The Caching and Pricing Strategy for Information-Centric Networking with Advertisers’Participation

As users’access to the network has evolved into the acquisition of mass contents instead of IP addresses,the IP network architecture based on end-to-end communication cannot meet users’needs.Therefore,the Information-Centric Networking(ICN)came into being.From a technical point of view,ICN is a promising future network architecture.Researching and customizing a reasonable pricing mechanism plays a positive role in promoting the deployment of ICN.The current research on ICN pricing mechanism is focused on paid content.Therefore,we study an ICN pricing model for free content,which uses game theory based on Nash equilibrium to analysis.In this work,advertisers are considered,and an advertiser model is established to describe the economic interaction between advertisers and ICN entities.This solution can formulate the best pricing strategy for all ICN entities and maximize the benefits of each entity.Our extensive analysis and numerical results show that the proposed pricing framework is significantly better than existing solutions when it comes to free content.

A Survey on Information-Centric Networking: Rationales, Designs and Debates

The basic function of the Internet is to delivery data(what) to serve the needs of all applications. IP names the attachment points(where) to facilitate ubiquitous interconnectivity as the current way to deliver data. The fundamental mismatch between data delivery and naming attachment points leads to a lot of challenges, e.g., mapping from data name to IP address, handling dynamics of underlying topology, scaling up the data distribution, and securing communication, etc. Informationcentric networking(ICN) is proposed to shift the focus of communication paradigm from where to what, by making the named data the first-class citizen in the network, The basic consensus of ICN is to name the data independent from its container(space dimension) and session(time dimension), which breaks the limitation of point-to-point IP semantic. It scales up data distribution by utilizing available resources, and facilitates communication to fit diverse connectivity and heterogeneous networks. However, there are only a few consensuses on the detailed design of ICN, and quite a few different ICN architectures are proposed. This paper reveals the rationales of ICN from the perspective of the Internet evolution, surveys different design choices, and discusses on two debatable topics in ICN, i.e.,self-certifying versus hierarchical names, and edge versus pervasive caching. We hope this survey helps clarify some mis-understandings on ICN and achieve more consensuses.

What to Cache：Differentiated Caching Resource Allocation and Management in Information-Centric Networking

Information-centric networking(ICN) aims to improve the efficiency of content delivery and reduce the redundancy of data transmission by caching contents in network nodes. An important issue is to design caching methods with better cache hit rate and achieve allocating on-demand. Therefore, an in-network caching scheduling scheme for ICN was designed, distinguishing different kinds of contents and dynamically allocating the cache size on-demand. First discussing what was appropriated to be cached in nodes, and then a classification about the contents could be cached was proposed. Furthermore, we used AHP to weight different contents classes through analyzing users' behavior. And a distributed control process was built, to achieve differentiated caching resource allocation and management. The designed scheme not only avoids the waste of caching resource, but also further enhances the cache availability. Finally, the simulation results are illustrated to show that our method has the superior performance in the aspects of server hit rate and convergence.

Exploring Content Popularity in Information-Centric Networks

Information-Centric Networking(ICN), an alternative architecture to the current Internet infrastructure, focuses on the distribution and retrieval of content by employing caches in a network to reduce network traffic. The employment of caches may be accomplished using graph-based and content-based criteria such as the position of a node in a network and content popularity. The contribution of this paper lies on the characterization of content popularity for on-path in-network caching. To this end, four dynamic approaches for identifying content popularity are evaluated via simulations. Content popularity may be determined per chunk or per object, calculated by the number of requests for a content against the sum of requests or the maximum number of requests. Based on the results, chunk-based approaches provide 23% more accurate content popularity calculations than object-based approaches. In addition, approaches that are based on the comparison of a content against the maximum number of requests have been shown to be more accurate than the alternatives.

