Resource Allocation In Networks Research Articles

Anti-bump control for switched systems with decentralised adaptive event-triggering

A control system may exist bumps during mode transitions and discontinuous control input would seriously affect performance. In this paper, the anti-bump (or bumpless) switching control for triggered and switched systems under injection attacks is studied. This control method combines decentralised adaptive event-triggering mechanism (DAETM) and anti-bump condition. The main work is as follows: First, a DAETM is considered to allocate network resources properly and resist data injection attacks. Then, a data buffer is adopted to consolidate data from decentralised channels for ensuring timely utilisation. In addition, a controller gain condition is presented to achieve the bumpless switching between subsystems. Moreover, by adopting piecewise Lyapunov function method, sufficient conditions are obtained for triggered switched systems to be exponentially stable and anti-bump under injection attacks. Finally, the design method effectiveness is verified by actual circuit simulation.

Layout design of densest weakly coupled multi-core fibers to minimize the network blocking rate

The suppression of inter-core crosstalk (IC-XT) that affects each lightpath is crucial for resource allocation in space-division multiplexing elastic optical networks (SDM-EONs) with multi-core fibers (MCFs). Resource allocation approaches that limit the simultaneous use of adjacent cores in the same frequency band to the MCFs composing each lightpath have been widely adopted to suppress IC-XT. However, in principle, such methods are inefficient because they cannot fully utilize all cores. This study examines the core density from the perspective of the core layout in weakly coupled MCFs and the IC-XT suppression requirement. The densest MCF layout maximizes the network capacity while restricting the amount of IC-XT within the tolerance threshold for each lightpath. Specifically, we propose an XT-free condition, maintaining the IC-XT to each lightpath within the acceptable tolerance level. In addition, we evaluated numerous MCFs that satisfy or do not satisfy the XT-free condition with various network topologies and cladding diameters. This evaluation also validates the IC-XT reduction performance of the proposed framework compared with that of the conventional resource-allocation approach. Here, we incorporate our indirect IC-XT calculation method that affects lightpaths from other cores via its nearest cores, which was overlooked in the resource allocation problem. Based on these comprehensive examinations, we propose a method to determine the densest core layout for a given network topology and route and modulation format selection algorithm.

Game-based computation offloading and resource allocation in stochastic geometry-modeling vehicular networks

Game-based computation offloading and resource allocation in stochastic geometry-modeling vehicular networks

An efficient Protocol for Enhanced Flynet Communication in Presence of Partitioning

This paper presents an adaptive fault-tolerant resource allocation protocol tailored for FlyNet, a dynamic aerial network characterized by its mobility and three-dimensional operational space. Addressing the challenges of network partitioning and resource allocation in aerial networks, the proposed algorithm dynamically adjusts to the network's changing topology, ensuring consistent and efficient resource management. Through robust fault tolerance mechanisms, the protocol enhances FlyNet's reliability, maintaining seamless communication and optimal resource utilization even amid node mobility and disruptions. Simulation results demonstrate the algorithm's effectiveness in adapting to dynamic environments, maximizing resource utilization, and minimizing communication delays.

Improving the efficiency of resource allocation in educational content delivery networks using the 1C:ESB integration bus

Learning Management Systems (LMS) play a key role in the educational process by providing students with various educational content and instructors with tools to monitor student knowledge and assess progress. However, as the number of students increases, the load on data transmission channels increases, which can lead to a decrease in the quality of learning. To solve this problem, Content Delivery Networks (CDN) are used to speed up the loading of websites and reduce the load on servers. CDN ensure the availability and security of websites and improve their scalability (resistance to smooth load growth) by distributing content across a network of servers around the world, allowing users to access content faster and more efficiently.Integration data exchange bus (Enterprise Service Bus, ESB) can be an effective tool for optimizing CDN content distribution algorithm. It allows integration of different systems and applications, enabling data exchange between them. This is particularly useful in the context of distance learning, which requires fast and reliable delivery of educational content, feedback, learning analytics, etc. Implementing an integration bus of data exchange with LMS-based distance learning system can be a key factor in ensuring an efficient and quality educational process. The article considers the properties and characteristics of the integration bus and justifies the use of the integration bus to optimize the algorithm of CDN content distribution, in particular in LMS.

Contribution-Based Resource Allocation for Effective Federated Learning in UAV-Assisted Edge Networks.

