A RISK ASSESSMENT METHOD OF THE WIRELESS NETWORK SECURITY

The core of network security is the risk assessment. In this letter,a risk assessment method is introduced to estimate the wireless network security. The method,which combines Analytic Hier-archy Process (AHP) method and fuzzy logical method,is applied to the risk assessment. Fuzzy logical method is applied to judge the important degree of each factor in the aspects of the probability,the influence and the uncontrollability,not to directly judge the important degree itself. The risk as-sessment is carved up 3 layers applying AHP method,the sort weight of the third layer is calculated by fuzzy logical method. Finally,the important degree is calculated by AHP method. By comparing the important degree of each factor,the risk which can be controlled by taking measures is known. The study of the case shows that the method can be easily used to the risk assessment of the wireless network security and its results conform to the actual situation.

Backbone formulation algorithm in wireless sensor network based on cross-entropy method

In wireless sensor network,virtual backbone is a cost effective broadcasting method.Connected dominating set formation is proposed to construct a virtual backbone.However,it is NP-Hard to find a minimum connected dominating set in an arbitrary graph.In this paper,based on cross-entropy method,we present a novel backbone formulation algorithm(BFA-CE)in wireless sensor network.In BFA-CE,a maximal independent set is got at first and nodes in the independent set are required to get their action sets.Based on those action sets,a backbone is generated with the cross-entropy method.Simulation results show that our algorithm can effectively reduce the size of backbone network within a reasonable message overhead,and it has lower average node degree.This approach can be potentially used in designing efficient broadcasting strategy or working as a backup routing of wireless sensor network.

Matrix-Geometric Method Based Unified Delay Analysis for Wireless Relay Networks

A lot of work has been focused on desig-ning and analyzing various cooperative diversity pro-tocols for wireless relay networks. To provide a uni-fied queuing analytic framework, we fonmlate an em-bedded Markov chain, which rams out to be a Quasi-Birth-and-Death （QBD） process. Using the Matrix-Ce-ometric method, we can analyze the average delay in a unified way. Theoretical analysis is validated by simu-lation results. We show that the delay performances of Amplify-and-Forward or Decode-and-Forwaxd （AF/ DF） and incremental AF/DF schemes can be analyzed in the unified way. Thus, we can always choose the best cooperative diversity scheme in different scenari-os for delay minimization.

Wireless-LAN安全及认证方法的研究与分析

首先介绍了无线局域网面临的安全问题,并对无线局域网的安全要求进行了分析;介绍了国内外Wire-less-LAN安全认证机制的研究现状,在此基础上详细分析了基于EAP的无线网络安全认证方法;最后对几种主要的认证方法进行了分析比较。

A Formal Reputation System for Trusting Wireless Sensor Network

In this paper, a formal system is proposed based on beta reputation for the development of trustworthy wireless sensor networks （FRS-TWSN）. Following this approach, key concepts related to reputation are formal described step by step for wireless sensor networks where sensor nodes maintain reputation for other sensors and use it to evaluate their trustworthiness. By proving some properties of beta reputation system, the beta distribution is founded to fit well to describe reputation system. Also, a case system is developed within this framework for reputation representation, updates and integration. Simulation results show this scheme not only can keep stable reputation but also can prevent the system from some attacks as bad mouthing and reputation cheating.

Clustering Network Topology Control Method Based on Responsibility Transmission

The topology control is an effective approach which can improve the quality of wireless sensor network at all sides. Through studying the mechanism of sensor network data transmission, the nature of data transmission in wireless sensor network is concluded as a kind of responsibility transmission. By redefining the responsibility and availability of nodes, the strategy for cluster head selection is studied, the responsibility and availability is determined by the combination of the residual energy, location and current flow of nodes. Based on the above, new clustering network topology control algorithm based on responsibility transmission CNTCABRT and hierarchical multi-hop CNTCABRT is presented in this paper, whose algorithm structure is along the famous LEACH algorithm. Experimental result demonstrates its promising performance over the famous LEACH algorithm in the cluster head selection, the size of cluster, the deployment of nodes and the lifetime of nodes, and several innovative conclusions are proposed finally.

