The effect of network convergence on digital culture industry: Model construction of network industrial economics and empirical study on China

This paper constructs the model of network economics to study the effect of different levels of network convergence on the digital culture industry. Then uses regression models and mediating effect models to test the effect mechanism of network convergence on the digital culture industry of China. This paper used panel data to conduct an empirical study. The data in this paper were quarterly. The time range was from the first quarter of 2009 to the third quarter of 2013 for 19 quarters.The three data types in econometrics are time series data, cross-sectional data, and panel data.The main conclusions are as follows. Network convergence brings positive policy effects and adverse capital effects. The impact of network convergence on firm performance of the digital culture industry is not statistically significant, and this effect also has no indirect effects on the test of mediating effect. However, network convergence indirectly leads to the reduction of operating costs of the digital culture industry. The indirect effect is brought by the chain mediating effect of policy effect and capital effect. The study could provide a reference for other countries and regions. Meanwhile, it can be used to analyze the impact of different media convergence on digital industries.

Antinoise Learning and Coalitional Game GAN

Adversarial learning stability has an important influence on the generated image quality and convergence process in generative adversarial networks (GANs). Training dataset (real data) noise and the balance of game players have an impact on adversarial learning stability. In the gradient backpropagation of the discriminator, the noise samples increase the gradient variance. It can increase the uncertainty in the network convergence progress and affect stability. In the two-player zero-sum game, the game ability of the generator and discriminator is unbalanced. Generally, the game ability of the generator is weaker than that of the discriminator, which affects the stability. To improve the stability, an antinoise learning and coalitional game generative adversarial network (ANL-CG GAN) is proposed, which achieves this goal through the following two strategies. (i) In the real data loss function of the discriminator, an effective antinoise learning method is designed, which can improve the gradient variance and network convergence uncertainty. (ii) In the zero-sum game, a generator coalitional game module is designed to enhance its game ability, which can improve the balance between the generator and discriminator via a coalitional game strategy. To verify the performance of this model, the generated results of the designed GAN and other GAN models in CELEBA and CIFAR10 are compared and analyzed. Experimental results show that the novel GAN can improve adversarial learning stability, generate image quality, and reduce the number of training epochs.

Strategic Analysis of Circular Economy on "Integration of Two Networks": Take the Tobacco Industry in C City as an Example

Objectives: Reducing garbage sorting and renewable resources recovery increment is an important content of promoting circulation economy development of China, based on the C city tobacco industry as an example, first the present situation of "two network convergence" in C city tobacco industry and results were analyzed, and then using accident tree analysis it is concluded that the tobacco industry "two nets fusion" are faced with the problem, and then establish orthogonalization discrete Hopfield neural network capability evaluation model, "two network convergence" By using Matlab to conduct network learning and training, simulation experiments and data analysis, the quantitative evaluation of the tobacco industry "two-network integration" ability of five cities including C city is objectively obtained. On this basis, the countermeasures for improving the tobacco industry "two-network integration" ability of China are given.

