Adaptive Learning Video Streaming with QoE in Multi-Home Heterogeneous Networks

In recent years,real-time video streaming has grown in popularity.The growing popularity of the Internet of Things(IoT)and other wireless heterogeneous networks mandates that network resources be carefully apportioned among versatile users in order to achieve the best Quality of Experience(QoE)and performance objectives.Most researchers focused on Forward Error Correction(FEC)techniques when attempting to strike a balance between QoE and performance.However,as network capacity increases,the performance degrades,impacting the live visual experience.Recently,Deep Learning(DL)algorithms have been successfully integrated with FEC to stream videos across multiple heterogeneous networks.But these algorithms need to be changed to make the experience better without sacrificing packet loss and delay time.To address the previous challenge,this paper proposes a novel intelligent algorithm that streams video in multi-home heterogeneous networks based on network-centric characteristics.The proposed framework contains modules such as Intelligent Content Extraction Module(ICEM),Channel Status Monitor(CSM),and Adaptive FEC(AFEC).This framework adopts the Cognitive Learning-based Scheduling(CLS)Module,which works on the deep Reinforced Gated Recurrent Networks(RGRN)principle and embeds them along with the FEC to achieve better performances.The complete framework was developed using the Objective Modular Network Testbed in C++(OMNET++),Internet networking(INET),and Python 3.10,with Keras as the front end and Tensorflow 2.10 as the back end.With extensive experimentation,the proposed model outperforms the other existing intelligentmodels in terms of improving the QoE,minimizing the End-to-End Delay(EED),and maintaining the highest accuracy(98%)and a lower Root Mean Square Error(RMSE)value of 0.001.

Reinforcing a Dangerous Rock Mass Using the Flexible Network Method

Because the main failure type of a dangerous rock mass is collapse, the treatment of such a mass should focus on controlling collapse failure. When treating dangerous rock masses, disturbing the mass （e. g. by blasting） needs to be avoided, as this new damage could cause collapse. So the self-bearing capacity of the mountain mass must be used to treat the dangerous rock mass. This article is based on a practical example of the control of a dangerous rock mass at Banyan Mountain, Huangshi, Hubei Province. On the basis of an analysis of damage mechanism and the stability of the dangerous rock mass, a flexible network reinforcement method was designed to prevent the collapse of the rock mass. The deformations of section Ⅱ w of the dangerous rock mass before and after the flexible network reinforcement were calculated using the two-dimensional finite element method. The results show that the maximum deformation reduced by 55 % after the application of the flexible network reinforcement, from 45.99 to 20.75 ram, which demonstrates that the flexible network method is effective, and can provide some scientific basis for the treatment of dangerous rock masses.

Optimization Scheme of Trusted Task Offloading in IIoT Scenario Based on DQN

With the development of the Industrial Internet of Things(IIoT),end devices(EDs)are equipped with more functions to capture information.Therefore,a large amount of data is generated at the edge of the network and needs to be processed.However,no matter whether these computing tasks are offloaded to traditional central clusters or mobile edge computing(MEC)devices,the data is short of security and may be changed during transmission.In view of this challenge,this paper proposes a trusted task offloading optimization scheme that can offer low latency and high bandwidth services for IIoT with data security.Blockchain technology is adopted to ensure data consistency.Meanwhile,to reduce the impact of low throughput of blockchain on task offloading performance,we design the processes of consensus and offloading as a Markov decision process(MDP)by defining states,actions,and rewards.Deep reinforcement learning(DRL)algorithm is introduced to dynamically select offloading actions.To accelerate the optimization,we design a novel reward function for the DRL algorithm according to the scale and computational complexity of the task.Experiments demonstrate that compared with methods without optimization,our mechanism performs better when it comes to the number of task offloading and throughput of blockchain.

Wind Generation Modeling for Transmission Systems Adequacy Studies with Economic Dispatch

In this paper, a strategy is proposed in order to introduce in a realistic way wind generation into a HL11 （bulk power system） non sequential Monte Carlo adequacy study with economic dispatch. Thanks to the implemented solution, wind generation is consequently confronted to operational constraints related to high powered thermal units, nuclear parks or thermal machines with technical minimum value. Moreover, during each simulated system state, a DC load flow is also calculated in order to evaluate reinforcements optimizing the large scale integration of wind power production. The simulation tool modified during the present work is called Scanner and is the property of Tractebel Engineering （Gaz de France-Suez） company. It has been here applied to an academic test system： the Roy Billinton Test System （RBTS）.