QoS Optimization Research Articles

A deep reinforcement learning-based intelligent QoS optimization algorithm for efficient routing in vehicular networks

With the rapid development of Telematics, Vehicle Self-Organizing Networks (VANETs) play an increasingly critical role in Intelligent Transportation Systems (ITS). Especially in the environment without roadside assistance units (RSUs), how to effectively manage inter-vehicle communication and improve the stability and communication efficiency of the network has become a hot topic of current research. In this paper, a Deep Reinforcement Learning-based Intelligent QoS-optimized efficient routing algorithm for vehicular networks (DRLIQ) is proposed for VANETs with/without RSU environments, and routing methods are proposed respectively. Among them, in RSU-free environment, the DRLIQ algorithm utilizes the powerful processing capability of deep reinforcement learning to intelligently select the optimal data transmission path by dynamically learning and adapting to the changes in the vehicular network, thus effectively reducing communication interruptions and delays, and improving the accuracy of data transmission. The performance of the DRLIQ algorithm under different vehicle densities is evaluated in simulation experiments and compared with current popular algorithms. The experimental results show that the DRLIQ algorithm outperforms the comparison algorithms in reducing the number of communication interruptions, BER and network delay, especially in vehicle-dense environments. In addition, the DRLIQ algorithm shows higher adaptability and stability in coping with network topology changes and vehicle dynamics.

Multi-Dimensional QoS Evaluation and Optimization of Mobile Edge Computing for IoT: A Survey

Multi-Dimensional QoS Evaluation and Optimization of Mobile Edge Computing for IoT: A Survey

Revolutionizing healthcare with fog computing, IoT, and machine learning: An innovative framework for enhanced data security and QoS optimization

Revolutionizing healthcare with fog computing, IoT, and machine learning: An innovative framework for enhanced data security and QoS optimization

Q-Learning Model for Blockchain Security in Internet of Medical Things Networks

Secure Internet of Medical Things (IoMT) installations must take a long time to complete, which leaves them vulnerable to low energy performance, low throughput, and low packet delivery rates. This limits performance in real-time deployments, which has an effect on healthcare efficiency. Many optimization approaches have been proposed by researchers to deal with these issues, but most of them increase the complexity of mining and thus reduce its scalability. In order to improve the temporal QoS performance of IoMT deployments, this paper proposes the creation of a novel bio-inspired model. In order to evaluate the temporal mining performance (of miners) in terms of mining throughput, block mining ratio, latency, and energy, the proposed model makes use of the Mayfly Optimization (MO) Method. A fitness function based on these indices is then used to decide whether or not the underlying blockchains should be combined or split up. During a split operation, a blockchain is divided into two equal parts, and during a merge operation, the blockchain is saved for later retrieval. The proposed model improves block mining performance by 3.2%, compared to the average QoS optimization model under similar conditions, increases mining speed by 3.5%, decreases energy consumption by 4.9%, increases throughput by 2.8%, and improves performance by 3.2%.

QoS optimization for full-duplex enabled relaying system with consecutive interference annulment

In this research work QoS optimization issue while power division in with full-duplex enable transfer capability in the presence of a direct connection. A novel system model for power division based relaying system with full-duplex capability and a strategy that employs techniques such as consecutive interference cancellation. They also use the existing system to define the channel capacity alleviation issue. Then, using the suggested repeated power division ratio method and the planned information architecture, the defined issue is solved. FDBPT created a Power dividing based relaying technology with FD Decode and forwarding capability in this research paper, taking into account the existence of a direct connection. Then, to solve the spectrum efficiency optimization model, information decoding strategy based on SIC and MRC algorithms was presented to decipher signals from the destination node. An IPDP methodology was presented in the proposed way to solve the stated enormous performance maximizing issue. The proposed information decoding technique takes into account the benefits of direct connection, FD, and SIC. Furthermore, a number of simulations demonstrate the higher performance of the proposed approach. NS2 simulations are used to attain higher performance than previous techniques.

Machine Learning for QoS Optimization and Energy-Efficient in Routing Clustering Wireless Sensors

Machine Learning for QoS Optimization and Energy-Efficient in Routing Clustering Wireless Sensors

RPL-OFs analysis and dynamic of selection for QoS optimisation of RPL protocol

RPL-OFs analysis and dynamic of selection for QoS optimisation of RPL protocol

SFC Orchestration Method for Edge Cloud and Central Cloud Collaboration: QoS and Energy Consumption Joint Optimization Combined With Reputation Assessment

SFC Orchestration Method for Edge Cloud and Central Cloud Collaboration: QoS and Energy Consumption Joint Optimization Combined With Reputation Assessment

Radio-Frequency Fingerprinting for Distributed IoT Networks: Authentication and QoS Optimization

With the advance of the Internet of Things (IoT), limited wireless resources cannot meet the requirements of all users. User authentication can identify the users who are not authenticated and only allocate the resources to authenticated users. However, traditional authentication methods require complicated computations that restrict the wide deployment of low-cost devices in IoT applications. This article proposes a federal learning-based authentication method with radio-frequency fingerprinting recognition under a distributed architecture, which is novel and different from the previous works since these works primarily focus on the network with a centralized node. We propose a distributed optimization problem in a hybrid local and edge computing scenario to minimize the network delay. By transforming the original problem into its Lagrange function and calculating its derivatives, we propose a method of solving the optimization problem and prove that the proposed method could reduce the unnecessary consumption of wireless resources and effectively improve the entire network's performance. The analytical results also indicate that the proposed method could outperform the benchmark authentication methods in reducing network delay.