CMLP: Exploiting Caches at Multiple Levels of Proxies to Enhance Seamless Mobility Support in Information-Centric Networks

The recent evolution of the Internet towards "Information-centric" transfer modes has renewed the interest in exploiting proxies to enhance seamless mobility. In this work, we focus on the case of multiple levels of proxies in ICN architectures, in which content requests from mobile subscribers and the corresponding items are proactively cached to these proxies at different levels. Specifically, we present a multiple-level proactive caching model that selects the appropriate subset of proxies at different levels and supports distributed online decision procedures in terms of the tradeoff between delay and cache cost. We show via extensive simulations the reduction of up to 31.63% in the total cost relative to Full Caching, in which caching in all 1-level neighbor proxies is performed, and up to 84.21% relative to No Caching, in which no caching is used. Moreover, the proposed model outperforms other approaches with a flat cache structure in terms of the total cost.

HHR:Hierarchical Hybrid Routing Scheme for Information-Centric Network

Publish/subscribe(pub/sub) paradigm is the main communication model for Information-Centric Network(ICN) proposals.A key issue for pub/sub system is how to route the content objects to the correct subscribers,and ICN is no exception.ICN network would be divided into core domain and many edge domains as today's internet does.HHR(Hierarchy Hybrid Routing scheme) is presented for ICN:A Chord-like routing scheme is used in core domain,while edge domains routing structure can be classified into three categories,Local Routing(LR),Delivery of Local Publication to Core domain(DLPC),and Remote Publication Routing into edge domain(RPR).LR can be decided by each edge domain,which determined by many factors,such as locality characteristic for pub/sub information and local policies.A hierarchical routing algorithm is proposed to solve DLPC and RPR simultaneously.Simulation results demonstrate that HHR can be fast deployed,and can be applied in large scale network or dynamic subscription environment.

A comparative study of name resolution and routing mechanisms in 网 information-centric networks

Information-Centric Networking (ICN) is an innovative paradigm for the future internet architecture, which addresses IP network limitations in supporting content distribution and information access by decoupling content from hosts and providing the ability to retrieve a content object by its name (identifier), rather than its storage location (IP address). Name resolution and routing is critical for content retrieval in ICN networks. In this research, we perform a comparative study of two widely used classes of ICN name resolution and routing schemes, namely flooding and Distributed Hash Table (DHT). We consider the flooding-based routing in Content-Centric Networks due to its wide acceptance. For the DHT scheme, we design a multi-level DHT that takes into account the underlying network topology and uses name aggregation to further reduce control overhead and improve network efficiency. Then, we compare the characteristics and performance of these two classes of name resolution and routing through extensive simulations. The evaluation results show that the performances of these two approaches are reliant on several factors, including network size, content location dynamics, and content popularity. Our study reveals insights into the design tradeoffs and offers guidelines for design strategies.

Neighborhood-Based In-Network Caching for Information-Centric Networks

The current Internet is based on host-centric networking, and a user needs to know the host address before reaching a data target in the network. The new architecture of information-centric networking (ICN) facilitates users to locate data targets by giving their data names without any information about host addresses. In-network caching is one of the prominent features in ICN, which allows network routers to cache data contents. In this paper, we emphasize the management of in-network cache storage, and this includes the mechanisms of cache replacement and cache replication. A new cost function is then proposed to evaluate each cache content and the least valuable content is evicted when cache is full. To increase cache utilization, a cooperative caching policy among neighboring routers is proposed. The proper network locations to cache data contents are also discussed in the paper. Experimental results show the superiority of the proposed caching policy than some traditional caching polices.

Redundant Transmission Control Algorithm for Information-Centric Vehicular IoT Networks

Vehicular Adhoc Networks(VANETs)enable vehicles to act as mobile nodes that can fetch,share,and disseminate information about vehicle safety,emergency events,warning messages,and passenger infotainment.However,the continuous dissemination of information fromvehicles and their one-hop neighbor nodes,Road Side Units(RSUs),and VANET infrastructures can lead to performance degradation of VANETs in the existing hostcentric IP-based network.Therefore,Information Centric Networks(ICN)are being explored as an alternative architecture for vehicular communication to achieve robust content distribution in highly mobile,dynamic,and errorprone domains.In ICN-based Vehicular-IoT networks,consumer mobility is implicitly supported,but producer mobility may result in redundant data transmission and caching inefficiency at intermediate vehicular nodes.This paper proposes an efficient redundant transmission control algorithm based on network coding to reduce data redundancy and accelerate the efficiency of information dissemination.The proposed protocol,called Network Cording Multiple Solutions Scheduling(NCMSS),is receiver-driven collaborative scheduling between requesters and information sources that uses a global parameter expectation deadline to effectively manage the transmission of encoded data packets and control the selection of information sources.Experimental results for the proposed NCMSS protocol is demonstrated to analyze the performance of ICN-vehicular-IoT networks in terms of caching,data retrieval delay,and end-to-end application throughput.The end-to-end throughput in proposed NCMSS is 22%higher(for 1024 byte data)than existing solutions whereas delay in NCMSS is reduced by 5%in comparison with existing solutions.

