Caching Strategies in NDN Based Wireless Ad Hoc Network:A Survey

Wireless Ad Hoc Networks consist of devices that are wirelessly connected.Mobile Ad Hoc Networks(MANETs),Internet of Things(IoT),and Vehicular Ad Hoc Networks(VANETs)are the main domains of wireless ad hoc network.Internet is used in wireless ad hoc network.Internet is based on Transmission Control Protocol(TCP)/Internet Protocol(IP)network where clients and servers interact with each other with the help of IP in a pre-defined environment.Internet fetches data from a fixed location.Data redundancy,mobility,and location dependency are the main issues of the IP network paradigm.All these factors result in poor performance of wireless ad hoc networks.The main disadvantage of IP is that,it does not provide in-network caching.Therefore,there is a need to move towards a new network that overcomes these limitations.Named Data Network(NDN)is a network that overcomes these limitations.NDN is a project of Information-centric Network(ICN).NDN provides in-network caching which helps in fast response to user queries.Implementing NDN in wireless ad hoc network provides many benefits such as caching,mobility,scalability,security,and privacy.By considering the certainty,in this survey paper,we present a comprehensive survey on Caching Strategies in NDN-based Wireless AdHocNetwork.Various cachingmechanism-based results are also described.In the last,we also shed light on the challenges and future directions of this promising field to provide a clear understanding of what caching-related problems exist in NDN-based wireless ad hoc networks.

基于新型Contention网络的滚动轴承早期故障诊断方法研究

针对滚动轴承早期故障诊断问题,为了同时建模振动信号中的高频和低频特征,实现高精度诊断,提出了一种新的模型Contention。它以一种互补的方式集成了空洞卷积和自注意力机制的优点,具有同时捕捉高频和低频信息的能力。为了验证其诊断能力,首先,在完整信息原则下将振动信号连续构造成数据集;其次,搭建Contention网络并训练,其最终测试集准确度可达100%,t-SNE显示随网络层次的深入特征被明显聚类;最后,设置对照实验,将该模型与传统RNN、CNN、Transformer模型对比,证明该模型具备突出的诊断能力。

Hierarchical Content Caching in Fog Radio Access Networks:Ergodic Rate and Transmit Latency

In order to alleviate capacity constraints on the fronthaul and decrease the transmit latency, a hierarchical content caching paradigm is applied in the fog radio access networks(F-RANs). In particular, a specific cluster of remote radio heads is formed through a common centralized cloud at the baseband unit pool, while the local content is directly delivered at fog access points with edge cache and distributed radio signal processing capability. Focusing on a downlink F-RAN, the explicit expressions of ergodic rate for the hierarchical paradigm is derived. Meanwhile, both the waiting delay and latency ratio for users requiring a single content are exploited. According to the evaluation results of ergodic rate on waiting delay, the transmit latency can be effectively reduced through improving the capacity of both fronthaul and radio access links. Moreover, to fully explore the potential of hierarchical content caching, the transmit latency for users requiring multiple content objects is optimized as well in three content transmission cases with different radio access links. The simulation results verify the accuracy of the analysis, further show the latency decreases significantly due to the hierarchical paradigm.

Caching Algorithm with a Novel Cost Model to Deliver Content and Its Interest over Content Centric Networks

Recently the content centric networks(CCNs) have been advocated as a new solution to design future networks. In the CCNs, content and its interest are delivered over the content store and pending interest table, respectively, where both have limited capacities. Therefore, how to design the corresponding algorithms to efficiently deliver content and inertest over them becomes an important issue. In this paper, based on the analysis of content distribution, status of content store, and pending interest, we propose a novel caching algorithm with which the resources of content store and pending interest table can be efficiently used. Simulation results prove that the proposal can outperform the conventional methods.

A chaos genetic algorithm for optimizing an artificial neural network of predicting silicon content in hot metal

A genetic algorithm based on the nested intervals chaos search (NICGA) hasbeen given. Because the nested intervals chaos search is introduced into the NICGA to initialize thepopulation and to lead the evolution of the population, the NICGA has the advantages of decreasingthe population size, enhancing the local search ability, and improving the computational efficiencyand optimization precision. In a multi4ayer feed forward neural network model for predicting thesilicon content in hot metal, the NICGA was used to optimize the connection weights and thresholdvalues of the neural network to improve the prediction precision. The application results show thatthe precision of predicting the silicon content has been increased.

