International Journal of Internet Protocol Technology (50 papers in press)
Simulation Research of Nodes Target Tracking Algorithm Based on Clustering in Wireless Sensor Network
by Kaibin Wei
Abstract: Since sensor nodes are constrained in cost and volume, they have limited ability in data storage and processing. Therefore, the routing algorithm needs lower time and space complexity for energy conservation. This article design a dynamic cluster head selection mechanism based on backoff time of residual energy of nodes, aiming at energy conservation and low complexity for the algorithm. At the phase of cluster head selection, we reduce the backoff time of nodes with more energy, so it can win the competition in cluster head selection and control the number of cluster heads, by controlling the radius of cluster lead broadcasting. Then the cluster member nodes are chosen according to the hop range to cluster head, whose weights are computed by the distance to cluster head and the residual energy. At the premise of saving self-energy, it can choose different cluster head and save the energy of cluster head to achieve global energy balance and prolong the network lifetime. The simulations verify that the improved algorithm can make reasonable distribution location of cluster head. The membership has close number and the reasonable topology ensure the global consumption of network.
Keywords: WSN; target tracking; LEACH; energy efficient; cluster head.
Secure Encryption Algorithms for Wireless Sensor Networks Based on Node Trust Value
by Li Li
Abstract: The data transmission in the wireless sensor network is affected by the forwarding randomness of the relay node, resulting in poor security encryption performance. In order to improve the security encryption capability of wireless sensor network, a wireless sensor network security encryption algorithm based on node trust value is proposed. The linear coding design of sensor network transmission data is carried out according to the trust degree, and the composite chaotic model is used for recursive analysis of vector quantization in wireless sensor network encryption process, and then the linear block code design of encryption key and decryption key is realized. The regression analysis results of node trust value are used to realize wireless sensor network security encryption. Simulation results show that the security of wireless sensor network encryption used proposed method is better, the distortion of encrypted transmission is lower, and the performance is better.
Keywords: node trust value; wireless sensor network; security encryption; algorithm design.
A Novel Encryption Algorithm for Unstructured Big Data in Wireless Communication Network
by Lixia Hou, Chungeng Ma, Lihui Yang
Abstract: In order to improve the confidentiality and security of transmission through data encryption, a novel encryption algorithm for unstructured big data in wireless communication network is proposed. In this algorithm, an encryption scheme of homomorphic public key is adopted to establish encryption key of unstructured big data, and an elliptic curve homomorphic encryption scheme is also adopted to design public key random number; the integral multiple bit length is adaptively adjusted to fixed-numbered public keys at different block lengths, and the random sampling method is adopted to extract features of encryption bit sequence, on which cyclic shift encryption is performed; encryption key and decryption key are established, and information entropy is adopted as a key sensitivity parameter concerning big data encryption to perform source coding to realize code element frequency detection of encryption output of unstructured big data, on which key padding is performed to optimize data encryption. Simulation results show that in unstructured big data encryption in wireless communication network, the proposed method can provide good confidentiality and steganography and relatively strong anti-attack capability.
Keywords: wireless communication network; unstructured big data; encryption; public key.
Heuristic Scheduling Algorithm for Hybrid Storage Data in the Cloud Computing Environment
by Zhihong Xin
Abstract: In the optimization design of hybrid cloud storage data in the cloud computing environment, it is required to perform heuristic scheduling to data and to improve the ability to send and receive cloud storage data, so a heuristic scheduling algorithm for hybrid storage data in the cloud computing environment based on correlation spectral density feature extraction is proposed. In this algorithm, a distributed structure data model of hybrid storage data in the cloud computing environment is built and adaptive link forwarding control is designed for big data in cloud storage space. Simulation results show that in heuristic scheduling of hybrid storage data in the cloud computing environment, this method can improve the data storage throughput performance and provide good balance of data storage and high fidelity.
Keywords: cloud computing environment; hybrid storage data; heuristic scheduling; feature extraction; spectral analysis.