Distributed Application Addressing in 6G Network

To ensure the extreme performances of the new 6G services,applications will be deployed at deep edge,resulting in a serious challenge of distributed application addressing.This paper traces back the latest development of mobile network application addressing,analyzes two novel addressing methods in carrier network,and puts forward a 6G endogenous application addressing scheme by integrating some of their essence into the 6G network architecture,combining the new 6G capabilities of computing&network convergence,endogenous intelligence,and communication-sensing integration.This paper further illustrates how that the proposed method works in 6G networks and gives preliminary experimental verification.

Protecting User Privacy in a Multi-Path Information-Centric Network Using Multiple Random-Caches

In-network caching is a fundamental mechanism advocated by information-centric networks （ICNs） for efficient content delivery. However, this new mechanism also brings serious privacy risks due to cache snooping attacks. One effective solution to this problem is random-cache, where the cache in a router randomly mimics a cache hit or a cache miss for each content request/probe. In this paper, we investigate the effectiveness of using multiple random-caches to protect cache privacy in a multi-path ICN. We propose models for characterizing the privacy of multi-path ICNs with random-caches, and analyze two different attack scenarios： 1） prefix-based attacks and 2） suffix-based attacks. Both homogeneous and heterogeneous caches are considered. Our analysis shows that in a multi-path ICN an adversary can potentially gain more privacy information by adopting prefix-based attacks. Furthermore, heterogeneous caches provide much better privacy protection than homogeneous ones under both attacks. The effect of different parameters on the privacy of multi-path random-caches is further investigated, and the comparison with its single-path counterpart is carried out based on numerical evaluations. The analysis and results in this paper provide insights in designing and evaluating multi-path ICNs when we take privacy into consideration.

CDM: Content Diffusion Model for Information-Centric Networks

This paper proposes the Content Diffusion Model(CDM)for modeling the content diffusion process in information-centric networking(ICN).CDM is inspired by the epidemic model and it provides a method of theoretical quantitative analysis for the content diffusion process in ICN.Specifically,CDM introduces the key functions to formalize the key factors that influence the content diffusion process,and thus it can construct the model via a simple but efficient way.Further,we derive CDM by using different combinations of those key factors and put them into several typical ICN scenarios,to analyze the characteristics during the diffusion process such as diffusion speed,diffusion scope,average fetching hops,changing and final state,which can greatly help to analyze the network performance and application design.A series of experiments are conducted to evaluate the efficacy and accuracy of CDM.The results show that CDM can accurately illustrate and model the content diffusion process in ICN.

Screening biomarkers for spinal cord injury using weighted gene co-expression network analysis and machine learning

