Allocation Algorithm Research Articles

Capacity performance of uplink NOMA system with 2K+1 users over Fisher-Snedecor composite fading channels

In this paper, we study an uplink power-domain non-orthogonal multiple access (PD-NOMA) system, in which 2K+1 users are served. The user clustering process based on High-High/High-Low algorithm precedes the utilization of the data-rate based power allocation algorithm. Channels are characterized by Fisher-Snedecor composite fading model interpreted as model with a high level of generality. The influence of different fading/shadowing channel conditions, number of users and their positions is portrayed through the numerical results of data sum rate of the studied PD-NOMA system.

Winner-Leader-Follower a Novel Hit Allocation Algorithm for Pixel Detectors

Winner-Leader-Follower a Novel Hit Allocation Algorithm for Pixel Detectors

A Combined Topology Formation and Rate Allocation Algorithm for Aeronautical Ad Hoc Networks

This paper aims to address the problem of providing internet connectivity to aircraft flying above the ocean without using satellite connectivity given that there is no ground network infrastructure in the ocean. Is it possible to guarantee a minimum flow rate to each aircraft flying over an ocean by forming an aeronautical ad hoc network and connecting that network to internet via a set of limited number of ground base stations at the coast as anchor points? We formulated the problem as mixed-integer-linear programming (MILP) to maximize the number of aircraft with flow data rate above a certain threshold. Since this is a multi-commodity flow problem and at least NP-complete, we propose a two-phase heuristic algorithm to efficiently form topology and assign flows to each aircraft by maximizing the minimum flow. The performance of the heuristic algorithm is evaluated over the North Atlantic Corridor, heuristic performs only 8% less than the optimal result with low densities. In high network densities, the connectivity percentage changes from 70% to 40% under 75 Mbps data rate threshold. Furthermore, the connectivity percentage is investigated for different network parameters such as altitude and compared with upper and lower bounds and a baseline algorithm.

5G Network Performance Optimization Strategies for Smart Grids

Abstract In this paper, a network slice resource allocation algorithm based on delay sensing is proposed for the problems of service delay and QoS reduction, and resource utilization reduction caused by unreasonable network slice resource allocation. MIMO technology is being studied to establish a communication system that supports multiple inputs and outputs for channels. A resource preallocation method for 5G edge computing is proposed by reasonably allocating resources in the 5G edge computing environment. Then, the performance of the 5G network is optimized, and the optimized network performance is analyzed based on case studies. The results show that the buffer depth of the optimized 5G network is reduced by 1.25, the network delay is reduced by 0.02s, and the network throughput is significantly improved. This study provides the theoretical basis and technical support for the further development of a smart grid in a 5G environment.

A holistic mathematical cyber security model in fog resource management in computing environments

In the evolving landscape of computing environments, fog computing has emerged as a pivotal technology for handling the increasing demands of Internet of Things (IoT) applications. Fog computing, with its decentralized approach, brings computational power closer to IoT devices, reducing latency and bandwidth constraints. However, this distributed architecture poses significant cybersecurity challenges, necessitating a holistic mathematical model to ensure the security and integrity of fog resources. This review presents a comprehensive mathematical cyber security model that is tailored to fog resource management in computing environments. The models generally encompass a wide array of security aspects, including data confidentiality, integrity, availability, and authentication. Leveraging advanced mathematical techniques, the models offer a robust framework for assessing and enhancing security in fog computing systems. The key components include threat modeling, risk assessment, and dynamic resource allocation strategies. By systematically analyzing potential threats and vulnerabilities specific to fog computing, these models assist in devising effective countermeasures and security policies. Moreover, there is a need to incorporate an adaptive resource allocation algorithm to dynamically allocate computing resources based on security priorities and real-time threat assessments. This review demonstrates the effectiveness of the mathematical cyber security model in bolstering the resilience of fog computing environments. The review highlights the need for safeguarding the next generation of fog computing systems against cyber threats, fostering the continued growth and adoption of transformative technology.