This paper considers UAVs as edge computing nodes and investigates a novel network resource allocation method for federated learning within a three-layer wireless network architecture containing cloud, edges (UAVs), and clients. To address the issue of fair bandwidth resource allocation among clients participating in federated learning, a contribution calculation strategy based on the Shapley value (SV) used as the weight for model aggregation is proposed. On this basis, a client selection and wireless resource allocation method based on model contribution is further designed. By reducing the training and aggregation frequency of the low-contribution clients during the asynchronous aggregation phase, the limited bandwidth resources are allocated to high-contribution clients, thus improving the convergence speed and accuracy of the global model. Simulation experiments demonstrate that the proposed method can significantly reduce the system delay and total energy consumption with gains between 15% and 50% while also improving the final accuracy of the global model by 0.3% and 2% on both short-term and long-term perspectives, respectively.

On mitigating reactive jamming with dynamic resource allocation in optical IRS and UAV-assisted FSO-based networks

Free space optics (FSO) offers a promising opportunity to enhance next-generation network’s capacity with its unlicensed spectrum and wide bandwidth. However, jamming attacks, coupled with inherent anomalies in the FSO-based channel, threaten the performance of these networks. This is especially problematic for security-sensitive applications that demand a resilient communication infrastructure. To address this issue, optical intelligent reflecting surfaces (IRS) and unmanned aerial vehicles (UAVs) can provide promising solutions. This work introduces an efficient approach for mirror element assignment in UAV-assisted FSO-based networks, aimed at mitigating reactive jamming attacks while satisfying users’ quality-of-service (QoS) requirements. To ensure network reliability, we formulate an optimization problem that enhances overall network performance by simultaneously allocating resources such as mirror elements with awareness of jamming attacks. The formulated optimization problem is a binary linear programming problem, which is generally NP-hard. To address this, we introduce a batch-based sequential fixing linear programming procedure called the Reactive Jamming-Aware Mirror Element Allocation (RJA-MEA) scheme. This scheme optimally assigns mirror elements to satisfy the users’ rate demands. In this paper, the performance of the RJA-MEA scheme is compared with reference schemes such as Reactive Jamming Unaware-Mirror Element Allocation (RJU-MEA), Reactive Jamming-Aware Equal Mirror Element Allocation (RJA-EMEA), and Reactive Jamming Unaware-Equal Mirror Element Allocation (RJU-EMEA) schemes. The simulation results reveal that the proposed RJA-MEA scheme surpasses existing reference schemes, thereby significantly improving the overall network sumrate performance.

Power-Domain Interference Graph Estimation for Multi-hop BLE Networks

Traditional wisdom for network management allocates network resources separately for the measurement and communication tasks. Heavy measurement tasks may compete limited resources with communication tasks and significantly degrade overall network performance. It is therefore challenging for the interference graph, deemed as incurring heavy measurement overhead, to be used in practice in wireless networks. To address this challenge in wireless sensor networks, our core insight is to use power as a new dimension for interference graph estimation (IGE) such that IGE can be done simultaneously with the communication tasks using the same frequency-time resources. We propose to marry power-domain IGE with concurrent flooding to achieve simultaneous measurement and communication in BLE networks, where the power linearity prerequisite for power-domain IGE holds naturally true in concurrent flooding. With extensive experiments, we conclude the necessary conditions for the power linearity to hold and analyze several non-linearity issues of power related to hardware imperfections. We design and implement network protocols and power control algorithms for IGE in multi-hop BLE networks and conduct experiments to show that the marriage is mutually beneficial for both IGE and concurrent flooding. Furthermore, we demonstrate the potential of IGE in improving channel map convergence and convergecast in BLE networks.

Research and Application of Emergency Logistics Resource Allocation Algorithm based on Supply Chain Network

The ”Fuzzy-Enhanced for Emergency Logistics Resource Allocation in Supply Chain Networks (FEM-ELRAS)” presents a novel approach to optimizing emergency logistics and resource allocation in supply chain networks, especially during critical disaster response scenarios. This research integrates fuzzy logic with the improve Multi Agent Genetic Algorithm (MAGA), creating a more adaptive and efficient framework capable of handling the uncertainties and complexities inherent in emergency situations. FEM-ELRAS employs fuzzy decision variables to represent ambiguous and fluctuating parameters like demand at disaster sites, supply availability, and variable transportation conditions. It incorporates a fuzzy inference system, utilizing expert-derived rules to guide the allocation process amidst uncertain and rapidly changing conditions. The algorithm’s evaluation mechanism is enhanced with fuzzy logic, offering a refined assessment of solution effectiveness, balancing multiple logistical objectives such as minimizing response time, optimizing costs, and maximizing resource utilization and delivery precision. Moreover, fuzzy logic principles are integrated into the genetic algorithm’s operators, enabling more context-sensitive and flexible solution adaptations. FEM-ELRAS is particularly designed to navigate the trade-offs between different logistical goals in emergency scenarios, making it a robust tool for decision-makers in disaster management. Its application promises significant improvements in emergency response efficiency, showcasing a step forward in the field of emergency logistics and supply chain management.