Multi-user edge service orchestration based on Deep Reinforcement Learning

The fifth generation (5G) of mobile network offers a remarkable degree of flexibility to mobile operators, enabling them to provide users with effective and tailored network services. Software Defined Networking (SDN), Network Function Virtualization (NFV), and edge computing have given the operator the opportunity to easily bring computational capacity to the edge and to support latency-sensitive services. While 5G standards have defined the technological and architectural frameworks to orchestrate services, finding effective resources management and QoS optimization policies is still an open research issue.In this paper, we propose an online orchestration methodology for a multi-user edge service. The orchestrator goal is to simultaneously maximize the QoS, and minimize the amount of resources needed.We provide a mathematical formulation to compute an optimal offline policy and derive an online approach based on a model-free Deep Reinforcement Learning (DRL) framework. As a novel feature, the DRL agent action is modeled as a parametric combinatorial problem. A tailored multi-objective reward function leads the agent towards an effective choice of parameters for such a model. Our models are built, trained and fine-tuned by exploiting real data.Extensive simulations in diverse scenarios show that our DRL online approach produces solutions with small gaps to the optimal offline ones, enabling the operator to both save resources and grant the users an adequate QoS level.

Retracted: Energy Consumption and QoS Optimization Coverage Mechanism in Wireless Sensor Networks Based on Swarm Sensing Algorithm

Retracted: Energy Consumption and QoS Optimization Coverage Mechanism in Wireless Sensor Networks Based on Swarm Sensing Algorithm

Retraction Note: A QoS optimization system for complex data cross-domain request based on neural blockchain structure

Retraction Note: A QoS optimization system for complex data cross-domain request based on neural blockchain structure

Design and QoS Optimization Model of Party Building Cloud Platform Based on FoRTAN

Using FoumNPowERsTAⅡoNv4. o to develop a simulation program is the core focus of this paper. It has a good wlND0ws interface, which can intuitively and conveniently carry out a man-to-machine dialogue, which is convenient for simulation racing. Its application example shows that the program has universality and practicability. The informatization work of party building in colleges and universities can be carried out according to different situations: confidential work can be combined with the campus informatization platform; collaborative work between units can introduce third-party questionnaires and cloud collaboration documents to connect the data communication network with information. Intranet integration and unified deployment. With the growth of communication data volume, higher requirements are put forward for the quality of service of converged network QoS and the resource consumption of base stations and users.

Full-duplex QoS Optimization using Enhanced firefly Algorithm

The major goal is to determine how to allocate resources in a full-duplex cloud radio access network. Furthermore, due of the dispersed characteristic of the Portable Broadcasting Antenna that decreases self-interference. A full-duplex communication system enables information to be sent and processed at the same time among terminals. It has a bandwidth efficiency that is double that of a half-duplex data transmission. The goal of the research is to determine the best power allocation for the receiver transmitter whenever the flow of information is at its highest. The Enhanced Firefly Algorithm is used for efficiency. It's an improvement process that operates in the same way that a firefly's fascination to strobe does. The stronger light encourages the less brilliant firefly to come closer. It's an iterative procedure, and also the community of fireflies finally propagates on the strongest one. The matching power value is logged whenever the optimum inverted information rate is reached, and it is used to distribute amongst radio transmitter and several station receiver heads. Energy parameters are fixed to select the optimal derivative. Lastly, using NS2 to depict graphical representations, the suggested technique is superior to generalized benders decomposition (GBD)-based resource allocation (GRA) algorithm.

Energy Consumption and QoS Optimization Coverage Mechanism in Wireless Sensor Networks Based on Swarm Sensing Algorithm

Objective. To study energy consumption and QoS optimization coverage mechanism in wireless sensor networks based on swarm sensing algorithm. Methods. The swarm sensor algorithm is reported to optimize the configuration of wireless sensor nodes, improve network service performance, and provide feedback on effective sensor placement. Results. Two WSN coverage optimization schemes were proposed in this essay. The first scheme applied drosophila intelligent algorithm to WSN coverage optimization, and the second scheme integrated particle swarm optimization algorithm and improved firefly algorithm to complement each other. Both schemes can achieve good results in practical application. Scheme one can increase the coverage rate by 6.36% compared with previous schemes, while scheme two can increase it by 6.48%. Both schemes are basically the same, so they can be applied in the optimization of WSN node deployment. Conclusion. As an optimization method, group perception algorithm is inspired by biology. Through the continuous enrichment and development of this algorithm, it has been effectively used in the coverage optimization of the wireless sensor networks.

