Dynamic Allocation Research Articles

Drone path planning and dynamic arena-target allocation for crowd surveillance

The paper addresses the problem of surveillance of dynamic crowds in a bounded arena using multiple drones. The strategy proposes a prediction-based path planning approach for drones, where the target’s future position is predicted and used to generate a traverse path. The procedure for grouping numerous human beings in order to deal with a dense crowd scenario is also described. An algorithm to dynamically divide and allocate the arena to drones is also proposed to ensure equal workload distribution among all drones in the framework. The paper also discusses relevant concepts like detecting anomalous crowd activity and searching for any missing target in the arena. The proposed algorithms are validated on MATLAB environment numerically and ROS/Gazebo physics engine as a part of simulation-in-the-loop (SITL).

Employee-centered Industry 5.0 - Case analysis of Haier

How green digital intelligence and ecology promote enterprises to transform into industry 5.0 has attracted the attention of the industry and academia. Still, its internal driving mechanism at the micro level has not been clarified. Based on the perspective of enterprise dynamic capability and resource allocation, this paper studies the “path-process-capability” logical scheme of Industry 5.0 restructuring value chain. Firstly, the multi-case configuration analysis is carried out through focused stepwise quantitative case analysis to explore the resource collaboration path of Industry 5.0 transformation. Then, the process of digital intelligence and ecological reconstruction of the value chain is studied longitudinally for a single case, and the stage characteristics of green digital intelligence and ecology driving the Industrial 5.0 transformation of manufacturing enterprises are studied by combining the time series interval method and the critical event method. The process mechanism of digital intelligence and Industry 5.0 key capabilities are found. Aimed at the path problem of green digital intelligence and ecological reconstruction of the manufacturing value chain, based on multi-case statistical analysis and single-case in-depth analysis, the reconstruction mechanism and scheme of manufacturing ecological value chain were obtained. The research findings are as follows: firstly, four configuration forms of digital intelligence and ecological resource allocation are identified, and the rational allocation of resources is realized through the collaborative effect of resources. Secondly, the ecological process of green digital intelligence of manufacturing enterprises is based on the process of “standardization - online - integration - intelligence - ecological - Industry 5.0”, and the key technologies of each stage are identified. Third, in the transformation process of Industry 5.0, three key capabilities of enterprise digital production, digital drive, and ecological economy are constructed and data-led to promote the transformation and development of enterprise intelligent services and leverage the new business form of digital ecology.

Water Banking as a Strategy for the Management and Conservation of a Critical Resource: A Case Study from Tunisia’s Medjerda River Basin (MRB)

The increasingly adverse impacts of climate change (e.g., rainfall patterns, droughts, and floods), coupled with the ever-increasing water demands, are often translated into a contingent liability for water users’ communities. Additional complexities arise due to competing priorities, water rights, and transboundary water sources. Therefore, conventional water management practices should shift toward more comprehensive and responsive integrative approaches, even for systems with limited data. Furthermore, water managers must prioritize dynamic and interactive management techniques for existing systems. One such management technique is water banking, which is the focus of this study. Herein, a dynamic interactive water allocation model, which encompasses the water managers and heterogeneous parties with competing demands, is developed. The voluntary sales of water shares between parties are illustrated through the specific case of the Medjerda River in Tunisia, an excellent example of a transboundary basin with limited hydrologic data and conflicting water use requirements between its upstream and downstream sectors. A set of scenarios is developed for the first analysis with this model: two management scenarios that include the no-water trade and the water banking option; three demand scenarios that include a combination of steady-, low-, and high-water demand conditions; and two hydrologic scenarios that include dry and wet conditions. Based on an economic model, the economic impacts of water banking are calculated using estimates of the costs of water shortages brought to users that illustrate the magnitude. The results show that the water banking technique can improve water resource availability by optimizing the management, operation, and conservation of natural and artificial water storage systems and water distribution infrastructure. Specifically, water banking can offset users’ profit losses during severe conditions (i.e., drought), even with limited hydrologic data. This water management technique would allow the Tunisian government to minimize the economic impacts on farmers from drought and to plan for future uncertainties by optimizing the water storage potential in years of abundant rainfall.

