An Efficient Heterogeneous Ring Signcryption Scheme for Wireless Body Area Networks

Wireless body area networks(WBANs)are an emerging technology for the real-time monitoring of physiological signals.WBANs provide a mechanism for collecting,storing,and transmitting physiological data to healthcare providers.However,the open wireless channel and limited resources of sensors bring security challenges.To ensure physiological data security,this paper provides an efficient Certificateless Public Key Infrastructure Heterogeneous Ring Signcryption(CP-HRSC)scheme,in which sensors are in a certificateless cryptosystem(CLC)environment,and the server is in a public key infrastructure(PKI)environment.CLC could solve the limitations of key escrow in identity-based cryptography(IBC)and certificate management for public keys in PKI.While PKI is suited for the server because it is widely used on the Internet.Furthermore,this paper designs a ring signcryption method that allows the controller to anonymously encrypt physiological data on behalf of a set of sensors,but the server does not exactly know who the sensor is.The construction of this paper can achieve anonymity,confidentiality,authentication,non-repudiation,and integrity in a logically single step.Under the computational Diffie-Hellman(CDH)problem,the formal security proof is provided in the random oracle model(ROM).This paper demonstrates that this scheme has indistinguishability against adaptive chosen ciphertext attacks(IND-CCA2)and existential unforgeability against adaptive chosen message attacks(EUF-CMA).In terms of computational cost and energy usage,a comprehensive performance analysis demonstrates that the proposed scheme is the most effective.Compared to the three existing schemes,the computational cost of this paper’s scheme is reduced by about 49.5%,4.1%,and 8.4%,and the energy usage of our scheme is reduced by about 49.4%,3.7%,and 14.2%,respectively.

Study of Radio over Fiber Transmission with a Single Line in Ring Topology for Deployment of MIMO Antenna Access Points in a Cell-less Network

The massive growth of wireless traffic goes hand in hand with the deployment of advanced radio interfaces as well as network densification. This growth has a direct impact on the radio access architecture, which today is moving from centralized to distributed deployments through the use of a large number of access points (APs). This paper verifies the feasibility of deploying multiple APs in series on a single line in a ring topology in a cell-less network. On the one hand, this technique will further improve the communication capacity and flexibility of a Radio-over-Fiber (RoF) based mobile communication system and will reduce its construction cost. And on the other hand, this deployment topology is a solution to achieve a massive cell-free Multiple-Input Multiple-Output (MIMO) architecture and a cost-effective fronthaul solution. First, a passive optical add/drop multiplexer (OADM) is used to extract and add downlink and uplink signals from the remote access points of one kilometer. Then, a deployment model is developed with version 17 Optisystem software. The results obtained showed that the quadrature amplitude modulation (QAM) does not adapt to this multi-carrier transmission to deploy several AP in series on a single line. Thus, the performance degradation increases when the number of APs integrated on the line increases.

Beam based alignment using a neural network

Beams typically do not travel through the magnet centers because of errors in storage rings.The beam deviating from the quadrupole centers is affected by additional dipole fields due to magnetic field feed-down.Beam-based alignment(BBA)is often performed to determine a golden orbit where the beam circulates around the quadrupole center axes.For storage rings with many quadrupoles,the conventional BBA procedure is time-consuming,particularly in the commissioning phase,because of the necessary iterative process.In addition,the conventional BBA method can be affected by strong coupling and the nonlinearity of the storage ring optics.In this study,a novel method based on a neural network was proposed to determine the golden orbit in a much shorter time with reasonable accuracy.This golden orbit can be used directly for operation or adopted as a starting point for conventional BBA.The method was demonstrated in the HLS-II storage ring for the first time through simulations and online experiments.The results of the experiments showed that the golden orbit obtained using this new method was consistent with that obtained using the conventional BBA.The development of this new method and the corresponding experiments are reported in this paper.

Soft measurement model of ring's dimensions for vertical hot ring rolling process using neural networks optimized by genetic algorithm

Vertical hot ring rolling(VHRR) process has the characteristics of nonlinearity,time-variation and being susceptible to disturbance.Furthermore,the ring's growth is quite fast within a short time,and the rolled ring's position is asymmetrical.All of these cause that the ring's dimensions cannot be measured directly.Through analyzing the relationships among the dimensions of ring blanks,the positions of rolls and the ring's inner and outer diameter,the soft measurement model of ring's dimensions is established based on the radial basis function neural network(RBFNN).A mass of data samples are obtained from VHRR finite element(FE) simulations to train and test the soft measurement NN model,and the model's structure parameters are deduced and optimized by genetic algorithm(GA).Finally,the soft measurement system of ring's dimensions is established and validated by the VHRR experiments.The ring's dimensions were measured artificially and calculated by the soft measurement NN model.The results show that the calculation values of GA-RBFNN model are close to the artificial measurement data.In addition,the calculation accuracy of GA-RBFNN model is higher than that of RBFNN model.The research results suggest that the soft measurement NN model has high precision and flexibility.The research can provide practical methods and theoretical guidance for the accurate measurement of VHRR process.