Immune changes and inflammatory responses have been identified as central events in the pathological process of spinal co rd injury.They can greatly affect nerve regeneration and functional recovery.However,there is still limited understanding of the peripheral immune inflammato ry response in spinal cord inju ry.In this study.we obtained microRNA expression profiles from the peripheral blood of patients with spinal co rd injury using high-throughput sequencing.We also obtained the mRNA expression profile of spinal cord injury patients from the Gene Expression Omnibus(GEO)database(GSE151371).We identified 54 differentially expressed microRNAs and 1656 diffe rentially expressed genes using bioinformatics approaches.Functional enrichment analysis revealed that various common immune and inflammation-related signaling pathways,such as neutrophil extracellular trap formation pathway,T cell receptor signaling pathway,and nuclear factor-κB signal pathway,we re abnormally activated or inhibited in spinal cord inju ry patient samples.We applied an integrated strategy that combines weighted gene co-expression network analysis,LASSO logistic regression,and SVM-RFE algorithm and identified three biomarke rs associated with spinal cord injury:ANO10,BST1,and ZFP36L2.We verified the expression levels and diagnostic perfo rmance of these three genes in the original training dataset and clinical samples through the receiver operating characteristic curve.Quantitative polymerase chain reaction results showed that ANO20 and BST1 mRNA levels were increased and ZFP36L2 mRNA was decreased in the peripheral blood of spinal cord injury patients.We also constructed a small RNA-mRNA interaction network using Cytoscape.Additionally,we evaluated the proportion of 22 types of immune cells in the peripheral blood of spinal co rd injury patients using the CIBERSORT tool.The proportions of naive B cells,plasma cells,monocytes,and neutrophils were increased while the proportions of memory B cells,CD8^(+)T cells,resting natural killer cells,resting dendritic cells,and eosinophils were markedly decreased in spinal cord injury patients increased compared with healthy subjects,and ANO10,BST1 and ZFP26L2we re closely related to the proportion of certain immune cell types.The findings from this study provide new directions for the development of treatment strategies related to immune inflammation in spinal co rd inju ry and suggest that ANO10,BST2,and ZFP36L2 are potential biomarkers for spinal cord injury.The study was registe red in the Chinese Clinical Trial Registry(registration No.ChiCTR2200066985,December 12,2022).

Pluggable multitask diffractive neural networks based on cascaded metasurfaces

Optical neural networks have significant advantages in terms of power consumption,parallelism,and high computing speed,which has intrigued extensive attention in both academic and engineering communities.It has been considered as one of the powerful tools in promoting the fields of imaging processing and object recognition.However,the existing optical system architecture cannot be reconstructed to the realization of multi-functional artificial intelligence systems simultaneously.To push the development of this issue,we propose the pluggable diffractive neural networks(P-DNN),a general paradigm resorting to the cascaded metasurfaces,which can be applied to recognize various tasks by switching internal plug-ins.As the proof-of-principle,the recognition functions of six types of handwritten digits and six types of fashions are numerical simulated and experimental demonstrated at near-infrared regimes.Encouragingly,the proposed paradigm not only improves the flexibility of the optical neural networks but paves the new route for achieving high-speed,low-power and versatile artificial intelligence systems.

Social-ecological perspective on the suicidal behaviour factors of early adolescents in China:a network analysis

Background In early adolescence,youth are highly prone to suicidal behaviours.Identifying modifiable risk factors during this critical phase is a priority to inform effective suicide prevention strategies.Aims To explore the risk and protective factors of suicidal behaviours(ie,suicidal ideation,plans and attempts)in early adolescence in China using a social-ecological perspective.Methods Using data from the cross-sectional project‘Healthy and Risky Behaviours Among Middle School Students in Anhui Province,China',stratified random cluster sampling was used to select 5724 middle school students who had completed self-report questionnaires in November 2020.Network analysis was employed to examine the correlates of suicidal ideation,plans and attempts at four levels,namely individual(sex,academic performance,serious physical llness/disability,history of self-harm,depression,impulsivity,sleep problems,resilience),family(family economic status,relationship with mother,relationship with father,family violence,childhood abuse,parental mental illness),school(relationship with teachers,relationship with classmates,school-bullying victimisation and perpetration)and social(social support,satisfaction with society).Results In total,37.9%,19.0%and 5.5%of the students reported suicidal ideation,plans and attempts in the past 6 months,respectively.The estimated network revealed that suicidal ideation,plans and attempts were collectively associated with a history of self-harm,sleep problems,childhood abuse,school bullying and victimisation.Centrality analysis indicated that the most influential nodes in the network were history of self-harm and childhood abuse.Notably,the network also showed unique correlates of suicidal ideation(sex,weight=0.60;impulsivity,weight=0.24;family violence,weight=0.17;relationship with teachers,weight=-0.03;school-bullying perpetration,weight=0.22),suicidal plans(social support,weight=-0.15)and suicidal attempts(relationship with mother,weight=-0.10;parental mental llness,weight=0.61).Conclusions This study identified the correlates of suicidal ideation,plans and attempts,and provided practical implications for suicide prevention for young adolescents in China.Firstly,this study highlighted the importance of joint interventions across multiple departments.Secondly,the common risk factors of suicidal ideation,plans and attempts were elucidated.Thirdly,this study proposed target interventions to address the unique influencing factors of suicidal ideation,plans and attempts.

