MULTIPLE ATTRIBUTE NETWORK SELECTION ALGORITHM BASED ON AHP AND SYNERGETIC THEORY FOR HETEROGENEOUS WIRELESS NETWORKS

It is a hot issue in communication research field to select the best network for Heterogeneous Wireless Networks(HWNs),and it is also a difficult problem to reduce the handoff number of vertical handoff.In order to solve this problem,the paper proposes a multiple attribute network selection algorithm based on Analytic Hierarchy Process(AHP)and synergetic theory.The algorithm applies synergetics to network selection,considering the candidate network as a compound system composed of multiple attribute subsystems,and combines the subsystem order degree with AHP weight to obtain entropy of the compound system,which is opposite the synergy degree of a network system.The greater the synergy degree,the better the network performance.The algorithm takes not only the coordination of objective attributes but also Quality of Service(QoS)requirements into consideration,ensuring that users select the network with overall good performance.The simulation results show that the proposed algorithm can effectively reduce the handoff number and provide uses with satisfactory QoS according to different services.

An overview of intelligent selection and prediction method in heterogeneous wireless networks

Heterogeneous wireless access technologies will coexist in next generation wireless networks.These technologies form integrated networks,and these networks support multiple services with high quality level.Various access technologies allow users to select the best available access network to meet the requirements of each type of communication service.Being always best connected anytime and anywhere is a major concern in a heterogeneous wireless networks environment.Always best connected enables network selection mechanisms to keep mobile users always connected to the best network.We present an overview of the network selection and prediction problems and challenges.In addition,we discuss a comprehensive classification of related theoretic approaches,and also study the integration between these methods,finding the best solution of network selection and prediction problems.The optimal solution can fulfill the requirements of the next generation wireless networks.

Hybrid Satellite-UAV-Terrestrial Maritime Networks:Network Selection for Users on A Vessel Optimized with Transmit Power and UAV Position

The hybrid satellite-UAV-terrestrial maritime networks have shown great promise for broadband coverage at sea.The existing works focused on vessels collaboratively served by UAV-enabled aerial base station(ABSs)and terrestrial base stations(TBSs)deployed along the coast,and proved that data rate could be improved by optimizing transmit power and ABS’s position.In practice,users on a vessel can be collaboratively served by an ABS and a vesselenabled base station(VBS)in different networks.In this case,how to select the network for users on a vessel is still an open issue.In this paper,a TBS and a satellite respectively provide wireless backhaul for the ABS and the VBS.The network selection is jointly optimized with transmit power of ABS and VBS,and ABS’s position for improving data rate of all users.We solve it by finding candidates for network selection and iteratively solving transmit power and ABS’s position for each candidate.Simulation results demonstrate that data rate can be improved by collaborative coverage for users on a vessel.

Network selection in cognitive radio enabled Wireless Body Area Networks

In recent years,the great interest in Wireless Body Area Networks(WBANs)has been aroused significantly due to the advancement in wireless communications.In wireless communication,all WBAN nodes that monitor the human body's vital functions transfer information to a central sink node,which is directly connected to a Cognitive Radio enabled Controller called CRC.To transfer this information from a CRC to an e-health server,it requires long-range wireless networks,such as UMTS,LTE,WiMAX,WiFi,and satellite internet provider.It is challenging for a CRC to select the best networks for different WBAN data traffic,such as emergency mandatory,delay sensitive,and general monitoring.This paper proposes a scheme for selecting the best network from the available networks depending on the Quality of Service(QoS)requirements for different WBAN applications.Different multiple attribute decision-making algorithms are used in the proposed scheme.Numerical results and discussion reveal that the proposed scheme is effective in making a good network selection in situations where there is a conflict among different QoS requirements for different WBAN applications.

融合SKNet与MobilenetV3的芒果叶片病虫害分类方法

【目的】针对芒果叶片病虫害缺少数据集和识别准确率低的问题,筛选构建芒果叶片病虫害分类模型,以提高芒果叶病虫害分类准确率。【方法】提出使用去噪扩散模型进行病虫害数据增强,同时联合SKNet与MobilenetV3模型的芒果叶片病虫害分类方法。首先使用去噪扩散模型对数据集进行扩充,再采用多尺度结构相似性指标对生成的病虫害图像与拍摄的病虫害图像之间的相似程度进行评估,接着对DDIM与DCGAN网络训练和生成效果进行比对。在MobilenetV3模型中,将SE注意力模块替换为SKNet模块进行构建网络模型。【结果】使用DDIM生成的所有类型的病虫害图像与拍摄的病虫害图像的MS-SSIM指标均大于0.63,且都高于DCGAN。相较于其他注意力模块,联合SKNet与MobilenetV3的分类效果最佳,在98%以上。对添加CA、CBAM、ECA注意力模块进行平滑类激活图可视化,对比其他注意力模块,使用SKNet注意力分布区域更为集中在病虫害叶片上。【结论】该方法在病虫害叶片检测上具有良好的应用前景,能提升病虫害识别效率与精度,减少检测成本,同时可应用于移动式或者嵌入式设备。