A hierarchical adaptive federated reinforcement learning for efficient resource allocation and task scheduling in hierarchical IoT network

The increasing demand for processing numerous data from IoT devices in a hierarchical IoT network drives researchers to propose different resource allocation methods in the edge hosts efficiently. Traditional approaches often compromise on one of these aspects: either prioritizing local decision-making at the edge, which lacks global system insights or centralizing decisions in cloud systems, which raises privacy concerns. Additionally, most solutions do not consider scheduling tasks at the same time to effectively complete the prioritized task accordingly. This study introduces the hierarchical adaptive federated reinforcement learning (HAFedRL) framework for robust resource allocation and task scheduling in hierarchical IoT networks. At the local edge host level, a primal–dual update based deep deterministic policy gradient (DDPG) method is introduced for effective individual task resource allocation and scheduling. Concurrently, the central server utilizes an adaptive multi-objective policy gradient (AMOPG) which integrates a multi-objective policy adaptation (MOPA) with dynamic federated reward aggregation (DFRA) method to allocate resources across connected edge hosts. An adaptive learning rate modulation (ALRM) is proposed for faster convergence and to ensure high performance output from HAFedRL. Our proposed HAFedRL enables the effective integration of reward from edge hosts, ensuring the alignment of local and global optimization goals. The experimental results of HAFedRL showcase its efficacy in improving system-wide utility, average task completion rate, and optimizing resource utilization, establishing it as a robust solution for hierarchical IoT networks.

Cross-User Dependent Task Offloading and Resource Allocation in Spatial-Temporal Dynamic MEC Networks

Cross-User Dependent Task Offloading and Resource Allocation in Spatial-Temporal Dynamic MEC Networks

Matching Combined Heterogeneous Multi-Agent Reinforcement Learning for Resource Allocation in NOMA-V2X Networks

Matching Combined Heterogeneous Multi-Agent Reinforcement Learning for Resource Allocation in NOMA-V2X Networks

Resource Allocation for Dynamic Platoon Digital Twin Networks: A Multi-Agent Deep Reinforcement Learning Method

Resource Allocation for Dynamic Platoon Digital Twin Networks: A Multi-Agent Deep Reinforcement Learning Method

Efficient Multitask Reinforcement Learning Without Performance Loss.

We propose an iterative sparse Bayesian policy optimization (ISBPO) scheme as an efficient multitask reinforcement learning (RL) method for industrial control applications that require both high performance and cost-effective implementation. Under continual learning scenarios in which multiple control tasks are sequentially learned, the proposed ISBPO scheme preserves the previously learned knowledge without performance loss (PL), enables efficient resource use, and improves the sample efficiency of learning new tasks. Specifically, the proposed ISBPO scheme continually adds new tasks to a single policy neural network while completely preserving the control performance of previously learned tasks through an iterative pruning method. To create a free-weight space for adding new tasks, each task is learned through a pruning-aware policy optimization method called the sparse Bayesian policy optimization (SBPO), which ensures efficient allocation of limited policy network resources for multiple tasks. Furthermore, the weights allocated to the previous tasks are shared and reused in new task learning, thereby improving sample efficiency and the performance of new task learning. Simulations and practical experiments demonstrate that the proposed ISBPO scheme is highly suitable for sequentially learning multiple tasks in terms of performance conservation, efficient resource use, and sample efficiency.

Joint Fronthaul Load Balancing and Computation Resource Allocation in Cell-Free User-Centric Massive MIMO Networks

Joint Fronthaul Load Balancing and Computation Resource Allocation in Cell-Free User-Centric Massive MIMO Networks

Evaluating a Multidisciplinary Model for Managing Human Uncertainty in 5G Cyber–Physical–Social Systems

This paper presents a comprehensive evaluation of the previously introduced multidisciplinary model to quantify human uncertainty (MMtQHU) within a realistic 5G-enabled cyber–physical–social systems (CPSS) environment. The MMtQHU, which integrates human, social, and environmental factors into CPSS modeling, is applied to the Ingolstadt traffic scenario (InTAS), a detailed urban simulation reflecting high-traffic conditions. By modeling unpredictable driver behaviors, such as deviations from optimal routes, the study assesses the model’s effectiveness in managing human-induced uncertainties in vehicle-for-hire (VFH) applications. The evaluation shows that human uncertainty significantly impacts 5G network resource allocation and traffic dynamics. A comparative analysis of traditional resource allocation methods reveals their limitations in handling the dynamic nature of human behavior. These findings underscore the necessity for advanced, adaptive strategies, potentially leveraging artificial intelligence and machine learning to enhance the resilience and efficiency of 5G networks in CPSS environments. The study offers valuable insights for future advancements in robust and adaptive 5G infrastructure by highlighting the critical role of integrating human behavior into CPSS models.