Image super‐resolution via dynamic network

Convolutional neural networks depend on deep network architectures to extract accurate information for image super‐resolution.However,obtained information of these con-volutional neural networks cannot completely express predicted high‐quality images for complex scenes.A dynamic network for image super‐resolution(DSRNet)is presented,which contains a residual enhancement block,wide enhancement block,feature refine-ment block and construction block.The residual enhancement block is composed of a residual enhanced architecture to facilitate hierarchical features for image super‐resolution.To enhance robustness of obtained super‐resolution model for complex scenes,a wide enhancement block achieves a dynamic architecture to learn more robust information to enhance applicability of an obtained super‐resolution model for varying scenes.To prevent interference of components in a wide enhancement block,a refine-ment block utilises a stacked architecture to accurately learn obtained features.Also,a residual learning operation is embedded in the refinement block to prevent long‐term dependency problem.Finally,a construction block is responsible for reconstructing high‐quality images.Designed heterogeneous architecture can not only facilitate richer structural information,but also be lightweight,which is suitable for mobile digital devices.Experimental results show that our method is more competitive in terms of performance,recovering time of image super‐resolution and complexity.The code of DSRNet can be obtained at https://github.com/hellloxiaotian/DSRNet.

Mapping Network-Coordinated Stacked Gated Recurrent Units for Turbulence Prediction

Accurately predicting fluid forces acting on the sur-face of a structure is crucial in engineering design.However,this task becomes particularly challenging in turbulent flow,due to the complex and irregular changes in the flow field.In this study,we propose a novel deep learning method,named mapping net-work-coordinated stacked gated recurrent units(MSU),for pre-dicting pressure on a circular cylinder from velocity data.Specifi-cally,our coordinated learning strategy is designed to extract the most critical velocity point for prediction,a process that has not been explored before.In our experiments,MSU extracts one point from a velocity field containing 121 points and utilizes this point to accurately predict 100 pressure points on the cylinder.This method significantly reduces the workload of data measure-ment in practical engineering applications.Our experimental results demonstrate that MSU predictions are highly similar to the real turbulent data in both spatio-temporal and individual aspects.Furthermore,the comparison results show that MSU predicts more precise results,even outperforming models that use all velocity field points.Compared with state-of-the-art methods,MSU has an average improvement of more than 45%in various indicators such as root mean square error(RMSE).Through comprehensive and authoritative physical verification,we estab-lished that MSU’s prediction results closely align with pressure field data obtained in real turbulence fields.This confirmation underscores the considerable potential of MSU for practical applications in real engineering scenarios.The code is available at https://github.com/zhangzm0128/MSU.

Activation Redistribution Based Hybrid Asymmetric Quantization Method of Neural Networks

The demand for adopting neural networks in resource-constrained embedded devices is continuously increasing.Quantization is one of the most promising solutions to reduce computational cost and memory storage on embedded devices.In order to reduce the complexity and overhead of deploying neural networks on Integeronly hardware,most current quantization methods use a symmetric quantization mapping strategy to quantize a floating-point neural network into an integer network.However,although symmetric quantization has the advantage of easier implementation,it is sub-optimal for cases where the range could be skewed and not symmetric.This often comes at the cost of lower accuracy.This paper proposed an activation redistribution-based hybrid asymmetric quantizationmethod for neural networks.The proposedmethod takes data distribution into consideration and can resolve the contradiction between the quantization accuracy and the ease of implementation,balance the trade-off between clipping range and quantization resolution,and thus improve the accuracy of the quantized neural network.The experimental results indicate that the accuracy of the proposed method is 2.02%and 5.52%higher than the traditional symmetric quantization method for classification and detection tasks,respectively.The proposed method paves the way for computationally intensive neural network models to be deployed on devices with limited computing resources.Codes will be available on https://github.com/ycjcy/Hybrid-Asymmetric-Quantization.