Joint Rain Streaks & Haze Removal Network for Object Detection

In the realm of low-level vision tasks,such as image deraining and dehazing,restoring images distorted by adverse weather conditions remains a significant challenge.The emergence of abundant computational resources has driven the dominance of deep Convolutional Neural Networks(CNNs),supplanting traditional methods reliant on prior knowledge.However,the evolution of CNN architectures has tended towards increasing complexity,utilizing intricate structures to enhance performance,often at the expense of computational efficiency.In response,we propose the Selective Kernel Dense Residual M-shaped Network(SKDRMNet),a flexible solution adept at balancing computational efficiency with network accuracy.A key innovation is the incorporation of an M-shaped hierarchical structure,derived from the U-Net framework as M-Network(M-Net),within which the Selective Kernel Dense Residual Module(SDRM)is introduced to reinforce multi-scale semantic feature maps.Our methodology employs two sampling techniques-bilinear and pixel unshuffled and utilizes a multi-scale feature fusion approach to distil more robust spatial feature map information.During the reconstruction phase,feature maps of varying resolutions are seamlessly integrated,and the extracted features are effectively merged using the Selective Kernel Fusion Module(SKFM).Empirical results demonstrate the comprehensive superiority of SKDRMNet across both synthetic and real rain and haze datasets.

Dynamic Target Wireless Network Selection Technique Using Fuzzy Linguistic Variables

Even though various wireless Net- work Access Technologies （NATs） with dif- ferent specifications and applications have been developed in the recent years, no single wireless technology alone can satisfy the any- time, anywhere, and any service wire- less-access needs of mobile users. A real seamless wireless mobile environment is only realized by considering vertical and horizontal handoffs together. One of the major design issues in heterogeneous wireless networks is the support of Vertical Handoff （VHO）. VHO occurs when a multi-interface enabled mobile terminal changes its Point of Attachment （PoA） from one type of wireless access technology to another, while maintaining an active session. In this paper we present a novel multi-criteria VHO algorithm, which chooses the target NAT based on several factors such as user preferences, system parameters, and traf- tic-types with varying Quality of Service （QoS） requirements. Two modules i.e., VHO Neces- sity Estimation （VHONE） module and target NAT selection module, are designed. Both modules utilize several ＂weighted＂ users＇ and system＇s parameters. To improve the robust- ness of the proposed algorithm, the weighting system is designed based on the concept of fuzzy linguistic variables.

A Throughput-Aware Joint Vehicle Route and Access Network Selection Approach Based on SMDP

In intelligent transportation system(ITS), the interworking of vehicular networks(VN) and cellular networks(CN) is proposed to provide high-data-rate services to vehicles. As the network access quality for CN and VN is location related, mobile data offloading(MDO), which dynamically selects access networks for vehicles, should be considered with vehicle route planning to further improve the wireless data throughput of individual vehicles and to enhance the performance of the entire ITS. In this paper, we investigate joint MDO and route selection for an individual vehicle in a metropolitan scenario. We aim to improve the throughput of the target vehicle while guaranteeing its transportation efficiency requirements in terms of traveling time and distance. To achieve this objective, we first formulate the joint route and access network selection problem as a semi-Markov decision process(SMDP). Then we propose an optimal algorithm to calculate its optimal policy. To further reduce the computation complexity, we derive a suboptimal algorithm which reduces the action space. Simulation results demonstrate that the proposed optimal algorithm significantly outperforms the existing work in total throughput and the late arrival ratio.Moreover, the heuristic algorithm is able to substantially reduce the computation time with only slight performance degradation.

A Network Selection Scheme Based on TOPSIS in Heterogeneous Network Environment

This paper deals with network selection problem for users in heterogeneous network environment. The main context is to improve the TOPSIS( Technique for Order Preference by Similarity to Ideal Solution) network scheme by combining the network properties and the users' requirement accurately and decrease ping-pong effect. The method of entropy and FAHP( Fuzzy Analytic Hierarchy Process) are used to calculate weight value and the sojourn time calculation is used to avoid ping-pang effect. The simulation results show that the improved scheme enhances the more accuracy of network selection than the existing methods and reduces the number of ping-pang effect.