Real-Time Monitoring System for Passive Energy-saving Houses in the Internet of Things environment
by Huikai Zheng
Abstract: The real-time monitoring system can improve the management of passive energy-saving houses. Therefore, this paper proposes a real-time monitoring system for passive energy-saving houses in the Internet of Things. Construct the overall framework of the system, use DSP integrated signal processor to process real-time information; select PCI9054 bus as output bus transmission mode, control the local bus through PCI, use the transceiver conversion module to analyze the monitoring information in real time; use ADO.NET component library Interact with the database to realize IoT management and control of monitoring information, and implement system software design by wavelet transform algorithm. The simulation results show that the designed system has a monitoring delay of only 2s, and the overall stability is strong. It can process the monitoring information of energy-saving houses in real time.
Keywords: Internet of Things environment; passive; energy-efficient house; monitoring system.
Research on Quick Extraction Method for Integrated Information of Intelligent Transportation System Scheduling Based on Internet of Things
by Xiangjun Tian
Abstract: In order to improve the remote monitoring capability for the integrated information of intelligent transportation system (ITS) scheduling, a quick extraction method for integrated information of ITS scheduling based on tracking and recognition of the distributed Internet of Things (IoT) sensor information fusion is proposed. Using the comprehensive information of IoT node scheduling, the adaptive distributed optimization positioning design of the IoT node is extracted；Combining pattern recognition technology for scheduling information processing;The integrated information mining and scheduling feature extraction is implemented by quantitative fusion tracking method, which realizes the automatic mining of intelligent transportation system integration information.The simulation results show that the proposed method has better automatic clustering for extracting integrated information of ITS scheduling, as well as faster extraction speed and higher matching ability. It also has a good application value in the integrated information of ITS scheduling.
Keywords: Internet of Things (IoT); intelligent transportation system (ITS); scheduling; integrated information; extraction; clustering.
Falsified Data Filtering Method for Smart Grid Wireless Communication based on SVM
by Ying Zhang
Abstract: In order to solve the problems of long filter time, low filter efficiency and low utilization rate of filtered information in traditional data filtering methods, a method of falsified data filtering in smart grid wireless communication based on SVM is proposed. In the initial stage of population search, chaos model is introduced to increase the diversity of individuals, adaptive factors are added into the updating mechanism to increase the global search capability, and falsified feature data is introduced into the fitness function to adjust the classification accuracy and the number of features by using penalty factors. At the later stage of iteration, with the classification accuracy as the objective function. The experimental results show that the filtering accuracy of the proposed method is as high as 99.89%, and the filter time is greatly reduced. The utilization rate of the filtered information is about 90%, and the overall filter efficiency and accuracy are high.
Keywords: smart grid; wireless communication; falsified data filtering method.
Dynamic resource adaptation method for airborne network based on multi-objective optimization
by Jin Guo, Shengbing Zhang
Abstract: Aiming at the problem that the existing methods have low resource utilization, low task submission success rate, high overhead and high consumption in the process of airborne network resource adaptation, a dynamic resource allocation method based on multi-objective optimization for airborne network is proposed. Taking the RAGE delay of the airborne network system model as the objective function, the problem is transformed into the graph dyeing problem. By constructing the dynamic resource migration model on the airborne network server and the application-aware dynamic resource migration model, the average delay in the dynamic resource allocation process of the airborne network can be achieved. The speech completed the key technical analysis of the dynamic resource adaptation of the airborne network. The experimental results show that the method overcomes the shortcomings of the existing methods, improves resource utilization, task submission success rate and deadline satisfaction rate, reduces system overhead and improves user experience.
Keywords: airborne network; dynamic resources; adaptation; key technologies; analysis.
Multi-channel scheduling analysis of dynamic data in wireless networks oriented big data
by Jinjun Ruan
Abstract: In order to reduce the running time of dynamic data multi-channel scheduling in wireless networks, a multi-channel scheduling analysis method based on POMDP(Partially Observable Markov Decision Process) for dynamic data of wireless networks is proposed. Firstly, the data type and dynamic data transmission process in wireless network are analyzed. According to the node's request arrival rate and service rate, the network state transition probability and observation probability are calculated. Load balancing is used as the performance optimization target of dynamic data multi-channel in wireless network. , calculate its performance function. By calculating the observation probability and performance function, the dynamic data multi-channel scheduling analysis for big data wireless networks is finally realized. The experimental results show that the proposed method has high data transmission efficiency and low packet loss rate, and the scheduling operation time is relatively short. The effectiveness of the proposed method is verified.