Hybrid Multiple Access Techniques Performance Analysis Of Dynamic Resource Allocation

The Non-Orthogonal Multiple Access (NOMA) and the Orthogonal Frequency Division Multiple Access (OFDMA) are promising techniques for next-generation wireless systems. Although much attention in the literature considered these techniques, an effective combination between such systems as hybrid multiple access (HMA) needs more attention. For this reason, this paper devotes to proposing two different scenarios of HMA using multi-user OFDM (MU-OFDM) and power domain (PD) NOMA for downlink transmission to overcome the limitations of both Orthogonal Multiple Access (OMA) and NOMA. Due to the randomness of a wireless channel, different user grouping strategies and dynamic power allocation (DPA) strategies are employed to satisfy users’ requirements. The proposed systems give high flexibility utilizing bitrate allocation and user fairness. System results show superior performance to traditional OMA and NOMA systems. The achieved variety of fairness is helpful for the diversity of applications which is a principal requirement for beyond-fifth-generation (B5G) networks. The next step in this analysis is to enhance the proposed systems’ spectral efficiency (SE) by introducing beamforming for massive Multiple-Input Multiple-Output (MIMO) systems. The bit error rate (BER) result of the proposed system achieves almost the same error floor with a benefit of approximately 10 dB signalto-noise ratio (SNR) when 90 resource blocks (RBs) are used.  

Developing A Novel Load Balancing Approach for Mitigating Resource Contention in Multi-Cloud Environments

In the rapidly evolving landscape of cloud computing, the management of resources and load balancing across multiple cloud environments presents significant challenges. This paper proposes a novel approach to address resource contention issues in multi-cloud environments through the development of an innovative load-balancing mechanism. By leveraging advanced algorithms and dynamic resource allocation strategies, our approach aims to effectively distribute workloads across diverse cloud infrastructures, thereby optimizing resource utilization and mitigating contention. Through extensive experimentation and evaluation, we demonstrate the effectiveness and efficiency of our proposed solution in enhancing performance and resilience in multi-cloud deployments. Our findings underscore the importance of tailored load-balancing mechanisms in enabling scalable and reliable operations in complex cloud environments.

Dynamic network-aware container allocation in Cloud/Fog computing with mobile nodes

The massive adoption of movable devices, created a new use cases such as Internet of Things (IoT), autonomous vehicles, spacecraft computing, etc. Not only end users, but also computing infrastructures can change their location. The applications are composed of many interdependent microservices with specific resource requirements. Latest state of the art in Cloud/Fog infrastructures is considering only the computational and network (latency, bandwidth) resources during scheduling of microservices, but in a static way. The network variability when the nodes are moving in space is not considered. This leads to increased total latency, hindering the Quality of Service (QoS) and network costs. Many researchers are focused only on a theoretical level. This paper proposes a novel technique for network-aware dynamic allocation of interdependent microservices on moving infrastructure nodes, applicable in practice. It is composed of a generic MILP optimization model and implementation in a Cloud/Fog platform. Several examples with sample Edge-Native application are obtained. The results show reduction in the total end-to-end network latency compared to the latest state of the art.

A dynamic ventilation strategy for industrial buildings based on weight factors

In industrial buildings, pollution sources are often multi-point and unstable. This study proposes a dynamic exhaust strategy (DES) that aims to balance energy utilization efficiency and system performance without the need for monitoring indoor environmental parameters. Firstly, the DES system only monitors the local pollutant concentration in each outlet to determine its "exhaust potential". Weight factors are then assigned to adjust the total exhaust flowrate, achieving real-time dynamic weight allocation (DWA) and improving the system performance in removing pollutants. Secondly, the system monitors the total pollutants removal from all outlets to enable dynamic total exhaust flowrate adjustment (DTA) to reduce energy consumption. A stochastic release model of multiple pollution sources was established in a typical thermally polluted workshop, and numerical simulations were conducted to investigate the effects of factors such as the stochastic non-uniformity of sources release, frequency and range of dynamic parameter adjustments, and energy-saving potential of the DES system. The results indicate that the DES system demonstrates increased effectiveness in pollutants removal as the randomness of the sources release increases, with a 12% improvement in capture efficiency compared to the unregulated system. Additionally, by utilizing 80% of the total exhaust flowrate to adjust it at a frequency of 5 seconds per time in the DWA process, the system can meet requirements for pollutants removal, performance, and stability. Furthermore, the co-regulation of the DWA and DTA processes can reduce energy consumption by 78% while maintaining an 81% capture efficiency compared to using the DWA alone.