Energy-efficient Scheme for Multiple Access Network Selection Using Principal Component Analysis

This paper brings forward a novel dynamic multiple access network selection scheme(NDMAS),which could achieve less energy loss and improve the poor adaptive capability caused by the variable network parameters.Firstly,a multiple access network selection mathematical model based on information theory is presented.From the perspective of information theory,access selection is essentially a process to reduce the information entropy in the system.It can be found that the lower the information entropy is,the better the system performance fulfills.Therefore,this model is designed to reduce the information entropy by removing redundant parameters,and to avoid the computational cost as well.Secondly,for model implementation,the Principal Component Analysis(PCA) is employed to process the observation data to find out the related factors which affect the users most.As a result,the information entropy is decreased.Theoretical analysis proves that system loss and computational complexity have been decreased by using the proposed approach,while the network QoS and accuracy are guaranteed.Finally,simulation results show that our scheme achieves much better system performance in terms of packet delay,throughput and call blocking probability than other currently existing ones.

Using Extension Theory and Fuzzy Analytic Hierarchy Process on Heterogeneous Wireless Network Selection Algorithm

In order to solve the problem that determining decision factors weights is of subjectivity in heterogeneous wireless network selection algorithm, a network selection algorithm based on extension theory and fuzzy analytic hierarchy process （FAHP） is proposed in this paper. In addition, user and operator codetermine the optimal network using the proposed algorithm, which can give consideration to user and operator benefits. The fuzzy judgment matrix is coustructed by membership degree of decision factors which is calculated according to extension theory. The comprehensive weight of each decision factor is obtained using FAHP. Finally, the optimal network is selected through total property value ranldng of each candidate network under user preference and operator preference. The simulation results show that the proposed algorithm can select the optimal network efficiently and accurately, satisfy user preference, and implement load balance between networks.

A dynamic programming algorithm for network selection in 3G/WLAN

An essential characteristic of the 4th Generation(4G) wireless networks is integrating various heterogeneous wireless access networks.This paper considers the network selection for both admission and handoff strategy problems in heterogeneous network of 3G/WLAN.A novel dynamic programming algorithm is proposed by taking heterogeneous network characteristics,user mobility and different service types into account.The specificity of our approach is that it puts the situations in a new model and makes decisions in stages of different states.Simulation results validate that the proposed scheme can obtain better new call blocking and handoff dropping probability performance than traditional schemes while ensuring quality-of-services(QoS) for both real-time and data connections.

SDN-Based Data Offloading for 5G Mobile Networks

The rapid growth of 3G/4G enabled devices such as smartphones and tablets in large numbers has created increased demand for mobile data services. Wi-Fi offloading helps satisfy the requirements of data-rich applications and terminals with improved multi- media. Wi-Fi is an essential approach to alleviating mobile data traffic load on a cellular network because it provides extra capacity and improves overall performance. In this paper, we propose an integrated LTE/Wi-Fi architecture with software-defined networking （SDN） abstraction in mobile baekhaul and enhanced components that facilitate the move towards next-generation 5G mo- bile networks. Our proposed architecture enables programmable offloading policies that take into account real-time network conditions as well as the status of devices and applications. This mechanism improves overall network performance by deriving real- time policies and steering traffic between cellular and Wi-Fi networks more efficiently.

A Survivability Routing Mechanism in SDN Enabled Wireless Mesh Networks:Design and Evaluation

In software-defined networking,the separation of control plane from forwarding plane introduces new challenges to network reliability.This paper proposes a fault-tolerant routing mechanism to improve survivability by converting the survivability problem into two sub-problems:constructing an elastic-aware routing tree and controller selection.Based on the shortest path tree,this scheme continuously attempts to prune the routing tree to enhance network survivability.After a certain number of iterations,elastic-aware routing continues to improve network resiliency by increasing the number of edges in this tree.Simulation results demonstrate this fault-tolerant mechanism performs better than the traditional method in terms of the number of protected nodes and network fragility indicator.

Efficient Virtual Network Embedding Algorithm Based on Restrictive Selection and Optimization Theory Approach