SDN Control Strategy and QoS Optimization Simulation Performance Based on Improved Algorithm.

In the past few years, the Internet has become more and more popular, and more and more people use the Internet. A lot of new software has been developed on the Internet, so the network traffic has increased rapidly. A company once clearly stated that, in the future, the network volume in our country will reach nearly 5 zb. In addition, the network is comprehensively unified, and the learning of the Internet is strengthened by the way of suggesting errors. It is also necessary to learn from the external environment to make the finite value of the network as good as possible. Reinforcement learning is about solving many difficult problems. Use reinforcement learning to promote and change this state, so that the advantages of the network can be fully developed. Regarding the environment and actions at the time as the basic mapping requirements, and the network technology strategy can be best developed. In order to solve the QoS optimization scheme of the current mainstream heuristic algorithm in the software-defined network scene, the software-defined network QoS optimization algorithm is proposed. First, the network resources and status information are unified into the network model, and then, the long- and short-term memory network is used to improve the algorithm's flow perception ability. Finally, based on the deep reinforcement learning algorithm, a dynamic traffic scheduling strategy that satisfies the QoS objective is constructed. Among them, QoS refers to the main functions of the entire network system, especially for some new users, but for many old users, this system can represent a certain application, for example, whether a certain software can get timely, whether it can work smoothly when processing videos, and whether it can be interrupted when making a voice call for improvement.

Mobility-aware and Privacy-protecting QoS Optimization in Mobile Edge Networks

Mobility-aware and Privacy-protecting QoS Optimization in Mobile Edge Networks

Efficient configuration for multi-objective QoS optimization in wireless sensor network

Wireless sensor network (WSN) is a self-organizing network consisting of many sensor nodes situated in the monitoring area. One of the most important challenges of WSNs is to configure the network in a way to improve the quality-of-service parameters, and thus enhance the network performance and reduce energy consumption. Energy consumption increases due to several reasons such as unsuccessful delivery of packets to the receiver, re-transmission of packets, delay in packet delivery to the sink, low received signal strength, inadequate link quality, noise level, etc. Each sensor network consists of different configuration parameters including the distance between receiver and transmitter, packet arrival time, packet size, maximum queue size, maximum number of transmissions, transmission power level, etc. All the mentioned parameters will affect several indicators such as latency, received signal strength, link quality, successful/fail packet delivery, noise level, and the number of re-transmissions. Finding the relationship between these two sets of parameters and optimizing them reduce energy consumption in the network and increase network lifetime. To achieve this, an efficient and new method based on support vector regression and genetic algorithm called MSOG is presented in this paper to model and optimize wireless sensor network indicators (such as delay, received signal strength, packet arrival time, successful packet delivery). The simulation results show that MSOG has better outcomes in optimizing network parameters and indicators in comparison with a similar algorithm.

Energy-efficient collaborative optimization for VM scheduling in cloud computing

Energy-efficient resource scheduling has become a hot issue in the field of cloud computing. However, there is an inevitable conflict between energy-saving and QoS optimization. In real-world scenarios, the volatility of cloud task arrival will cause the optimization problem to become more difficult. To achieve a better trade-off between these two goals, a novel resource scheduling framework based on collaborative optimization is proposed for the cloud computing environment. Based on the Lyapunov optimization method, the optimization problem can be solved explicitly in each time slice. We build a multi virtual machine queuing model and analyze the relationships between the task queues’ backlog and the system energy consumption. We also introduce a method of using stacked denoising auto-encoder for extracting the QoS features to improve the constraints of the collaborative optimization objective function. Finally, we propose an efficient resource scheduling strategy to give full play to the processing capabilities of the virtual machine. Experimental results show that, compared with other advanced energy-saving strategies, our scheduling strategy can effectively reduce the energy consumption of the cloud data center while guaranteeing QoS, and reduce the total scheduling time cost of data center by more than 20%.

Energy-efficient VM scheduling based on deep reinforcement learning

Achieving data center resource optimization and QoS guarantee driven by high energy efficiency has become a research hotspot. However, QoS information directly sampled from the cloud environment will inevitably be affected by a small amount of structured noise. This paper proposes a deep reinforcement learning model based on QoS feature learning to optimize data center resource scheduling. In the deep learning stage, we propose a QoS feature learning method based on improved stacked denoising autoencoders to extract more robust QoS characteristic information. In the reinforcement learning stage, we propose a multi-power machines (PMs) collaborative resource scheduling algorithm based on reinforcement learning. Extensive experiments show that compared with other excellent resource scheduling strategies, our method can effectively reduce the energy consumption of cloud data centers while maintaining the lowest service level agreement (SLA) violation rate. A good balance is achieved between energy-saving and QoS optimization.