Keywords: big data; wireless network; dynamic data; multi-channel; scheduling;.
Design of Information Management System for Structural Monitoring Based on Network Fragmentation
by Guoning Yue
Abstract: Aiming at the shortcomings of current information management system, such as low security, poor economy and long response time, a design method of structure monitoring information management system based on network fragments is proposed. Firstly, the overall architecture of the network debris structure monitoring information management system is designed and the requirements of the monitoring information management system are analyzed. Then, based on the analysis of the main characteristics of the network debris monitoring information management system, the modules and subsystems of the system are designed separately, and the system data management scheme is given. In this way, the design of structured monitoring information management system based on network fragments is completed. The experimental results show that the information management system can manage the network information safely and economically. The response time is short, and the overall performance of the structured monitoring information management system is improved.
Keywords: Network Fragmentation; Structural Monitoring; Information Management; System Design.
Research on the Filtering Recommendation Technology of Network Information based on Big Data Environment
by Lei Cui
Abstract: Aiming at the problems of long recommendation time and low accuracy in traditional filtering recommendation methods for network information, a filtering recommendation method based on trusted user rating of network information is proposed. This paper analyses the relationship between the evaluation time of network users and the change of users' interest in the item in different time periods, builds the time forgetting model and time window model of network information, generates time filtering function, filters the historical score of the item by time filtering function, obtains the similarity of users' score, and constructs the trusted user-item rating matrix of network information, to generate the rating recommendation list of target user for network information prediction, and then complete recommendation. The experimental results show that the proposed method has shorter recommendation completion time and higher accuracy.
Keywords: Big data; Network information; Filtering; Recommendation.
Integrated Monitoring Algorithms for Software Data Security Situation on Private Cloud Computing Platform
by Ying Liu, Haitao Liu
Abstract: Aiming at the problems of large monitoring error, poor real-time performance, serious missing detection of abnormal data and high energy consumption in current data security monitoring methods, a comprehensive monitoring algorithm of software data security situation for private cloud computing platform based on scenario entropy is proposed. Based on data redundancy clearance and data security mechanism, data security situation monitoring indicators are selected, and the scenario entropy difference of each index calculated is taken as monitoring target. The experimental results show that the detection error of this method is between 1% and 3%, and it has high monitoring accuracy. The anomalous response delay is between 0.5 and 1 μs, which has high response efficiency. The loss rate of abnormal data is between 0.2% and 0.5%, and the rate of missing abnormal data is low. The monitoring energy consumption is between 60NJ and 75NJ, and the monitoring energy consumption is low.
Keywords: private cloud; data; security situation; monitoring;rnrn.
Open Interactive Education Algorithm Based on Cloud Computing and Big Data
by Jing Wei, Liangguang Mo
Abstract: In order to improve the self-learning ability of cloud computing and scheduling ability of big data resources in open interactive education, an open interactive education algorithm based on cloud computing and big data is proposed. An information flow model for open education big data is constructed, and big data mining is conducted to an open interactive education platform through the association rules mining method based on parallel scheduling to extract semantic ontology information feature quantity of interactive education; spatial attribute clustering is performed in the cloud computing environment according to the feature extraction results, and big data information is scheduled through the multi-feature weight allocation method. Simulation results show that in open interactive education, this method can cause relatively good output performance of big data mining, relatively high accuracy of feature information clustering of open interactive education and relatively strong feature resolution and recognition ability of data output, which meets the educational resource scheduling and allocation requirements of open education.
Keywords: cloud computing; big data; open education; data mining; parallel scheduling.