Examining Care Network Characteristics in Older Adults' Relocation to Residential Care Settings.

When older adults face increasing care needs or limited support, remaining safely and comfortably at home becomes challenging. Extant research has primarily concentrated on characteristics of the older adult or their primary caregiver on nursing home admission. This study examines the risk of older adults transitioning to residential care (e.g., assisted living, nursing home), focusing on the influence of their care network or involvement of multiple helpers. Using the National Health and Aging Trends Study, we conducted competing risk regression models that account for mortality, following 7,085 initially community-dwelling older adults across Rounds 1-9 (2011-2019). We assessed network composition, size, shared tasks, and the number of in-network specialists or generalists while controlling for individual sociodemographic and health factors. Individuals with care networks that shared medical tasks had the highest risk of moving to a residential care setting, followed by those sharing household tasks. Conversely, shared mobility or self-care and transportation responsibilities were associated with lower risks. Having more generalists, but not specialists, increased the risk. Larger networks were associated with heightened risk, although having close family members like a spouse was protective. The findings underscore that care network characteristics are critical to older adults' ability to age in place. Specifically, older adults with larger networks, lacking a spouse or child, and providing complex care are at greater risk for relocating. Understanding care networks can guide interventions related to care network coordination and resource allocation to help avoid or postpone a residential care move.

Multi-objective network resource allocation method based on fractional PID control

In this paper, a fractional proportional–integral–derivative (PID) distributed optimization algorithm is proposed to solve the network resource allocation problem. The algorithm combines fractional calculus and the concept of PID control, which improves the convergence rate and increases the freedom, flexibility and potential with multiple parameters compared with the existing algorithms. Meanwhile, the results of simulation study verified the efficiency and superiority of the algorithm.

Enhancing IoT connectivity through spectrum sharing in 5G networks

The integration of the Internet of Things (IoT) into high-performance devices and monitoring systems, spanning domains such as smart building, e-health care, and smart agriculture, necessitates a critical emphasis on advancing mobile communications through efficient spectrum utilization. This research addresses pivotal challenges within agricultural IoT applications, specifically focusing on the substantial decline in spectrum efficiency observed with the increasing escalation of network bandwidth. Acknowledging the absence of comprehensive reviews on 5G resource allocation strategies in the existing literature, our study aims to contribute to a nuanced understanding of their implications for service quality. The identified research gaps underscore an urgent need for heightened efforts to optimize resource allocation in 5G networks. This investigation delves into the intricacies of spectrum sharing and real-time analysis techniques within the 5G and beyond network, with a targeted focus on augmenting agricultural IoT services. Three distinct models, namely (i) Non-Priority Algorithm (NPA), (ii) Reserved Channel Algorithm (RCA) - No Permanent Channels, and (iii) Reserved Channel Algorithm (RCA) - Permanent Channels, were meticulously designed and simulated for Agricultural IoT application scenarios. The methodology encompasses the comprehensive evaluation of performance metrics, including call blocking, termination, and handover, to strategically identify and allocate spectrum resources effectively. The research endeavors to address ongoing challenges pertaining to effective communication, standardization, and data management for diverse 5G IoT devices. In light of these persisting concerns, the study not only seeks to enhance the overall efficiency of 5G IoT networks but also proposes innovative perspectives on intelligent and ingenious spectrum allocation techniques. The anticipated outcomes pledge to optimize the utilization of limited spectrum through novel spectrum-sharing strategies, thereby contributing to the advancement of 5G networks and bolstering agricultural IoT devices and services.

Deep Deterministic Policy Gradient-Based Resource Allocation Considering Network Slicing and Device-to-Device Communication in Mobile Networks.

Next-generation mobile networks, such as those beyond the 5th generation (B5G) and 6th generation (6G), have diverse network resource demands. Network slicing (NS) and device-to-device (D2D) communication have emerged as promising solutions for network operators. NS is a candidate technology for this scenario, where a single network infrastructure is divided into multiple (virtual) slices to meet different service requirements. Combining D2D and NS can improve spectrum utilization, providing better performance and scalability. This paper addresses the challenging problem of dynamic resource allocation with wireless network slices and D2D communications using deep reinforcement learning (DRL) techniques. More specifically, we propose an approach named DDPG-KRP based on deep deterministic policy gradient (DDPG) with K-nearest neighbors (KNNs) and reward penalization (RP) for undesirable action elimination to determine the resource allocation policy maximizing long-term rewards. The simulation results show that the DDPG-KRP is an efficient solution for resource allocation in wireless networks with slicing, outperforming other considered DRL algorithms.