks (CRNs) represents an emerging field of study. This surge is primarily driven by operational expenses, concerns over traditional power sources, and limitations inherent in current CRN technologies. Furthermore, integrating AI into CRN operations significantly enhances efficiency and maximizes the application of the electromagnetic spectrum. To enable real-time processing, Cognitive Radio (CR) is paired with AI methodologies, fostering adaptive and intelligent resource allocation. This research paper outlines CRNs: their objectives, available resources, and constraints. It subsequently introduces AI techniques, emphasizing the profound influence of learning within CR contexts. The application of model methods such as Markov Model, fuzzy logic, and Neural Network is explored. AI technology is employed in critical CR tasks like spectrum sharing, spectrum sensing, resource allocation, optimization of spectrum mobility, decision-making processes, and more. The overarching goal is to showcase how AI can assist researchers in harnessing and implementing diverse CR designs effectively.

Deep Reinforcement Learning based Intelligent Resource Allocation in Hybrid Vehicle Scenario

Deep Reinforcement Learning based Intelligent Resource Allocation in Hybrid Vehicle Scenario

DRL-based intelligent resource allocation for physical layer semantic communication with IRS

Semantic communication and intelligent reflection surface (IRS) are considered to be promising technologies to solve the scarce spectrum resource problem for the sixth-generation (6G) communication networks. However, there is few research on semantic resource allocation for IRS-enhanced communication networks, which leverages the efficient spectrum utilization of both semantic communication and IRS. In this paper, the resource allocation problem in the IRS-assisted semantic communication network is investigated, and effective semantic spectral efficiency (ES-SE) is defined considering desired semantic similarity for downstream semantic tasks. For the purpose of maximizing the ES-SE, the selection of DeepSCs, allocation of subchannels, reflection array elements of the IRS and transmit beamforming of the base station (BS) are jointly optimized. Considering the necessity of real-time performance and full-link intelligence, a two-stage intelligent approach using dueling double deep Q networks (D3QN)-soft actor critic (SAC) is proposed to tackle the tough resource allocation problem with non-linear programming and coupled variables. Simulation results validate the effectiveness of our designed IRS-assisted semantic communication scheme and demonstrate the superior performance of our proposed intelligent approach.

Dynamic Allocation Mechanism of Cloud Computing Resources Driven by Neural Network

With the popularization of cloud computing technology, the dynamic allocation mechanism of cloud computing resources has become an important research field to improve resource utilization and meet the needs of diversified workloads. The purpose of this study is to explore the dynamic allocation mechanism of cloud computing resources driven by neural network and introduce the powerful ability of deep learning into cloud computing environment. We put forward a comprehensive framework, which combines data collection, analysis, decision-making and implementation to realize intelligent resource allocation. These data will be used to train BP neural network (BPNN). In order to predict the bidding price, a BPNN is designed, which usually includes input layer, hidden layer and output layer. The number of nodes in the input layer is equal to the dimension of the input feature, and the number of nodes in the output layer is 1, which indicates the prediction of the bidding price. Through experiments and simulations, we verify the effectiveness of the dynamic resource allocation mechanism driven by neural network. The results show that this mechanism can better adapt to the changing workload requirements, improve resource utilization and reduce resource waste. In addition, it provides better performance and user experience, thus enhancing the competitiveness of cloud computing systems.

Age of Information, Latency, and Reliability in Intelligent Vehicular Networks

Autonomous driving and intelligent transportation have posed stringent requirements on different performance metrics of vehicle-to-everything (V2X) communications depending on the wireless service being carried. Such coexisting heterogeneous performance requirements bring about increasing challenge in managing modern vehicular networks. In this article, we categorize wireless vehicular traffic into two kinds, namely, status update service and data communication service, where in safety-related applications the former is concerned with age of information (AoI) while the latter cares about latency and reliability in general. Afterwards, we uncover the relationship among AoI, latency, and reliability, and propose a novel graphical packet delivery model that is powerful for analyzing AoI performance. To unify the AoI, latency, and reliability management for different services, we further develop an intelligent resource allocation framework validated by a quantitative case study. Finally, we explore key challenges and opportunities to address the coexistence of status update and data communication services in intelligent vehicular networks.