Wireless Sensor Networks Secure Routing Algorithm Based on Trust Value Computation
by Qingzeng Xu
Abstract: In this paper, the security attack status in WSN is analyzed, and integrated with the features of WSN, a safety trust evaluation mechanism is proposed for WSN. It evaluates the nodes by delivery behavior of sensor nodes and considers the subjective evaluation with trust value recommendation of other nodes, to establish trust evaluation model which is easy to be implemented. It refers to indirect reputation parameters of neighbour nodes and integrates them to the energy reputation parameters, to achieve comprehensive evaluation value of nodes. The credibility is determined by the degree that is beyond or lower that the threshold. Simultaneously, we also take into account the link quality of links and put forward improved routing setting strategy. The simulation results indicate the model can reflect the credibility of nodes accurately and it can effectively protect malicious libel, which also improves the system security and reliability, to shoe the fairness of network.
Keywords: trust value; WSN; neighbour node; TCRA; reputation.
Big Data Network Security Index Correlation Measure Based on the Fusion of Modified Two Order Cone Programming Model
by Xiaohai Lan
Abstract: The existing technology can not meet the needs of large data network security index measurement in the era of big data. New theories and methods need to be explored to support the application of large data. In this paper, an improved second-order cone programming model (MTOCPM) is used as the data expression vector. The power spectral density feature of large data is extracted from a large number of noisy and fuzzy data. The second-order cone programming model of large data information flow is constructed by rough concept lattice method. Combining with the finite convergence of the second-order cone programming model algorithm, the reliability of each clustering sample is measured, and the accurate search of data clustering center is realized. Experiments show that the method based on MTOCPM fusion can not only greatly reduce the communication cost between network nodes, but also improve the global measurement accuracy by about 10%.
Keywords: Big Data; Modified Two Order Cone Programming Model; Network Security; Correlation Measure.
Research on the On-Demand Scheduling Algorithm of Intelligent Routing Load based on SDN
by Zheng Ma, Yan Ma, Xiaohong Huang, Manjun Zhang, Bo Su, Liang Zhao
Abstract: Aiming at the traditional on-demand scheduling method of routing load, there are many problems, such as long time to complete the total task and high energy consumption. An on-demand scheduling method of routing load based on multiple SINKs is proposed. The total routing load task is sent from multiple SINK nodes to SDN network. After receiving the sub-tasks sent by SINK, the intra-cluster nodes begin to collect network data together. After the cluster heads fuse the network data transmitted by the nodes, the results are sent to SINK nodes. As a result, the shortest total task completion time is obtained, and the shortest total task completion time is used as the objective function of routing load scheduling on demand. Ant colony algorithm is used to solve the problem and complete the scheduling. Experimental results show that the proposed method has shorter completion time and lower energy consumption of total task.
Keywords: SDN; routing load; on-demand scheduling; intelligence.
Deformation Detection Algorithm of Shallow and Large-span Tunnel Support Structure based on Wireless Sensor Network
by Huawei Wu, Chuan Sun, Yicheng Li, Yong Kuang
Abstract: In order to improve the security of tunnel support structure, a deformation detection algorithm of shallow and large-span tunnel support structure based on wireless sensor network is proposed. In this algorithm, a randomly distributed autonomous network grid structure is adopted for deployment of wireless sensor network nodes, and pressure sensors are adopted to acquire deformation force parameters concerning shallow and large-span tunnel support structure; the acquired deformation force parameters are done with sensor quantification and fusion to extract power spectrum feature quantity of the senor parameters; the time-frequency feature decomposition method is adopted to perform dependency determine for deformation sensor data concerning shallow and large-span tunnel support structure, to implement beam forming processing of the deformation parameters, and then according to the formed beam width and peak value, deformation amplitude detection and correlation parameter estimation are performed to improve quantitative analysis capability of structural deformation detection. Experimental results show that when the method is adopted in deformation detection of shallow and large-span tunnel support structure, the parameter estimation error is relatively small, which improves detection performance.
Keywords: wireless sensor network; shallow and large-span tunnel; support structure; deformation detection.