(RE)VISÃO DAS CAPACIDADES DINÂMICAS: ORIGENS E DESDOBRAMENTOS FUTUROS

ABSTRACT This paper aims to map out how the field of dynamic capabilities has developed since the seminal studies by Teece et al. (1997) and Eisenhardt and Martin (2000). We identified 10,838 papers and used the Latent Dirichlet Allocation algorithm for topic modeling. We conducted two analyses: the first was based on the temporality and characteristics of the studies; the second was interpretative and based on the network of theoretical concepts. Results indicate an approximation between the ideas of the two seminal studies, which were initially viewed as opposite. We observe a movement to value relational issues, following a collective construction path, and paying less attention to the firm itself. Overall, we were able to understand the consolidation of the dynamic capabilities field; understand the core elements involved in the development of dynamic capabilities; set out the original and current concepts of dynamic capabilities; and indicate tendencies and a possible future research agenda.

Energy-Efficient Federated Training on Mobile Device

On-device deep learning technology has attracted increasing interest recently. CPUs are the most common commercial hardware on devices and many training libraries have been developed and optimized for them. However, CPUs still suffer from poor training performance (i.e., training time) due to the specific asymmetric multiprocessor. Moreover, the energy constraint imposes restrictions on battery-powered devices. With federated training, we expect the local training to be completed rapidly therefore the global model converges fast. At the same time, energy consumption should be minimized to avoid compromising the user experience. To this end, we consider energy and training time and propose a novel framework with a machine learning-based adaptive configuration allocation strategy, which chooses optimal configuration combinations for efficient ondevice training. We carry out experiments on the popular library MNN and the experimental results show that the adaptive allocation algorithm reduces substantial energy consumption, compared to all batches with fixed configurations on off-the-shelf CPUs.

MADDPG-D2: An Intelligent Dynamic Task Allocation Algorithm Based on Multi-Agent Architecture Driven by Prior Knowledge

MADDPG-D2: An Intelligent Dynamic Task Allocation Algorithm Based on Multi-Agent Architecture Driven by Prior Knowledge

Comparison of Distributed Task Allocation Algorithms Considering Non-ideal Communication Factors for Multi-UAV Collaborative Visit Missions

Comparison of Distributed Task Allocation Algorithms Considering Non-ideal Communication Factors for Multi-UAV Collaborative Visit Missions

Quantum Based Deep Q-Network Bandwidth Resource Allocation Algorithm for UASN

Quantum Based Deep Q-Network Bandwidth Resource Allocation Algorithm for UASN

An energy-efficient resource allocation algorithm for managing on-demand services in fog-enabled vehicular ad hoc networks

An energy-efficient resource allocation algorithm for managing on-demand services in fog-enabled vehicular ad hoc networks

Adaptive Resource Allocation Algorithm for 5G Vehicular Cloud Communication

Adaptive Resource Allocation Algorithm for 5G Vehicular Cloud Communication

Data trading, power control and resource allocation algorithms for metaverse platform

Data trading, power control and resource allocation algorithms for metaverse platform

A Novel Approach for Minimizing Response Time in IoT using Adaptive Algorithm

This research offers four work and computer tool setups. The dynamic Resource Allocation Algorithm is crucial to the system. This lets you manage changing supply. Once the PWMA knows how much work is coming up, it may divide resources and plan. The Load Balancing Algorithm (LBA) distributes work evenly to avoid over- or under-utilization and it also provides access content faster via the Adaptive Caching Algorithm (ACA). The proposed system surpasses the top alternative in several domains, such as data transmission, reaction time, energy conservation, load distribution effectiveness, and recovery time from failures. This is because the suggested solution incorporates many disparate approaches. Graphs and charts are visual representations that effectively illustrate the similarities and differences between the two methodologies. The hybrid technique is especially beneficial when the workload is unpredictable and prone to fluctuations. To do this, it instructs you on the fundamentals of efficient and adaptable computer resource management.

Optimizing Task Scheduling and Resource Allocation in Computing Environments using Metaheuristic Methods