Smart Optimization Solution for Channel Access Attack Defense Under UAV-Aided Heterogeneous Network

6G-based wireless communication system is poised to redefine the next-generation network landscape by enabling novel services and applications such as intelligent link establishment, power control, data collection, transmission, and distribution. However, security issues, particularly recently revealed Channel Access Attack (CAA), present significant challenges to performance optimization tasks in the heterogeneous wireless networks of 6G, namely, Age of Information (AoI) Oriented Network (AoN), Throughput Oriented Network (ToN), and Latency Oriented Network (LoN). To address these challenges, this paper presents a game theory-based smart optimization solution to enable Unmanned Aerial Vehicles (UAV) to resist CAA within a 6G-based heterogeneous network. Our methodology begins by outlining the advantages and challenges associated with UAV usage, followed by the design of performance indicators and intelligent resource allocation schemes under the influence of CAA. Subsequently, we introduce definitions and categories within game theory, encompassing the concept and equilibrium of three typical game models. The efficacy of our proposed framework is validated through simulation results, which demonstrate the achievement of optimal AoI, enhanced throughput, and reduced latency compared with baseline methodologies when countering CAA in a UAV-assisted heterogeneous network.

Intelligent Resource Allocation Using an Artificial Ecosystem Optimizer with Deep Learning on UAV Networks

An Unmanned Aerial Vehicle (UAV)-based cellular network over a millimeter wave (mmWave) frequency band addresses the necessities of flexible coverage and high data rate in the next-generation network. But, the use of a wide range of antennas and higher propagation loss in mmWave networks results in high power utilization and UAVs are limited by low-capacity onboard batteries. To cut down the energy cost of UAV-aided mmWave networks, Energy Harvesting (EH) is a promising solution. But, it is a challenge to sustain strong connectivity in UAV-based terrestrial cellular networks due to the random nature of renewable energy. With this motivation, this article introduces an intelligent resource allocation using an artificial ecosystem optimizer with a deep learning (IRA-AEODL) technique on UAV networks. The presented IRA-AEODL technique aims to effectually allot the resources in wireless UAV networks. In this case, the IRA-AEODL technique focuses on the maximization of system utility over all users, combined user association, energy scheduling, and trajectory design. To optimally allocate the UAV policies, the stacked sparse autoencoder (SSAE) model is used in the UAV networks. For the hyperparameter tuning process, the AEO algorithm is used for enhancing the performance of the SSAE model. The experimental results of the IRA-AEODL technique are examined under different aspects and the outcomes stated the improved performance of the IRA-AEODL approach over recent state of art approaches.

An intelligent resource allocation strategy for machine type communication environment

SummaryMobile communication has multiplied in many digital intelligent applications. The machine‐type communication (MTC) services have afforded flexible communication facilities considering those facilities. However, high energy consumption and poor data rate have made the MTC a complex system. So, the resource allocation strategy has been introduced by different procedures such as neural models, optimization, and mathematical models. But, in some cases, these algorithms have recorded high complexity and high resource requirement. Hence, the present research article has planned to develop a novel Chimp‐based Extreme Neural Model (CbENM) for allocating the desired optimal resource for each machine user. Moreover, the resources were assigned based on the task completion deadline. Before the resource allocation process, the active state users were predicted by analyzing their data rate and deadlines. Finally, the desired resources were allocated to each user, and the performance was validated. The presented model has a lower delay bond rate and a high data aggregate rate.

Intelligent Resource Allocation for Grant-Free Access: A Reinforcement Learning Approach

Intelligent Resource Allocation for Grant-Free Access: A Reinforcement Learning Approach