Intelligent Resource Allocation Research Articles

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.

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

Digital twin‐based framework for wireless multimodal interactions over long distance

SummaryWireless multimodal interactions over long distance (WMILD) would give rise to numerous thrilling applications, such as remote touching and immersive teleoperations. However, long distances can induce large propagation delays, which makes it difficult to meet the ultra‐low latency requirements in haptic‐visual interactions. Considering existing works mainly focused on the wireless access part, this paper designs an end‐to‐end framework for general WMILD applications based on the digital twin (DT) technology and proposes an intelligent resource allocation and parameter compression scheme to guarantee WMILD performance under constraint network resources. In the framework, user device can acquire real‐time remote interactions by performing local interactions with nearby base station (BS), where a DT of the remote side is deployed to predict the remote haptic‐visual feedbacks. A reliable DT updating process is carefully designed to guarantee the DT accurately model its dynamic physical counterpart. To optimize the updating reliability, we formulate the resource allocation and parameter compression to be a constraint‐Markov decision problem, under the constraints on energy consumption, multimodal interactions and updating latencies. Then, a safe deep reinforcement learning algorithm is proposed to adapt resources and compression according to the dynamic DT updating workload, multimodal data‐streams and remote transmission capacities. Simulation shows the framework can achieve high updating reliability compared with baselines.

Intelligent Resource Allocation in Backscatter-NOMA Networks: A Soft Actor Critic Framework

With the use of power domain non-orthogonal multiple access (NOMA) and backscatter communication (BAC), future sixth-generation ultra massive machine-type communications networks are expected to connect large-scale internet of things (IoT) devices. However, due to NOMA co-channel interference, the power allocation to large-scale IoT devices becomes critical. The existing convex optimization-based solutions are highly complex, and therefore it is difficult to find the optimal solution to the resource allocation problem in a highly dynamic environment. To alleviate this problem, this work develops an efficient model-free BAC approach with a NOMA system to assist the base station with complex resource scheduling tasks in a dynamic BAC-NOMA IoT network. The objective is to increase the sum rate of uplink backscatter devices. More specifically, we jointly optimize the transmit power of downlink IoT users and the reflection coefficient of uplink backscatter devices using a reinforcement learning algorithm, namely, the soft-actor critic (SAC) algorithm. With the advantage of entropy regularization, the SAC agent learns to explore and exploit the dynamic BAC-NOMA network efficiently. The proposed algorithm ensures the quality of service (QoS) requirements of downlink users while enhancing the sum rate of uplink backscatter devices. Numerical results reveal the superiority of the proposed algorithm over the conventional optimization (benchmark) approach in terms of the average sum rate of uplink backscatter devices. We show that the network with multiple downlink users obtained a higher reward with respect to a large number of iterations compared to episodes with a lower number of iterations. Moreover, the proposed algorithm outperforms the benchmark scheme and BAC with orthogonal multiple access in terms of the average sum rate with the different number of backscatter devices. Additionally, we show that our proposed algorithm enhances sum rate efficiency with respect to different self-interference coefficients and different noise levels. Finally, we evaluate and show the sum rate efficiency of the proposed algorithm with different QoS requirements and cell radii.

Intelligent Resource Allocation for V2V Communication with Spectrum-Energy Efficiency Maximization.

Aiming to address the limitations of traditional resource allocation algorithms in the Internet of Vehicles (IoV), whereby they cannot meet the stringent demands for ultra-low latency and high reliability in vehicle-to-vehicle (V2V) communication, this paper proposes a wireless resource allocation algorithm for V2V communication based on the multi-agent deep Q-network (MDQN). The system model utilizes 5G network slicing technology as its fundamental feature and maximizes the weighted spectrum-energy efficiency (SEE) while satisfying reliability and latency constraints. In this approach, each V2V link is treated as an agent, and the state space, action, and reward function of MDQN are specifically designed. Through centralized training, the neural network parameters of MDQN are determined, and the optimal resource allocation strategy is achieved through distributed execution. Simulation results demonstrate the effectiveness of the proposed scheme in significantly improving the SEE of the network while maintaining a certain success rate for V2V link load transmission.