An Improved Artificial Neural Network Model for Predicting Silicon Content of Blast Furnace Hot Metal

Based on the skills of initializing weight distribution, adding an impulse in a neural network and expanding the ideal of plural weights, an artificial neural network model with three connection weights between one and another neural unit was established to predict silicon content of blast furnace hot metal. After the neural network was trained in the off-line state on the basis of a large number of practical data of a commercial blast furnace and making many learning patterns, satisfactory testing and simulating results of the model were obtained.

Prediction of Free Lime Content in Cement Clinker Based on RBF Neural Network

Considering the fact that free calcium oxide content is an important parameter to evaluate the quality of cement clinker, it is very significant to predict the change of free calcium oxide content through adjusting the parameters of processing technique. In fact, the making process of cement clinker is very complex. Therefore, it is very difficult to describe this relationship using the conventional mathematical methods. Using several models, i e, linear regression model, nonlinear regression model, Back Propagation neural network model, and Radial Basis Function （RBF） neural network model, we investigated the possibility to predict the free calcium oxide content according to selected parameters of the production process. The results indicate that RBF neural network model can predict the free lime content with the highest precision （1.3%） among all the models.

Joint Content Placement and Traffic Management for Cloud Mobile Video Distribution over Software-Defined Networks

To cope with the rapid growth of mobile video, video providers have leveraged cloud technologies to deploy their mobile video service system for more cost-effective and scalable performance. The emergence of Software-Defined Networking(SDN) provides a promising solution to manage the underlying network. In this paper, we introduce an SDN-enabled cloud mobile video distribution architecture and propose a joint video placement, request dispatching and traffic management mechanism to improve user experience and reduce the system operational cost. We use a utility function to capture the two aspects of user experience: the level of satisfaction and average latency, and formulate the joint optimization problem as a mixed integer programming problem. We develop an optimal algorithm based on dual decomposition and prove its optimality. We conduct simulations to evaluate the performance of our algorithm and the results show that our strategy can effectively cut down the total cost and guarantee user experience.

Algorithms to Calculate the Most Reliable Maximum Flow in Content Delivery Network

Calculating the most reliable maximum flow(MRMF)from the edge cache node to the requesting node can provide an important reference for selecting the best edge cache node in a content delivery network(CDN).However,SDBA,as the current state-of-the-art MRMF algorithm,is too complex to meet real-time computing needs.This paper proposes a set of MRMF algorithms:NWCD(Negative Weight Community Deletion),SCPDAT(Single-Cycle Preference Deletion Approximation algorithm with Time constraint)and SCPDAP(Single-Cycle Preference Deletion Approximation algorithm with Probability constraint).NWCD draws on the“flow-shifting”algorithm of minimum cost and maximum flow,and further defines the concept of negative weight community.This algorithm continuously deletes the negative weight communities,which can increase reliability while keeping the flow constant in the residual graph.It is proven that when all negative weight communities are deleted,the corresponding maximum flow is the MRMF.SCPDAT tries to approach the optimal solution to the greatest extent possible within the limited time,while SCPDAP tries to reach the probability threshold in the shortest amount of time.Both of these adopt the strategy of first deleting single-cycle communities(which contribute more to the reliability with lower time cost).Experiments show that,compared with SDBA,NWCD combined with the probabilistic pruning achieves an order of magnitude improvement in time cost,while SCPDAT and SCPDAP demonstrate better time performance and increased applicability.

A Survey of Applications Research on Content-Centric Networking

As a named data-based clean-slate future Internet architecture,Content-Centric Networking(CCN)uses entirely different protocols and communication patterns from the host-to-host IP network.In CCN,communication is wholly driven by the data consumer.Consumers must send Interest packets with the content name and not by the host’s network address.Its nature of in-network caching,Interest packets aggregation and hop-byhop communication poses unique challenges to provision of Internet applications,where traditional IP network no long works well.This paper presents a comprehensive survey of state-of-the-art application research activities related to CCN architecture.Our main aims in this survey are(a)to identify the advantages and drawbacks of CCN architectures for application provisioning;(b)to discuss the challenges and opportunities regarding service provisioning in CCN architectures;and(c)to further encourage deeper thinking about design principles for future Internet architectures from the perspective of upper-layer applications.