Performance Analysis of Routing Protocols On IPv4 and IPv6 Addressing Networks

With IPv4 addresses being exhausted, network engineers and researchers are encouraged to adopt IPv6. But before using the IPv6 network directly, engineers need to test their hardware and network performance under new conditions of IPv6 as it has an extended address, high complexity, overhead performance, and IPsec complications. As routing protocols play a crucial role in network performance, it leads to a network’s extended performance by finding the shortest path, good throughput, and lowest delay. As the specifications, viz. frame structure for IPv4 and IPv6 are entirely different, there are modified routing protocols specified for IPv6. Routing Information Protocol (RIP) and Routing Information Protocol Next Generation (RIPng) are distance vector routing protocols and use hop count as a cost. In this paper, we have used RIPng on the IPv6 addressing network and RIP on the IPv4 addressing network and then analyze and compare them on the basis of different performance parameters. For this comparison, three different applications – File Transfer Protocol (FTP), DB Query (DataBase), and electronic mail (e-mail) – are set on a network consisting of three different subnets, each having a diverse network topology. The performance parameters analyzed are global and object statistics, viz. ethernet delay, number of hops, applications response time, background traffic delay, traffic dropped, point-to-point links throughput, links utilization, and links queuing delay. The experimental results determine the strength of the routing protocols. Thus, the quantitative results give the option to choose the routing protocol according to the network scenarios. In terms of ethernet delay, traffic dropped, network convergence, and security, it is found that the RIPng_IPv6 network performs better than RIP_IPv4. RIPng_IPv6 has an ethernet delay of 2.9 milliseconds, traffic dropped of 0.29 packets/second, and network convergence of 17 seconds less than RIP_IPv4 values. However, the RIP_IPv4 network is scalable, uses less hop, and has 40 milliseconds of traffic delay, while RIPng_IPv6 has 0.40 seconds of traffic delay. RIP_IPv4 also has a better response time for all three applications, FTP as 100 milliseconds, DB as 40 milliseconds, and e-mail as 20 milliseconds which is much less than the values obtained for RIPng_IPv6 network. Therefore, according to the performance requirements, the network engineers/operators or researchers can use either the existing IPv4 network or a new IPv6 network to achieve the Quality of Service (QoS) target level.

Collision Avoidance Geographic P2P-RPL in Multi-Hop Indoor Wireless Networks

In home and building automation applications, wireless sensor devices need to be connected via unreliable wireless links within a few hundred milliseconds. Routing protocols in Low-power and Lossy Networks (LLNs) need to support reliable data transmission with an energy-efficient manner and short routing convergence time. IETF standardized the Point-to-Point RPL (P2P-RPL) routing protocol, in which P2P-RPL propagates the route discovery messages over the whole network. This leads to significant routing control packet overhead and a large amount of energy consumption. P2P-RPL uses the trickle algorithm to control the transmission rate of routing control packets. The non-deterministic message suppression nature of the trickle algorithm may generate a sub-optimal routing path. The listen-only period of the trickle algorithm may lead to a long network convergence time. In this paper, we propose Collision Avoidance Geographic P2P-RPL, which achieves energy-efficient P2P data delivery with a fast routing request procedure. The proposed algorithm uses the location information to limit the network search space for the desired route discovery to a smaller location-constrained forwarding zone. The Collision Avoidance Geographic P2P-RPL also dynamically selects the listen-only period of the trickle timer algorithm based on the transmission priority related to geographic position information. The location information of each node is obtained from the Impulse-Response Ultra-WideBand (IR-UWB)-based cooperative multi-hop self localization algorithm. We implement Collision Avoidance Geographic P2P-RPL on Contiki OS, an open-source operating system for LLNs and the Internet of Things. The performance results show that the Collision Avoidance Geographic P2P-RPL reduced the routing control packet overheads, energy consumption, and network convergence time significantly. The cooperative multi-hop self localization algorithm improved the practical implementation characteristics of the P2P-RPL protocol in real world environments. The collision avoidance algorithm using the dynamic trickle timer increased the operation efficiency of the P2P-RPL under various wireless channel conditions with a location-constrained routing space.

Managing 5G Converged Core With Access Traffic Steering, Switching, and Splitting

This chapter discusses the ongoing work around hybrid access and network convergence, with particular emphasis on recent works on ATSSS in 3GPP. Three main aspects are analyzed: policy enforcement, integration with 5G QoS framework, interaction with underlying multi-path transport protocol. The chapter also provides some preliminary testbed results showing the benefits of ATSSS in the management of multiple accesses analyzing some primary performance indicators such as achievable data rates, link utilization for aggregated traffic, and session setup latency. The chapter also provides some results by considering two examples of realization of ATSSS policies to avoid inefficiency in link utilization and to allow the fulfillment of data rate requirements.