Machine Learning Based Classifiers for QoE Prediction Framework in Video Streaming over 5G Wireless Networks

Recently,the combination of video services and 5G networks have been gaining attention in the wireless communication realm.With the brisk advancement in 5G network usage and the massive popularity of threedimensional video streaming,the quality of experience(QoE)of video in 5G systems has been receiving overwhelming significance from both customers and service provider ends.Therefore,effectively categorizing QoE-aware video streaming is imperative for achieving greater client satisfaction.This work makes the following contribution:First,a simulation platform based on NS-3 is introduced to analyze and improve the performance of video services.The simulation is formulated to offer real-time measurements,saving the expensive expenses associated with real-world equipment.Second,A valuable framework for QoE-aware video streaming categorization is introduced in 5G networks based on machine learning(ML)by incorporating the hyperparameter tuning(HPT)principle.It implements an enhanced hyperparameter tuning(EHPT)ensemble and decision tree(DT)classifier for video streaming categorization.The performance of the ML approach is assessed by considering precision,accuracy,recall,and computation time metrics for manifesting the superiority of these classifiers regarding video streaming categorization.This paper demonstrates that our ML classifiers achieve QoE prediction accuracy of 92.59%for(EHPT)ensemble and 87.037%for decision tree(DT)classifiers.

Comparison of electromagnetic exposure to child and adult from electric vehicle wireless power transmission

It is important to verify the safety of electric vehicle(EV)wireless power transmission for child passengers by studying the electromagnetic exposure difference between the child passengers and the adult passengers.The dielectric parameters of the child passengers’body were calculated under the operating frequency of 85 kHz.Using the finite element simulation software COMSOL Multiphysics,a model was established for the child passengers and adult passengers when the EVs charged by the wireless charging coil.This paper analyzed the distribution of magnetic induction intensity and induced electric field intensity generated on the body and head when the child passengers and adult passengers sat in four different positions.Additionally,the difference between the brain electromagnetic exposure values of children and adults was analyzed and compared with the limits set.The results showed that the electromagnetic exposure was the largest when the passenger sat in the co-driver position.The electromagnetic exposure level of child was slightly higher than that of adult at the same position,and the magnetic induction intensity and induced electric field intensity of both were much smaller than the public electromagnetic exposure recommendation values.

Real-Time and Intelligent Flood Forecasting Using UAV-Assisted Wireless Sensor Network

The Wireless Sensor Network(WSN)is a promising technology that could be used to monitor rivers’water levels for early warning flood detection in the 5G context.However,during a flood,sensor nodes may be washed up or become faulty,which seriously affects network connectivity.To address this issue,Unmanned Aerial Vehicles(UAVs)could be integrated with WSN as routers or data mules to provide reliable data collection and flood prediction.In light of this,we propose a fault-tolerant multi-level framework comprised of a WSN and a UAV to monitor river levels.The framework is capable to provide seamless data collection by handling the disconnections caused by the failed nodes during a flood.Besides,an algorithm hybridized with Group Method Data Handling(GMDH)and Particle Swarm Optimization(PSO)is proposed to predict forthcoming floods in an intelligent collaborative environment.The proposed water-level prediction model is trained based on the real dataset obtained fromthe Selangor River inMalaysia.The performance of the work in comparison with other models has been also evaluated and numerical results based on different metrics such as coefficient of determination(R2),correlation coefficient(R),RootMean Square Error(RMSE),Mean Absolute Percentage Error(MAPE),and BIAS are provided.

Data Prediction in Distributed Sensor Networks Using Adam Bashforth Moulton Method

Information collection from remote location is very important for several tasks such as temperate monitoring, air quality investigation, and wartime surveillance. Wireless sensor network is the first choice to complete these types of tasks. Basically, information prediction scheme is an important feature in any sensor nodes. The efficiency of the sensor network can be improved to large extent with a suitable information prediction scheme. Previously, there were several efforts to resolve this problem, but their accuracy is decreased as the prediction threshold reduces to a small value. Our proposed Adams-Bashforth-Moulton algorithm to overcome this drawback was compared with the Milne Simpson scheme. The proposed algorithm is simulated on distributed sensor nodes where information is gathered from the Intel Berkeley Research Laboratory. To maximize the power saving in wireless sensor network, our adopted method achieves the accuracy of 60.28 and 59.2238 for prediction threshold of 0.01 for Milne Simpson and Adams-Bashforth-Moulton algorithms, respectively.