Optimization Method of MAC protocol Based on SVM Neural Network in VANET
by Yucai Zhou, Xiaoya Xu, Caihong Liu, Yuelin Li
Abstract: Back off process is the important embodiment of the competition for wireless channel. The setting of the rational contention window is directly related to the communication quality. The optimal value of minimum contention window in IEEE 802.11 is related with a lot of network state information, such as competition node number in network, average length of collision data frame and data frame transmission speed and so on. This paper analyses the function expression of the optimal minimum competition window which integrates the network node number, average collision data frame length and sending rate of data frame. At the same time, the proposed optimized Method of MAC protocol combines with the SVN Neural Network which can memory communication environment which include node density and mobile velocity. Each terminal node in the network runs proposed MAC protocol optimization algorithm based on this function expression to adaptive adjust their minimum competition window and back off the optimal value to improve the network performance. The simulation results show that the effort of optimized algorithm in the Ad Hoc system is limited for unsaturated business VANET; But high accuracy and effect of the optimized algorithm in aspects of throughput and transmission delay has improved significantly for saturated business of VANET.
Keywords: VANET; IEEE 802.11; MAC protocol; SVM Neural Network.
Abnormal Network Data Mining Model based on Deep Training Learning
by Xiaoling Jiang, Hui Zhang, Jiaming Xu, Weicheng Wu, Xinyong Xie
Abstract: Aiming at the problems of low detection efficiency and poor clustering effect in traditional abnormal network data mining process, an abnormal data mining model based on kernel extreme learning machine and particle swarm optimization is proposed. The enhanced local linear embedding algorithm is used to extract the features of abnormal network data, and the required feature dimensions are extracted repeatedly to obtain the corresponding features of target network data. K-means algorithm is introduced to cluster the target network data to increase the identification of data mining. By improving the particle swarm optimization algorithm to optimize the parameters of the Kernel limit learning machine, the final abnormal data mining results are the best. The experimental results show that the proposed method has high detection efficiency and good clustering effect, which fully proves the superiority of the proposed method and lays a foundation for the progress of abnormal network data mining technology.
Keywords: Local linear embedding algorithm; Target network data characteristics; K-means algorithm; Improved particle swarm optimization algorithm.
Graph-based Recommendation by Trust
by Liejun Wang, Long Pan, Ji-Wei Qin
Abstract: The recommendation system as an effective tool is used to alleviate the information overload problem, and is being applied to personalized services. In recommendation, users ratings as explicit feedback data can clearly express users preference, however explicit feedback data has a natural defect that users interest for an item would varies in context such as emotions, time etc. and the ratings couldnt reflect the changing. In this paper, a novel graph recommendation algorithm is presented based on users trust relation that is regarded as implicit feedback data to calculate similarity to enhance the performance for Top-K recommendations. By evaluating the presented algorithm and compared to four competitive algorithms on the four real world datasets, the results show that the presented algorithm performs better than other algorithms in Precision, Recall and Converge.
Keywords: Recommendation; Graph-based Algorithm; Implicit Feedback; Social Networks.
Software Design of Intelligent Interactive Autonomous Learning System
by Xuewen She
Abstract: Aiming at the problems of poor quality of service, low performance of system cluster and low accuracy of parameter calculation in current software design method of autonomous learning system, a software design method of intelligent interactive autonomous learning system is proposed. Through system architecture design, system module design, system interaction design, system network design and system database design, the overall structure of interactive autonomous learning system software is constructed, and the project parameters and capability parameters are calculated by maximum likelihood method and expert estimation method. According to the user's own ability, personalized test questions and learning content are set for users, and the software design of autonomous learning system is realized. The experimental results show that the proposed method has high quality of service, good cluster performance and high accuracy of parameter calculation.
Keywords: Interactive; Autonomous learning; System design; System cluster;.
A novel optimal road layout model based on urban cloud data
by Lede Niu, Liran Xiong
Abstract: The model in traditional methods is poor in stability, efficiency and accuracy in urban road layout. In order to solve those problems, an optimal road layout model based on urban cloud data is proposed in this paper. In this model, the hardware is supplemented by GIS application software and Arc GIS technology is adopted for integrated development mode; geographic information is processed according to urban cloud data. Starting from road layout, simulation software such as VISSIM and VISSUM is combined on the designed model for traffic data sharing and simulation decision; software is designed based on calibration processing of 3S optimal urban road layout model parameters and traffic data are collected for road layout design, and the optimal layout is extracted through the genetic algorithm.The experimental results show that the method has high stability and design efficiency.