An Efficient Update Strategy for Content Synchronization in Content-Centric Networking(CCN)

Content-centric Networking(CCN) is progressively flattering the substitutable approach to the Internet architecture through illuminating information(content) dissemination on the Internet with content forenames.The emergent proportion of Internet circulation has expectant adjusting Content-centric architecture to enhance serve the user prerequisites of accessing content.In recent years,one of the key aspects of CCN is ubiquitous in-network caching,which has been widely received great attention in research interest.One foremost shortcoming of in-network caching is that content producers have no awareness about where their content is put in storage.Because routers in CCN have caching capabilities,therefore,each and every content router can cache the content item in its storage capacity.This is problematic in the case in which a producer wishes to update or make the changes in its content item.In this paper,we present an approach regarding how to address this issue with a scheme called efficient content update(ECU).Our proposed ECU scheme achieves content update via trifling packets that resemble contemporary CCN communication messages with the use of additional table.We measure the performance of ECU scheme by means of simulations and make available a comprehensive exploration of its results.

Super Node Routing Strategy in Content-Centric Networking

There were two strategies for the data forwarding in the content-centric networking(CCN): forwarding strategy and routing strategy. Forwarding strategy only considered a separated node rather than the whole network performance, and Interest flooding led to the network overhead and redundancy as well. As for routing strategy in CCN, each node was required to run the protocol. It was a waste of routing cost and unfit for large-scale deployment.This paper presents the super node routing strategy in CCN. Some super nodes selected from the peer nodes in CCN were used to receive the routing information from their slave nodes and compute the face-to-path to establish forwarding information base(FIB). Then FIB was sent to slave nodes to control and manage the slave nodes. The theoretical analysis showed that the super node routing strategy possessed robustness and scalability, achieved load balancing,reduced the redundancy and improved the network performance. In three topologies, three experiments were carried out to test the super node routing strategy. Network performance results showed that the proposed strategy had a shorter delay, lower CPU utilization and less redundancy compared with CCN.

Contract-Based Content Delivery in 5G Converged Networks

Mobile broadcasting services provided by converged networks do aid in satisfying users' demands for popular multimedia content while unicasting services offer personalized experiences for users.We analyze hybrid broadcasting unicasting framework from the perspective of network economics,where content provider(CP) figures out the cooperation of broadcasting and unicasting services providers,as long as their pricing strategies.To this end,a contract-based content delivery scheme is proposed.The profit of CP depends on users' preference and satisfaction for unicasting and broadcasting transmission.CP provides different users with distinctive data packages.The intent is to maximize its own profit.By classifying users into different types,the optimal contract in close form is derived.Numerical results show that the proposed optimal contract is able to generate incentive for users to employ broadcasting transmission,which further benefits both the CP and users.

Artificial Neural Network to Predict Leaf Population Chlorophyll Content from Cotton Plant Images

Leaf population chlorophyll content in a population of crops, if obtained in a timely manner, served as a key indicator for growth management and diseases diagnosis. In this paper, a three-layer multilayer perceptron （MLP） artificial neural network （ANN） based prediction system was presented for predicting the leaf population chlorophyll content from the cotton plant images. As the training of this prediction system relied heavily on how well those leaf green pixels were separated from background noises in cotton plant images, a global thresholding algorithm and an omnidirectional scan noise filtering coupled with the hue histogram statistic method were designed for leaf green pixel extraction. With the obtained leaf green pixels, the system training was carried out by applying a back propagation algorithm. The proposed system was tested to predict the chlorophyll content from the cotton plant images. The results using the proposed system were in sound agreement with those obtained by the destructive method. The average prediction relative error for the chlorophyll density （μg cm^-2） in the 17 testing images was 8.41%.