The Study and Applications of Sparse Methods Technology in Yunnan Mountain Substation

As one of the divisions in China Southern Power Grid, Yunnan Power Grid Corporation has conducted research and demonstration projects on multiple smart grid technologies to improve the power system reliability, save operation cost and enhance measurement accuracy. In this paper, we will introduce The Study of Yunnan Mountain Substation Data Aggregation Technology based on Sparse Methods. Most substations are built in the mountain, the complex geological conditions and poor natural conditions put forward higher requirements on the substation running and real-time comprehensive monitoring of substation system. Processing and polymerization research of large amounts of the monitoring data and information is studied in this article. This paper introduces the sparse methods and then explains the thinning algorithm, especially new algorithm is proposed. Finally, the substation sparse method architecture is put forward and the simulation experiment was carried out to prove the feasibility and effectiveness of the proposed method.

Energy-Efficient Distributed Lifetime Optimizing Scheme for Wireless Sensor Networks

In this paper, a sensing model for the coverage analysis of wireless sensor networks is provided. Using this model and Monte Carlo method, the ratio of private range to sensing range required to obtain the desired coverage can be derived considering the scale of deployment area and the number of sensor nodes. Base on the coverage analysis, an energy-efficient distributed node scheduling scheme is proposed to prolong the network lifetime while maintaining the desired sensing coverage, which does not need the geographic or neighbor information of nodes. The proposed scheme can also handle uneven distribution, and it is robust against node failures. Theoretical and simulation results demonstrate its efficiency and usefulness.

A Hybrid Approach to Neighbour Discovery in Wireless Sensor Networks

In the contemporary era of unprecedented innovations such as Internet of Things(IoT),modern applications cannot be imagined without the presence of Wireless Sensor Network(WSN).Nodes in WSN use neighbour discovery(ND)protocols to have necessary communication among the nodes.Neighbour discovery process is crucial as it is to be done with energy efficiency and minimize discovery latency and maximize percentage of neighbours discovered.The current ND approaches that are indirect in nature are categorized into methods of removal of active slots from wake-up schedules and intelligent addition of new slots.The two methods are found to have certain drawbacks.Thefirst category disturbs original integrity of wake-up schedules leading to reduced chances of discovering new nodes in WSN as neighbours.When second category is followed,it may have inefficient slots in the wake-up schedules leading to performance degradation.Therefore,the motivation behind the work in this paper is that by combining the two categories,it is possible to reap benefits of both and get rid of the limitations of the both.Making a hybrid is achieved by introducing virtual nodes that help maximize performance by ensuring original integrity of wake-up schedules and adding of efficient active slots.Thus a Hybrid Approach to Neighbour Discovery(HAND)protocol is realized in WSN.The simulation study revealed that HAND outperforms the existing indirect ND models.

Distributed Target Location in Wireless Sensors Network: An Approach Using FPGA and Artificial Neural Network

This paper analyzes the implementation of an algorithm into a FPGA embedded and distributed target location method using the Received Signal Strength Indicator (RSSI). The objective is to show a method in which an embedded feedforward Artificial Neural Network (ANN) can estimate target location in a distributed fashion against anchor failure. We discuss the lack of FPGA implementation of equivalent methods and the benefits of using a robust platform. We introduce the description of the implementation and we explain the operation of the proposed method, followed by the calculated errors due to inherent Elliott function approximation and the discretization of decimal values used as free parameters in ANN. Furthermore, we show some target location estimation points in function of different numbers of anchor failures. Our contribution is to show that an FPGA embedded ANN implementation, with a few layers, can rapidly estimate target location in a distributed fashion and in presence of failures of anchor nodes considering accuracy, precision and execution time.

Secure Path Cycle Selection Method Using Fuzzy Logic System for Improving Energy Efficiency in Statistical En-Route Filtering Based WSNs

Sensor nodes are easily compromised to malicious attackers due to an open environment. A false injected attack which takes place on application layer is elected by the compromised node. If the false report arrives in a base station, a false alarm is occurred, and the energy of the nodes is consumed. To detect the false report, statistical en-route filtering method is proposed. In this paper, we proposed the secure path cycle selection method using fuzzy rule-based system to consume effective energy. The method makes balanced energy consumption of each node. Moreover, the lifetime of the whole network will be increased. The base station determines the path cycle using the fuzzy rule-based system. The performance of the proposed method is demonstrated using simulation studies with the three methods.