Modeling the dynamic allocation problem of multi-service storage system with strategy learning

Gas storage, especially underground gas storage (UGS), plays a particularly important role in coping with supply disruptions. UGSs provide flexible services, e.g. firm storage service (FSS), interruptible storage service (ISS), and price hedging service (PHS), to their customers to harmonize the fluctuations in gas demand. At the beginning of each period, UGS has to allocate the overall capacity for different services for market auction. Allocation of available capacity is generally based on historical data and hands-on experience, which is operationally and economically inefficient. To address the problem, the paper developed a reinforcement learning model that captures the stochastic and dynamic features of the gas allocation problem. We study the optimal gas dispatching strategy under conditions where uncertain market states (e.g., demand load and price) exist in a continuous state space. The decision maker can improve their strategy by evaluating the expected rewards of storage actions in response to specific market states (or “learning” in the environment). In a 360-day numerical experiment, the model and algorithm demonstrate high efficiency in solving the problem. Meanwhile, several managerial implications regarding working gas volume (WGV) and agreed capacity are proposed to help improve the operational efficiency of UGS.

Inferred drought-induced plant allocation shifts and their impact on drought legacy at a tropical forest site.

While droughts predominantly induce immediate reductions in plant carbon uptake, they can also exert long-lasting effects on carbon fluxes through associated changes in leaf area, soil carbon, etc. Among other mechanisms, shifts in carbon allocation due to water stress can contribute to the legacy effects of drought on carbon fluxes. However, the magnitude and impact of these allocation shifts on carbon fluxes and pools remain poorly understood. Using data from a wet tropical flux tower site in French Guiana, we demonstrate that drought-induced carbon allocation shifts can be reliably inferred by assimilating Net Biosphere Exchange (NBE) and other observations within the CARbon DAta MOdel fraMework. This model-data fusion system allows inference of optimized carbon and water cycle parameters and states from multiple observational data streams. We then examined how these inferred shifts affected the duration and magnitude of drought's impact on NBE during and after the extreme event. Compared to a static allocation scheme analogous to those typically implemented in land surface models, dynamic allocation reduced average carbon uptake during drought recovery by a factor of 2.8. Additionally, the dynamic model extended the average recovery time by 5 months. The inferred allocation shifts influenced the post-drought period by altering foliage and fine root pools, which in turn modulated gross primary productivity and heterotrophic respiration for up to a decade. These changes can create a bust-boom cycle where carbon uptake is enhanced some years after a drought, compared to what would have occurred under drought-free conditions. Overall, allocation shifts accounted for 65% [45%-75%] of drought legacy effects in modeled NBE. In summary, drought-induced carbon allocation shifts can play a substantial role in the enduring influence of drought on cumulative land-atmosphere CO2 exchanges and should be accounted for in ecosystem models.

Online dynamic multi-user computation offloading and resource allocation for HAP-assisted MEC: an energy efficient approach

Nowadays, the paradigm of mobile computing is evolving from a centralized cloud model towards Mobile Edge Computing (MEC). In regions without ground communication infrastructure, incorporating aerial edge computing nodes into network emerges as an efficient approach to deliver Artificial Intelligence (AI) services to Ground Devices (GDs). The computation offloading and resource allocation problem within a HAP-assisted MEC system is investigated in this paper. Our goal is to minimize the energy consumption. Considering the randomness and dynamism of the task arrival of GDs and the quality of wireless communication, stochastic optimization techniques are utilized to transform the long-term dynamic optimization problem into a deterministic optimization problem. Subsequently, the problem is further decomposed into three sub-problems which can be solved in parallel. An online Energy Efficient Dynamic Offloading (EEDO) algorithm is proposed to address these problems. Then, we conduct the theoretical performance analysis for EEDO. Finally, we carry out parameter analysis and comparative experiments, demonstrating that the EEDO algorithm can effectively reduce system energy consumption while maintaining the stability of the system.