Network virtualization(NV) is widely considered as a key component of the future network and promises to allow multiple virtual networks(VNs) with different protocols to coexist on a shared substrate network(SN). One main challenge in NV is virtual network embedding(VNE). VNE is a NPhard problem. Previous VNE algorithms in the literature are mostly heuristic, while the remaining algorithms are exact. Heuristic algorithms aim to find a feasible embedding of each VN, not optimal or sub-optimal, in polynomial time. Though presenting the optimal or sub-optimal embedding per VN, exact algorithms are too time-consuming in smallscaled networks, not to mention moderately sized networks. To make a trade-off between the heuristic and the exact, this paper presents an effective algorithm, labeled as VNE-RSOT(Restrictive Selection and Optimization Theory), to solve the VNE problem. The VNERSOT can embed virtual nodes and links per VN simultaneously. The restrictive selection contributes to selecting candidate substrate nodes and paths and largely cuts down on the number of integer variables, used in the following optimization theory approach. The VNE-RSOT fights to minimize substrate resource consumption and accommodates more VNs. To highlight the efficiency of VNERSOT, a simulation against typical and stateof-art heuristic algorithms and a pure exact algorithm is made. Numerical results reveal that virtual network request(VNR) acceptance ratio of VNE-RSOT is, at least, 10% higher than the best-behaved heuristic. Other metrics, such as the execution time, are also plotted to emphasize and highlight the efficiency of VNE-RSOT.

Network Selection Based on Context-Awareness Services

In this paper,we propose a systemic architecture of network selection based on context-awareness services,which gathers contextual information that includes such network information,user information and local information.This network selection strategy considers the Quality of Service(QoS) and user preferences.Also,it perceives contexts such as speed,coverage percentage and location,etc.,and it eventually performs network selection decision making and network execution based on multiple factors.From the perspective of network decision,it presents two network selection algorithms,namely the fuzzy mathematics evaluation method and multiple attribute decision making using the TOPSIS evaluation method.System simulations suggest that network selection based on the mathematics evaluation method is much faster than the TOPSIS evaluation method.However,the TOPSIS evaluation method is practically more efficient.The network selection method based on context-awareness provides an effective and flexible network vertical handover strategy,and ensures a good accuracy and efficiency.

Real-time Business Network Selection Based on Auction Mechanism

In the framework of heterogeneous wireless networks,it is difficult for every user to obtain QoS-based services anywhere at any time.Due to heterogeneous networks,the dynamic network selection scheme needs to achieve seamless mobility,and also supports the optimization of service quality and load balancing.According to different business characteristics,this paper describes different real-time businesses in utility functions,and solves network selection problems for real-time businesses.Based on auction mechanism,it introduces the upset price in order to maximize online profits.Meanwhile,the network selection scheme is also helpful to control network congestion.The study of real-time business network selection based on auction mechanism can not only meet the demands of service quality of multiple realtime applications,but also achieves load balancing between different networks.

基于SKNet的增强型Pix2pixHD图像去雾方法

针对采用去雾算法处理后图片仍存在色彩失真、残雾遗留等问题,提出一种基于SKNet的增强型Pix2pixHD去雾方法,即在增强型Pix2pixHD网络的增强模块中引入SKNet网络,实现了不同尺度特征的选择和融合,提高了算法对图像特征的利用率。此外,在SKNet网络前添加可变形卷积,使得卷积核产生自由形变,以适应不规则的目标物体,增强了模型的可变换能力,达到了很好的去雾效果。较现有的去雾方法,文章提出的网络更好地利用了图像各个尺度的特征细节,提高了图像去雾的效果。

Multiplicative multi-attribute auction based optimal network selection for heterogeneous wireless networks

One of the remarkable features of the next generation network is the integration of heterogeneous wireless networks, which enables mobile users with multi-mode terminals access to the best available network seamlessly. However, most of previous work only takes account of either maximizing single user＇s utility or the whole network＇s payoff, rarely considers the negotiation between them. In this paper, we propose a novel network selection approach using improved multiplicative multi-attribute auction （MMA）. At first, an improved MMA method is put forward to define the user＇s utility. Additionally, user cost is defined by considering allocated bandwidth, network load intensity and cost factor parameter. And last the best suitable network is selected according to the user＇s performance-cost-ration. Simulation results confirm that the proposed scheme outperforms the existing scheme in terms of network selection＇s fairness, user＇s performance-cost-ration, load balancing and the number of accommodated users.

A New Selective Neural Network Ensemble Method Based on Error Vectorization and Its Application in High-density Polyethylene (HDPE) Cascade Reaction Process

Chemical processes are complex, for which traditional neural network models usually can not lead to satisfactory accuracy. Selective neural network ensemble is an effective way to enhance the generalization accuracy of networks, but there are some problems, e.g., lacking of unified definition of diversity among component neural networks and difficult to improve the accuracy by selecting if the diversities of available networks are small. In this study, the output errors of networks are vectorized, the diversity of networks is defined based on the error vectors, and the size of ensemble is analyzed. Then an error vectorization based selective neural network ensemble (EVSNE) is proposed, in which the error vector of each network can offset that of the other networks by training the component networks orderly. Thus the component networks have large diversity. Experiments and comparisons over standard data sets and actual chemical process data set for production of high-density polyethylene demonstrate that EVSNE performs better in generalization ability.