Content Centric Networking: A New Approach to Big Data Distribution

In this paper, we explore network architecture anal key technologies for content-centric networking （CCN）, an emerging networking technology in the big-data era. We descrihe the structure anti operation mechanism of tl CCN node. Then we discuss mobility management, routing strategy, and caching policy in CCN. For better network performance, we propose a probability cache replacement policy that is based on cotent popularity. We also propose and evaluate a probability cache with evicted copy-up decision policy.

A Novel Packet Switch Node Architecture for Contention Resolution in Synchronous Optical Packet Switched Networks

Packet contention is a key issue in optical packet switch (OPS) networks and finds a viable solution by including optical buffering techniques incorporating fiber delay lines (FDLs) in the switch architecture. The present paper proposes a novel switch architecture for packet contention resolution in synchronous OPS network employing the packet circulation in FDLs in a synchronized manner. A mathematical model for the proposed switch architecture is developed employing packet queuing control to estimate the blocking probability for the incoming traffic. The switch performance is analyzed with a suitable contention resolution al-gorithm through the computer simulation. The simulation results substantiate the proposed model for the switch architecture.

Deployment of the Content-Based Switching Network

In this paper, we propose a hybrid network architecture, called Content-based Switching Network (CSN), and its signaling scheme, which addresses the issues inherent to conventional hybrid networks which implement a horizontal separation over the entire network (from edge to edge). We will show how CSN nodes can flexibly choose their switching paradigm (store-and-forward, optical bypass, electrical bypass) during a path establishment. Contents being transferred in one piece from end-to-end, the concept of packet can be eluded in our network, and, in particular, the user is able to avoid complicated transport layer functions, like TCP, if they are not essential. In CSN, very large contents have a special status, since they cannot be store-and-forwarded. We will show how the resource management has been designed in order to deal with such contents. A section is dedicated to deployment and feasibility issues. Simulation results will show that CSN can successfully transfer contents at 1 Gbps and 10 Gbps, the maximum speed being limited by the state-of-the-art device technologies when buffering is required (memory speed), while no major limit is observed in the case of all-optical transfers other than the optical fiber speed. Other results concern the deployment of CSN from an unclean slate approach. They will show how beneficial can be the deployment of CSN from an Optical Circuit Switching network.

Reliable Content Distribution in P2P Networks Based on Peer Groups

Peer-to-Peer (P2P) networks are highly dynamic systems which are very popular for content distribution in the Internet. A single peer remains in the system for an unpredictable amount of time, and the rate in which peers enter and leave the system, i.e. the churn, is often high. A user that is obtaining content from a selected peer is frequently informed that particular peer is not available anymore, and is asked to select another peer, or will have another peer assigned, often without enough checks to confirm that the content provided by the new peer presents the same quality of the previous peer. In this work we present a strategy based on group communication for transparent and robust content access in P2P networks. Instead of accessing a single peer for obtaining the desired content, a user request is received and processed by a group of peers. This group of peers, called PCG (Peer Content Group) provides reliable content access in sense that even as members of the group crash or leave the system, users continue to receive the content if at least one group member remains fault-free. Each PCG member is capable of independently serving the request. A PCG is transparent to the user, as the group interface is identical to the interface provided by a single peer. A group member is elected to serve each request. A fault monitoring component allows the detection of member crashes. If the peer is serving request crashes, another group member is elected to continue providing the service. The PCG and a P2P file sharing applications were implemented in the JXTA platform. Evaluation results are presented showing the latency of group operations and system components.

BP Neural Network Based on Microwave Method for Measuring the Moisture Content of Textile

The moisture content of yarn and fabric is an important factor in textiles industry.A novel microwave method used for material moisture content measurements is described in this paper.It can estimate the moisture content of the yarn roll with a standard deviation of 1.58% in the range of 0% to 90.00%.According to the actual size of the yarn,the yarn roll simulation model is established.The microwave attenuation variations arising from the changes in the conductivity and dielectric constant of the wet cone yarn from1.8 GHz to 5.0 GHz frequency are obtained by ultra-wideband antenna.The measured data are analyzed using the BP neural network.The result shows that it is a non-contact and online method to solve the moisture content of the yarn in the wide moisture content range.