Desktop as a Service using Local Computers

Abstract: Desktop-as-a-Service (DaaS) has gained prominence as a cloud-based solution for delivering desktop environments to users. However, traditional DaaS models often rely on remote data centers, posing concerns regarding data security, latency, and dependence on external infrastructure. In this paper, we propose a paradigm shift by introducing a novel approach to DaaS that leverages the untapped potential of local computing resources.Our system harnesses the idle computing power available within the user's local environment, including desktops, laptops, and servers, to create a distributed DaaS platform. By integrating virtualization technologies and dynamic resource allocation strategies, we enable the provisioning of desktop environments ondemand, effectively reducing the reliance on centralized data centers. Key components of our system include a centralized management platform for orchestrating resource allocation and workload distribution, virtualization techniques for isolating and managing desktop instances, and robust security measures to safeguard sensitive data. Through experimental validation, we demonstrate the feasibility and performance of our approach, showcasing improvements in data privacy, latency reduction, and resource utilization compared to traditional DaaS models. Our research presents a significant step towards decentralized and resilient computing ecosystems, offering users greater control over their desktop environments while maintaining the scalability and accessibility inherent to DaaS paradigms

Analysis of Beijing Traffic Violations Based on the BERT-CRF Model

Traffic violations are a major cause of traffic accidents, yet current research falls short in comprehensively analysing these violations and the  named entity method fails to extract the name of traffic violation events from records, thereby lacking in providing guidance for managing urban traffic violations. By expanding the People’s Daily dataset from 71,456 words to 95,291 words, the BERT-CRF (Bidirectional Encoder Representations from Transformers-Conditional Random Field) model achieves an accuracy rate of 88.53%, a recall rate of 92.90% and an F1 score of 90.66%, successfully identifying event, time and location named entities within traffic violations. The data of traffic violations is then enhanced through forward geocoding and the Bayesian formula, and traffic violations are analysed from time, space, administrative region, gender and weather, to provide support for the dynamic allocation of law enforcement forces on traffic scenes and the precise management oftraffic violations.

Modeling Dynamic (De)Allocations of Local Memory for Translation Validation

End-to-End Translation Validation is the problem of verifying the executable code generated by a compiler against the corresponding input source code for a single compilation. This becomes particularly hard in the presence of dynamically-allocated local memory where addresses of local memory may be observed by the program. In the context of validating the translation of a C procedure to executable code, a validator needs to tackle constant-length local arrays, address-taken local variables, address-taken formal parameters, variable-length local arrays, procedure-call arguments (including variadic arguments), and the alloca () operator. We provide an execution model, a definition of refinement, and an algorithm to soundly convert a refinement check into first-order logic queries that an off-the-shelf SMT solver can handle efficiently. In our experiments, we perform blackbox translation validation of C procedures (with up to 100+ SLOC), involving these local memory allocation constructs, against their corresponding assembly implementations (with up to 200+ instructions) generated by an optimizing compiler with complex loop and vectorizing transformations.

Deep Reinforcement Learning based channel allocation (DRLCA) in Cognitive Radio Networks

The Spectrum is a scarce resource in wireless networks, hence these resources need to be perfectly channelized for their better utilization. By the study of the previous decades, it is found that the spectrum is colossally underutilized and the main reason for the underutilization found out to be the policies that are fixed and not dynamic. The dynamic spectrum allocation of frequency bands may overcome this problem. Cognitive radio provides an important concept that can used to solve the problem of underutilization of spectrum. Reinforcement learning is a key technique that is widely used to learn the spectrum allocation behaviour and maximize the system’s efficiency. Therefore, in this work, we have designed and developed a Reinforcement learning-based model to allocate the channels among secondary users. Also, a Deep Reinforcement learning-based channel allocation algorithm (DRLCA) has been proposed. The proposed DRLCA is compared with existing JPCRL [1]. In our algorithm, the Python libraries were used for simulation. From the simulation results and analysis, it is found that the DRLCA outperforms the JPCRL in terms of channel utilization by 5%.