A Ring Signature Suitable for Wireless Sensor Networks

Wireless Sensor Network （WSN） nodes are severely limited by their power, communication bandwidth, and storage space, and the traditional signature algorithm is not suitable for WSN environments. In this paper, we present a ring signature scheme designed for WSNs. In this scheme, all of the wireless sensor nodes are divided into several sub-groups and the sub-group nodes are used to generate the signature instead of the WSN cluster nodes. This scheme can effectively avoid the single node failure problem, and it also has a high availability. All nodes are flee to sign their own message, and the nodes that generate signatures can simultaneously calculate their own part of the signature, meeting the distributed parallel computing requirements. Compared with the traditional ring signature, this scheme reduces the energy consumption, and therefore is very suitable for WSNs.

Synchronizability of Two-Layer Cluster Ring Networks

Multilayer network is a frontier direction of network science research. In this paper, the cluster ring network is extended to a two-layer network model, and the inner structures of the cluster blocks are random, small world or scale-free. We study the influence of network scale, the interlayer linking weight and interlayer linking fraction on synchronizability. It is found that the synchronizability of the two-layer cluster ring network decreases with the increase of network size. There is an optimum value of the interlayer linking weight in the two-layer cluster ring network, which makes the synchronizability of the network reach the optimum. When the interlayer linking weight and the interlayer linking fraction are very small, the change of them will affect the synchronizability.

Prediction of Partial Ring Current Index Using LSTM Neural Network

The local time dependence of the geomagnetic disturbances during magnetic storms indicates the necessity of forecasting the localized magnetic storm indices.For the first time,we construct prediction models for the SuperMAG partial ring current indices(SMR-LT),with the advance time increasing from 1 h to 12 h by Long Short-Term Memory(LSTM)neural network.Generally,the prediction performance decreases with the advance time and is better for the SMR-06 index than for the SMR-00,SMR-12,and SMR-18 index.For the predictions with 12 h ahead,the correlation coefficient is 0.738,0.608,0.665,and 0.613,respectively.To avoid the over-represented effect of massive data during geomagnetic quiet periods,only the data during magnetic storms are used to train and test our models,and the improvement in prediction metrics increases with the advance time.For example,for predicting the storm-time SMR-06 index with 12 h ahead,the correlation coefficient and the prediction efficiency increases from 0.674 to 0.691,and from 0.349 to 0.455,respectively.The evaluation of the model performance for forecasting the storm intensity shows that the relative error for intense storms is usually less than the relative error for moderate storms.

Performance Evaluation and Pseudo-Conservation Laws in Token Ring Network with Batch Arrivals and Limited Services

This paper,using pseudo-conservation laws in cyclic-service systems, derives some expressions for the weighted sum of the mean waiting time token ring networks with exhaustive limitedservice policies on condition that messages arrived with batch Poisson, and discusses boundary conditions. At the same time, the results of the token ring network with exhaustive and non-exhaustiveservice strategy are obtained. Finally the exact expression of mean waiting time in symmetric ringnetwork with same service strategy is given.

Using Neural Network to Predict the Interfacial Friction and Flow Stress of Materials via Ring Compression

In this study a neural network approach is proposed to realize an automatic numerical prediction of the interfacial friction factor and the flow stress of materials. Decrease in the inner diameter and reduction in the height of the ring are taken as input

ORPOM model for optimum distribution of tree ring sampling based on the climate observation network

Tree ring dating plays an important role in obtaining past climate information.The fundamental study of obtaining tree ring samples in typical climate regions is particularly essential.The optimum distribution of tree ring sampling sites based on climate information from the Climate Observation Network(ORPOM model) is presented in this article.In this setup,the tree rings in a typical region are used for surface representation,by applying excellent correlation with the climate information as the main principle.Taking the Horqin Sandy Land in the cold and arid region of China as an example,the optimum distribution range of the tree ring sampling sites was obtained through the application of the ORPOM model,which is considered a reasonably practical scheme.

The Kemeny’s Constant and Spanning Trees of Hexagonal Ring Network

Spanning tree(τ)has an enormous application in computer science and chemistry to determine the geometric and dynamics analysis of compact polymers.In the field of medicines,it is helpful to recognize the epidemiology of hepatitis C virus(HCV)infection.On the other hand,Kemeny’s constant(Ω)is a beneficial quantifier characterizing the universal average activities of a Markov chain.This network invariant infers the expressions of the expected number of time-steps required to trace a randomly selected terminus state since a fixed beginning state si.Levene and Loizou determined that the Kemeny’s constant can also be obtained through eigenvalues.Motivated by Levene and Loizou,we deduced the Kemeny’s constant and the number of spanning trees of hexagonal ring network by their normalized Laplacian eigenvalues and the coefficients of the characteristic polynomial.Based on the achieved results,entirely results are obtained for the M鯾ius hexagonal ring network.