Multi Multi-Task Learning with Dynamic Splitting for Open Open-Set Wireless Signal Recognition

Open-set recognition(OSR)is a realistic problem in wireless signal recogni-tion,which means that during the inference phase there may appear unknown classes not seen in the training phase.The method of intra-class splitting(ICS)that splits samples of known classes to imitate unknown classes has achieved great performance.However,this approach relies too much on the predefined splitting ratio and may face huge performance degradation in new environment.In this paper,we train a multi-task learning(MTL)net-work based on the characteristics of wireless signals to improve the performance in new scenes.Besides,we provide a dynamic method to decide the splitting ratio per class to get more precise outer samples.To be specific,we make perturbations to the sample from the center of one class toward its adversarial direction and the change point of confidence scores during this process is used as the splitting threshold.We conduct several experi-ments on one wireless signal dataset collected at 2.4 GHz ISM band by LimeSDR and one open modulation recognition dataset,and the analytical results demonstrate the effective-ness of the proposed method.

Wireless Infrared Pyrometer with Fiber Optic: Construction and Processing Algorithms

In this paper, developed wireless portable infrared pyrometer with dual channel fiber optic is described. The pyrometer measures surface temperature in wide infrared spectral range of 2 - 25 um. A data processing algorithm based on the methods of synchronous detection providing

Genetic-Chicken Swarm Algorithm for Minimizing Energy in Wireless Sensor Network

Wireless Sensor Network(WSN)technology is the real-time applica-tion that is growing rapidly as the result of smart environments.Battery power is one of the most significant resources in WSN.For enhancing a power factor,the clustering techniques are used.During the forward of data in WSN,more power is consumed.In the existing system,it works with Load Balanced Cluster-ing Method(LBCM)and provides the lifespan of the network with scalability and reliability.In the existing system,it does not deal with end-to-end delay and deliv-ery of packets.For overcoming these issues in WSN,the proposed Genetic Algo-rithm based on Chicken Swarm Optimization(GA-CSO)with Load Balanced Clustering Method(LBCM)is used.Genetic Algorithm generates chromosomes in an arbitrary method then the chromosomes values are calculated using Fitness Function.Chicken Swarm Optimization(CSO)helps to solve the complex opti-mization problems.Also,it consists of chickens,hens,and rooster.It divides the chicken into clusters.Load Balanced Clustering Method(LBCM)maintains the energy during communication among the sensor nodes and also it balances the load in the gateways.The proposed GA-CSO with LBCM improves the life-span of the network.Moreover,it minimizes the energy consumption and also bal-ances the load over the network.The proposed method outperforms by using the following metrics such as energy efficiency,ratio of packet delivery,throughput of the network,lifetime of the sensor nodes.Therefore,the evaluation result shows the energy efficiency that has achieved 83.56%and the delivery ratio of the packet has reached 99.12%.Also,it has attained linear standard deviation and reduced the end-to-end delay as 97.32 ms.

Research on the Application of Wireless Network in Collecting Road Traffic Information

Due to the characteristics of variability and dispersion in traffic information, to get the reliable real-time traffic information has been a bottleneck in the development of intelligent transportation systems. However, with the development of wireless network technology and mobile Internet, the mobile phones are rapidly developed and more popular, so it is possible to get road traffic information by locating the mobile phones in vehicles. The system structure for the road traffic information collection is designed based on wireless network and mobile phones in vehicles, and the vehicle recognition and its information computation methods are given and discussed. Also the simulation is done for vehicle recognition and computation based on fuzzy cluster analysis method and the results are obtained and analyzed.

A Review of Identity Methods of Internet of Things (IOT)

The Internet of Things (IOT) is a recent technology originating from the field of sensor networks. It has received significant attention because it is involved in most aspects of our daily lives. The IOT vision makes objects of various kinds become part of the Internet by assigning each object a unique identifier, enabling objects to communicate with each other in the same or different environments. IOT can collect, process, and exchange data via a data communication network. There are many methods for identifying objects;some have existed since the beginning of IOT innovation, such as Radio Frequency Identification (RFID), Barcode/2D code, IP address, Electronic Product Codes (EPC), etc. Continuous development in IOT domain and the large number of objects connected to the Internet daily require an improved identification method to cope with the rapid development in this field. Many modern methods have been proposed recently, based on various technologies such as computer vision, fingerprinting, and machine learning. This paper introduces an overview of IOT and discusses its fundamental elements;it mainly focuses on identification of IOT which is considered the main part that the IOT systems rely on. The paper discusses the existing identification methods for IOT. Moreover, it provides a review of the modern identification methods proposed in recent literature.