Optimized Decentralized Swarm Communication Algorithms for Efficient Task Allocation and Power Consumption in Swarm Robotics

Unanimous action to achieve specific goals is crucial for the success of a robotic swarm. This requires clearly defined roles and precise communication between the robots of a swarm. An optimized task allocation algorithm defines the mechanism and logistics of decision-making that enable the robotic swarm to achieve such common goals. With more nodes, the traffic of messages that are required to communicate inside the swarm relatively increases to maintain decentralization. Increased traffic eliminates real-time capabilities, which is an essential aspect of a swarm system. The aim of this research is to reduce execution time while retaining efficient power consumption rates. In this research, two novel decentralized swarm communication algorithms are proposed, namely Clustered Dynamic Task Allocation–Centralized Loop (CDTA-CL) and Clustered Dynamic Task Allocation–Dual Loop (CDTA-DL), both inspired by the Clustered Dynamic Task Allocation (CDTA) algorithm. Moreover, a simulation tool was developed to simulate different swarm-clustered communication algorithms in order to calculate the total communication time and consumed power. The results of testing the proposed CDTA-DL and CDTA-CL against the CDTA attest that the proposed algorithm consumes substantially less time. Both CDTA-DL and CDTA-CL have achieved a significant speedup of 75.976% and 54.4% over CDTA, respectively.

Analysis of the Impact of Different Improvement Methods Based on YOLOV8 for Weed Detection

In response to the issues of missed detection, false positives, and low recognition rates for specific weed species during weed detection, a YOLOv8-based improved weed detection algorithm named EDS-YOLOv8 is proposed. Improvements were made in three main aspects. First, the YOLOv8 backbone network was enhanced with EfficientViT and RepViT architectures to improve the detection capability of dense-type weeds. Second, different attention mechanisms were added, such as SimAM and EMA, to learn 3D weights and achieve full fusion of features. BiFormer was introduced for dynamic sparse attention and resource allocation. Third, significant module improvement involved introducing dynamic snake convolution into the C2f module to further enhance detection capabilities for deformable objects, especially needle-shaped weeds. The improved model is validated on the established weed dataset. The results show that combining the original backbone network with dynamic snake convolutions yields the highest performance improvement. Precision, recall, mAP (0.5), and mAP (0.5:0.95) are improved by 5.6%, 5.8%, 6.4%, and 1%, respectively, and ablation experiments on the effects of the three improvement methods on model performance show that using EfficientViT as the backbone network while simultaneously improving the crucial module and adding the SimAM attention mechanism effectively enhances the model’s performance. Precision, recall, mAP (0.5), and mAP (0.5:0.95) are improved by 6%, 5.9%, 6.4%, and 0.7%, respectively.

Reconfiguration of distribution network-based wind energy resource allocation considering time-varying load using hybrid optimization method

This study proposed a method for optimizing a radial distribution network by integrating wind turbine allocation, considering fluctuating load demands, through the use of a hybrid Grey Wolf Optimizer-Genetic Algorithm (HGWOGA). This approach aims to decrease the network’s energy loss costs. By incorporating genetic algorithm techniques, the method enhances the Grey Wolf Optimizer’s efficiency, speeding up convergence and avoiding local optima. The strategy determines the network’s open lines and the placement and capacity of wind turbines, adhering to radiality and operational constraints. It categorizes load levels into residential, commercial, and industrial, providing a comprehensive analysis of energy losses and their cost implications under various scenarios, including constant and dynamic loads. The study suggests that managing time-varying demand offers a more accurate depiction of network challenges, enabling effective reconfiguration throughout different demand phases. Moreover, HGWOGA demonstrates its ability to find the global optimum efficiently, even with reduced population sizes—a feat not achievable with the Grey Wolf Optimizer alone. Comparative analyses reveal HGWOGA’s effectiveness in curbing network energy loss costs better than previous methodologies. By simultaneously applying network reconfiguration and wind turbine allocation, as opposed to merely reconfiguring the network, this approach notably reduces power loss, diminishes the cost of losses, and enhances the voltage profile. This synergistic strategy leverages the dynamic allocation of wind turbines within the network, optimizing energy flow and distribution efficiency, thereby offering a substantial improvement over conventional network reconfiguration methods.