Parametric Stabilization of the Ring and Linear Neural Network with Two Delays

This paper is devoted to the problem of stabilizing a Hopfield-type neural network with bi-directional ring architecture and two delays. The delay-independent and delay-dependent stability conditions are explicitly presented by the method of the characteristic roots and the skill of mathematical analysis. Moreover, if a link between the adjacent two neurons is cut, the ring neural network turns to a linear one, and the stability results are also established. Furthermore, a comparative analysis for the ring and linear network shows that the stability domain is enlarged after the breaking.

Eigenvalue Spectrum and Synchronizability of Double-Layer Directed Ring Networks

The synchronizability of multiplex undirected regular networks has been intensively studied based on the study of the synchronizability of single-layer networks. However, most real networks are characterized by some degree of directionality. So multiplex directed networks can better explain the synchronizability phenomenon. Here, based on the theory of master stability function (MSF), we study the eigenvalue spectrum and synchronizability of double-layer inter-layer directed ring networks (Networks-A) and double-layer intra-layer directed ring networks (Networks-B). The eigenvalue spectrum of the supra-Laplacian matrix of the networks is rigorously derived, and the influence of the networks structure parameters on the network’s synchronizability is analyzed. The correctness of the theory is further verified by numerical simulation analysis. Finally, the synchronizability of four kinds of double-layer ring networks with different coupling modes, namely, Networks-A, Networks-B, Networks-C (double-layer undirected ring networks), and Networks-D (double-layer undirected inter-layer random-added-edge ring networks), is compared and the results can provide guidance for constructing the optimal synchronization network.

Ring Network Technology for Carrier Ethernet

The fast development of IP services forces the traffic flow in the networks to move gradually from Time Division Multiplexing(TDM) services to packet data services.To adapt to the change,low-cost Ethernet technologies are introduced into the Metropolitan Area Network(MAN).Ring network control protocols and ring network protection technologies are applied to guarantee manageability and reliability required in the telecom networks.The Ethernet Ring Protection(ERP) and Resilient Packet Ring(RPR) are two different ring protection technologies.The Transport Multi-Protocol Label Switching(T-MPLS) network uses the T-MPLS Shared Protection Ring(TM-SPRing) technology to provide ring protection.Carriers can expand the protection as needed and use appropriate ring technologies to meet the requirements of Carrier Ethernet(CE).

An Energy Efficient Random Walk Based Expanding Ring Search Method for Wireless Networks

Wireless networks generate large amount of data. It is important to design energy efficient data search method since power of wireless nodes is finite. Expanding Ring Search (ERS) is a data search technique exploring for targets progressively, which is widely used to locate destinations or information in wireless networks. The existing studies on improving the energy efficiency of ERS cannot work without positioning systems. In this paper, we combine the technique of random walk with ERS, and propose a random walk based expanding ring search method (RWERS) for large-scale wireless networks. RWERS can work without using positioning systems, and improve the energy efficiency of ERS by preventing each node from transmitting the same request more than once using the technique of random walk. We compare RWERS with the optimal ERS strategy and CERS in networks with various shapes of terrains. The simulation results show that RWERS decreases the energy cost by 50% without decreasing in success rate compared with ERS, and has twice the success rate of CERS when the network is sparse. RWERS can be applied to various shapes of terrains better compared with CERS.

基于孪生神经网络的楔形环连接结构预紧状态辨识

楔形环连接结构由于其连接简单可靠、同时兼具节省空间及减重的优势,常被应用于鱼雷、航天飞行器等武器装备。针对楔形环连接结构预紧状态辨识方面存在的机理模型复杂、样本量小且类别不平衡的问题,提出了一种基于孪生神经网络模型的预紧状态辨识方法。为提高模型训练效率和效果,首先利用时频处理技术进行孪生神经网络模型特征增强,基于增强特征建立了3层孪生神经网络分类模型,实现楔形环预紧状态宏观分类。同时,为指导楔形环精密装配,通过特征可视化技术,深入分析了孪生神经网络训练过程特征聚类效果,并基于二维特征建立了预紧状态定量表征模型,引入目标状态聚类中心与接受域参量,用于实现楔形环连接结构预紧状态定量评估。通过试验验证了所提方法的有效性,该方法可为楔形环连接结构定量辨识提供新的技术途径和思路,具有一定工程应用价值。