Optimizing system performance in dynamic and heterogeneous environments and the efficient management of computational tasks are crucial. This paper therefore looks at task scheduling and resource allocation algorithms in some depth. The work evaluates five algorithms: Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Firefly Algorithm (FA) and Simulated Annealing (SA) across various workloads achieved by varying the task-to-node ratio. The paper identifies Finish Time and Deadline as two key performance metrics for gauging the efficacy of an algorithm, and a comprehensive investigation of the behaviors of these algorithms across different workloads was carried out. Results from the experiments reveal unique patterns in algorithmic behaviors by workload. In the 15-task and 5-node scenario, the GA and PSO algorithms outclass all others, completing 100 percent of tasks before deadlines, Task 5 was a bane to the ACO algorithm. The study proposes a more extensive system that promotes an adaptive algorithmic approach based on workload characteristics. Numerically, the GA and PSO algorithms triumphed completing 100 percent of tasks before their deadlines in the face of 10 tasks and 5 nodes, while the ACO algorithm stumbled on certain tasks. As it is stated in the study, The above-mentioned system offers an integrated approach to ill-structured problem of task scheduling and resource allocation. It offers an intelligent and aggressive scheduling scheme that runs asynchronously when a higher number of tasks is submitted for the completion in addition to those dynamically aborts whenever system load and utilization cascade excessively. The proposed design seems like full-fledged solution over project scheduling or resource allocation issues. It highlights a detailed method of the choice of algorithms based on semantic features, aiming at flexibility. Effects of producing quantifiable statistical results from the experiments on performance empirically demonstrate each algorithm performed under various settings.

A Cross-layer Resource Allocation Algorithm for Broadband Power Line Communication OFDM Systems

A Cross-layer Resource Allocation Algorithm for Broadband Power Line Communication OFDM Systems

Dynamic Routing and Spectrum Allocation Algorithms in Elastic Optical Networks for Satellite Internet (Invited)

Dynamic Routing and Spectrum Allocation Algorithms in Elastic Optical Networks for Satellite Internet (Invited)

Data Trading, Power Control and Resource Allocation Algorithms for Metaverse Platform

Data Trading, Power Control and Resource Allocation Algorithms for Metaverse Platform

A novel resource allocation scheme for full-duplex NOMA systems with power splitting-based SWIPT

This paper introduces novel resource allocation (RA) algorithms for optimizing wireless network performance, focusing on a full-duplex (FD) base station (BS) employing non-orthogonal multiple access (NOMA) technology. Motivated by the intriguing trade-off introduced by the simultaneous wireless information and power transfer (SWIPT) scheme, in which achievable data rates are sacrificed to enhance harvested energy, this study aims to quantify the delicate equilibrium between two vital aspects meticulously: the overall achievable data rate facilitated by NOMA and FD technologies and the energy harvested via the SWIPT approach. Specifically, our focus is on jointly optimizing the enhancement of both data rate and harvested energy for downlink (DL) users where a multi-objective optimization approach is developed in the proposed RA algorithm to study the existing trade-off between these conflicting objective functions. This problem optimizes the power allocation for both the BS and the users, allocating subcarriers, and optimizing the power splitting factor for SWIPT scheme. We developed efficient iterative solutions for the intended RA problems because they involve complex mixed-integer nonlinear optimization challenges that are generally difficult to solve. The effectiveness of the proposed RA framework is demonstrated using simulation results. These results investigate whether FD MC-NOMA systems, enabled by the proposed RA algorithms, provide a significant increase in system throughput compared to both conventional half-duplex (HD) multicarrier orthogonal multiple access (MC-OMA) systems and HD MC-NOMA systems, as well as other baseline methods. The results also demonstrate that our proposed scheme allows for a greater power harvest than the OMA-FD configuration and other baseline schemes.

Optimizing User-Level Packet Scheduling Performance through Optimal CQI-Based Resource Allocation in LTE

Our research investigates the Dynamic Resource Allocation in the Downlink of OFDMA-based LTE-A networks. It addresses both user-level and system-level packet scheduling performance. At the user-level, a Traffic Differentiator stage segregates packet queues from active users into different service queues based on service types. Users are prioritized within each service queue based on their QoS requirements and wireless channel conditions, utilizing SPSSA. At the system-level, fairness among users is a key consideration. We propose the PITDSA in the TD Scheduler stage, which aims to allocate just enough radio resource to real-time and non-real time services and assign the remaining available resource to background service. In the FD Scheduler stage, we propose an optimal CQI selection algorithm for resource allocation to exploit frequency domain multi-user diversity. We present our simulation results and analyses of all novel algorithms, and the performance of the Optimal CQI selection algorithm is compared with other algorithms. Our proposed scheduling algorithm demonstrates improved QoS for real-time and non-real time services while maintaining a good traded user-level and system-level performance. Future work can focus on optimizing the throughput or the fairness or both, and more advanced and complex techniques can be designed with the same goal.