A conditioned reflex action embedded associative context learning‐based energy efficient paradigm in smart city milieu

AbstractAn intelligent video surveillance system is crucial to enhance public safety, crime prevention, traffic, and crowd management in a smart city milieu. Situational awareness is an essential aspect of these surveillance systems and it is inferred through underlying context aware frameworks. However, these systems may not possess the ability to proactively disseminate the real‐time context among its sensor nodes. Moreover, in the specific conditions of occurrence of related or repeated events, these systems may also perform inefficiently through afresh context processing and disseminate cycles, without learning from the relevant context that has already been occurred and processed by the system. It leads to deteriorated performance, especially delay in reaction, overwhelmed processing, and energy expenditures. Therefore, to counter such issues, this research work proposes an energy efficient situational aware framework deployed in visual sensors network that is incorporated with context associative learning. System observes currently occurring context at each instance of an event. Overtime, context is refined and stored in context database. Such mechanism empowers the system to learn from previous experiences and develop relationship among the subsequent events that is embedded through this associative (adaptive) learning. Eventually, each event is processed through intelligent resource allocation, supported through mechanism of context learning that further illustrates the independent functions of reduced processing and improved (rapid) decision making resulting in evolution of energy efficient computing paradigm. Ultimately, the capability of learned reflex‐action is induced through introspectively evolved context of the system in entirety and against specific condition of recurred situation depicting minimum energy expenditure.

Multi-granularity fusion resource allocation algorithm based on dual-attention deep reinforcement learning and lifelong learning architecture in heterogeneous IIoT

Deep reinforcement learning (DRL) is a promising technology to address the resource allocation problem for efficient data transmission in complex network environments. However, most DRL-based resource allocation algorithms suffer from limited feature extraction capabilities and lack scalability and generalization, especially in heterogeneous Industrial Internet of Things (IIoT) environments. In this paper, we develop a lifelong learning architecture that can integrate artificial intelligence (AI) algorithms into the heterogeneous IIoT network for efficient data transmission. Based on this, we propose an intelligent resource allocation algorithm based on dual-attention DRL (DADR) for forwarding node selection and channel access slot allocation in a specific network environment. The proposed DADR algorithm combines the advantages of multi-dimension convolutional attention and multi-head self-attention mechanisms. It can provide local- and global-feature fusion capabilities for distributed nodes while maximizing the performance of data transmission. Furthermore, we present a lifelong federated meta reinforcement learning (LFMRL) that can effectively utilize prior knowledge and enable the DRL agent quickly adapt to a new environment. Specifically, LFMRL adopts a federated meta learning-based knowledge fusion algorithm to fuse the knowledge of learned DADR algorithms and iteratively update the shared model, thereby improving the scalability and generalization of the shared model in heterogeneous IIoT environments. In addition, a simple and efficient knowledge transfer mechanism is enabled to accelerate the DRL model convergence by transferring the knowledge of the shared model to the new environment. Simulation results demonstrate the effectiveness of the proposed algorithms in terms of energy efficiency, data transmission reliability, and network stability. Compared to DADR and FedAvg algorithms, LFMRL algorithm can further reduce the energy consumption, training time, and average forwarding node switching times, while improving packet delivery rate to 99.2%.

Intelligent Resource Allocation for IRS-Enhanced OFDM Communication Systems: A Hybrid Deep Reinforcement Learning Approach

Orthogonal frequency division multiplexing (OFDM) systems have been widely applied in practice since OFDM has diverse outstanding advantages. However, their performance improvement is confronted with bottlenecks. In this paper, in order to tackle this issue, intelligent resource allocation driven by reinforcement learning is studied in intelligent reflecting surface (IRS) enhanced OFDM systems. The system sum rate is maximized by jointly optimizing the subcarrier allocation, the transmit beamforming of the base station and the phase shift of the IRS. An intelligent resource allocation scheme based on combining deep Q networks (DQN) and deep deterministic policy-gradient (DDPG) is proposed to tackle the formulated challenging non-convex problems. In order to further improve the spectrum efficiency, spectrum sharing is considered in the IRS-enhanced OFDM system. The secondary users sum rate maximization framework is formulated by jointly optimizing the channel allocation, the transmit beamforming of the secondary base station (SBS) and the phase shift of the IRS. Dueling double deep Q networks (D3QN) and twin delayed deep deterministic policy gradient (TD3) are exploited to tackle the hybrid action space issue under interference. Simulation results demonstrate that our proposed schemes can significantly improve the transmission rate compared to the benchmark schemes.

Toward Secure Transmission in Fog Internet of Things Using Intelligent Resource Allocation

Thanks to the full play of the synergy of local node sets, fog Internet of things technology has become the focus of many researchers. Unfortunately, the data transmission of the sensing terminal therein has the risk of being eavesdropped, due to the openness of the wireless channel and the large-scale deployment of different types of terminals. Meanwhile, the encryption method based on cryptography is difficult to apply to the Internet of things terminals with limited capacity because of its high computational complexity. This paper considers the use of physical layer security technology where the fog node improves the secrecy capacity of the desired channel by sending artificial noise to the untrusted third party to ensure the reliable transmission of data. Specifically, an intelligent resource allocation method based on deep reinforcement learning is proposed to realize the rapid allocation of link resources and interference noise power in the scene, where the relevant elements, such as state, action and reward, are reasonably designed. Simulation results demonstrate that the presented algorithm could effectively resolves the allocation problem, and its transmission delay obviously outperforms that of multiple comparison methods.