Dynamic task allocation of hybrid flow shop for machines in parallel with different speeds based on an MLD prediction model

ABSTRACT The current hybrid flow shop task allocation usually assumes a static manufacturing environment, which cannot effectively handle uncertain events in the production process. To address this, the dynamic task allocation problem for machines in parallel with different speeds is studied, and a Mixed Logical Dynamical (MLD) model for predicting the state of the production system is established. A dynamic allocation method based on Model Predictive Control (MPC) is proposed. The proposed novel method integrates dynamic model with rolling optimization to better deal with uncertain events in production process by decomposing the overall planning problem into smaller local planning models. Numerical results show that the proposed method outperform the traditional global planning method and rule-based allocation method in terms of time and job processing rate. In addition, through the Plant Simulation software, the simulation results are consistent with the numerical results, which fully proves the effectiveness of the proposed method.

Dynamic resource allocation for energy-efficient downlink NOMA systems in 5G networks

Non-Orthogonal Multiple Access (NOMA) is a promising energy-efficient technology designed to satisfy the demands of future networks by efficiently sharing radio resources. In NOMA, the same radio resource is simultaneously assigned to two users at different power levels based on the NOMA-power domain. Resource allocation in NOMA presents a non-convex challenge, characterized as a non-deterministic polynomial (NP-hard) problem. This involves user and channel assignment and power allocation, making it an extraordinarily complex task to achieve an optimal solution. In this work, Simulated Annealing (SA) is proposed as an optimization technique for resource allocation in an energy-efficient downlink NOMA system. This resource allocation scheme addresses user and channel assignment, as well as power allocation, using SA as an efficient standalone approach to maximize energy efficiency in NOMA. SA is utilized to execute the assignment of users to channels, distribute the necessary power for each channel, and determine the power ratio among users sharing the same channel. The results obtained demonstrate a significant improvement in energy efficiency, outperforming the existing numerical methods by 22 %. The proposed SA scheme not only achieves a close optimal solution but also in less computational time, offering sufficient reliability in terms of energy efficiency enhancement when compared to the existing numerical method.

Optimization of financial data processing strategy based on LSTM-GCN

In the process of training LSTM-GCN mixed model with labeled data, a common practice is to divide the data set into a training set and a validation set, and cross-validation techniques are frequently used to ensure the effective partitioning of data. The training phase mainly involves the selection of optimization methods such as gradient descent to update the model parameters, so that the model can correctly identify the feature patterns in the data. In this process, we first define a loss function to quantify the difference between the model's predicted value and the actual value. For example, when dealing with classification problems, the cross-entropy loss function is often used; Whereas in regression problems, the mean square error loss function or the absolute error loss function is more common. Then, the loss gradient of each parameter is calculated by backpropagation algorithm, and optimization strategies such as gradient descent are applied to adjust the parameter values. Through repeated iterations, these algorithms aim to minimize the loss function as a core goal for training the model. In addition, the details of model training, such as setting the appropriate learning rate, taking the appropriate regularization measures, and choosing the appropriate weight initialization strategy, are also key steps that cannot be ignored. This paper conforms to the research and development trend of the industry, focusing on how to use the LSTM-GCN model to capture temporal and spatial characteristics, process multi-modal data, process nonlinear data and dynamic weight allocation, etc., providing new ideas for Internet finance companies in data processing. This model combines the advantages of LSTM and GCN, and has prominent advantages such as timeliness, strong representation learning ability, associability, and interpretability. It can process both time series data and graphic data, and can analyze and mine the correlation and regularity in large-scale time series data, so as to process financial data more accurately. It greatly reduces the risk of investment decision.