Five commonly used traditional Chinese medicine formulas in the treatment of ulcerative colitis:A network meta-analysis

BACKGROUND Currently,traditional Chinese medicine(TCM)formulas are commonly being used as adjunctive therapy for ulcerative colitis in China.Network meta-analysis,a quantitative and comprehensive analytical method,can systematically compare the effects of different adjunctive treatment options for ulcerative colitis,providing scientific evidence for clinical decision-making.AIM To evaluate the clinical efficacy and safety of commonly used TCM for the treatment of ulcerative colitis(UC)in clinical practice through a network metaanalysis.METHODS Clinical randomized controlled trials of these TCM formulas used for the adjuvant treatment of UC were searched from the establishment of the databases to July 1,2022.Studies that met the inclusion criteria were screened and evaluated for literature quality and risk of bias according to the Cochrane 5.1 standard.The methodological quality of the studies was assessed using ReviewManager(RevMan)5.4,and a funnel plot was constructed to test for publication bias.ADDIS 1.16 statistical software was used to perform statistical analysis of the treatment measures and derive the network relationship and ranking diagrams of the various intervention measures.RESULTS A total of 64 randomized controlled trials involving 5456 patients with UC were included in this study.The adjuvant treatment of UC using five TCM formulations was able to improve the clinical outcome of the patients.Adjuvant treatment with Baitouweng decoction(BTWT)showed a significant effect[mean difference=36.22,95%confidence interval(CI):7.63 to 65.76].For the reduction of tumor necrosis factor in patients with UC,adjunctive therapy with BTWT(mean difference=−9.55,95%CI:−17.89 to−1.41),Shenlingbaizhu powder[SLBZS;odds ratio(OR)=0.19,95%CI:0.08 to 0.39],and Shaoyao decoction(OR=−23.02,95%CI:−33.64 to−13.14)was effective.Shaoyao decoction was more effective than BTWT(OR=0.12,95%CI:0.03 to 0.39),SLBZS(OR=0.19,95%CI:0.08 to 0.39),and Xi Lei powder(OR=0.34,95%CI:0.13 to 0.81)in reducing tumor necrosis factor and the recurrence rate of UC.CONCLUSION TCM combined with mesalazine is more effective than mesalazine alone in the treatment of UC.

The optimal atropine concentration for myopia control in Chinese children: a systematic review and network Metaanalysis

AIM:To figure out whether various atropine dosages may slow the progression of myopia in Chinese kids and teenagers and to determine the optimal atropine concentration for effectively slowing the progression of myopia.METHODS:A systematic search was conducted across the Cochrane Library,PubMed,Web of Science,EMBASE,CNKI,CBM,VIP,and Wanfang database,encompassing literature on slowing progression of myopia with varying atropine concentrations from database inception to January 17,2024.Data extraction and quality assessment were performed,and a network Meta-analysis was executed using Stata version 14.0 Software.Results were visually represented through graphs.RESULTS:Fourteen papers comprising 2475 cases were included;five different concentrations of atropine solution were used.The network Meta-analysis,along with the surface under the cumulative ranking curve(SUCRA),showed that 1%atropine(100%)>0.05%atropine(74.9%)>0.025%atropine(51.6%)>0.02%atropine(47.9%)>0.01%atropine(25.6%)>control in refraction change and 1%atropine(98.7%)>0.05%atropine(70.4%)>0.02%atropine(61.4%)>0.025%atropine(42%)>0.01%atropine(27.4%)>control in axial length(AL)change.CONCLUSION:In Chinese children and teenagers,the five various concentrations of atropine can reduce the progression of myopia.Although the network Meta-analysis showed that 1%atropine is the best one for controlling refraction and AL change,there is a high incidence of adverse effects with the use of 1%atropine.Therefore,we suggest that 0.05%atropine is optimal for Chinese children to slow myopia progression.