Keywords: Cloud data; GIS; Arc GIS technology; optimal layout; model; genetic algorithm.
A Novel Network Intrusion Prevention System Based on Android Platform
by Guanlin Chen, Kunlong Zhou, Yubo Peng, Liang Zhou, Yong Zhang
Abstract: With the popularity of wireless networks in recent years, the mobile phone users accounted for 95.1% in China. Meanwhile, the security issues cannot be ignored. The cost of wireless attacks is getting lower, and there are more frequent occurrence of wireless fishing, wireless crack and other security events. However, the current traditional intrusion prevention method can only cope with relatively simple attack scenarios. Therefore, the development of new intrusion prevention system is particularly important. In this paper, we design and implement a novel network intrusion prevention system, which uses VpnService and TcpDump to capture traffic as a data source, cooperates with the single-step attack rule signature database and the attack chain signature database to perform real-time intrusion detection, and combines intent analysis to detect intrusion intention-behavior and output alarms. The results show that the system is effective in recognizing typical wireless attacks.
Keywords: WiFi; Intrusion Prevention System; Intrusion Intent; TcpDump.
A novel Neuro-Fuzzy based localization system for WSN using node proximity
by Somnath Sinha, Aditi Paul
Abstract: Localization is a challenging issue in wireless sensor network. This paper describes a neural network and fuzzy logic based approach for localization in Wireless Sensor Network (WSN). The Received Signal Strength Indicators (RSSIs) of some anchor nodes are used as basic parameter to estimate the location of sensor nodes. Using Fuzzy Logic the RSSI values of anchor nodes are categorized into some predefined regions and RSSI patterns are generated using Fuzzy Inference rules. These patterns are then used as input to a trained Neural Network (NN) for estimating a proximity factor of sensor nodes which in turn is used to calculate their positions. The RSSI patterns are used to find out the weighted position of the anchor nodes which when divided by the proximity factor gives the estimated position of the sensor nodes. A modified back propagation method is used to train the neural network. Proposed model is tested using network simulator NS2 and result shows accuracy up to 95%.
Keywords: wireless sensor network (WSN); fuzzy logic system; localization; receive signal strength indicator (RSSI); neural network; proximity.
Unsaturated traffic oriented spatial clustering multiusers MAC protocol for the next generation WLAN
by Yong Li, Bo Li, Mao Yang
Abstract: The next generation wireless local area network (WLAN) needs to significantly improve area throughput in high-dense deployment scenarios. Facing to the interference spread problem of OFDMA MAC, some existing studies introduce spatial clustering group-based OFDMA protocol (SCG-OFDMA), which enables geographically close nodes to send uplink OFDMA data simultaneously. However, SCG-OFDMA assumes that all the nodes have saturated traffic, which cannot be directly extended to the unsaturated traffic case. In this paper, an unsaturated traffic oriented spatial clustering multiusers MAC protocol (SCM-MAC) is proposed. Specifically, spatial clustering multiusers (SCM) discovery process is designed. After that, whether the cluster heads have traffic or not, they contend channel for their group members. Moreover, theoretical analysis of the optimal access radius is derived. Simulation results are in agreement with the theoretical analysis, and show that the area throughput of SCM-MAC is 34% and 50% higher than that of IEEE 802.11ax and OMAX protocol, respectively.
Keywords: the next generation WLAN; high dense deployment scenario; unsaturated traffic; spatial clustering multiusers discovery; OFDMA.
Energy Management Control System of Prefabricated Construction Based on Internet of Things Technology
by Lu Wang, Difei Jiang
Abstract: Energy management control of prefabricated construction is affected by the environment temperature and the internal structure of construction, and it is prone to energy drift distortion and larger energy consumption cost of the construction. This paper proposed an energy management control system of prefabricated construction based on internet of things (IoT) technology. ISA/EISA/Micro Channel were used as expansion bus. Program loading and cross-compiling methods were adopted to realize instruction complication and loading. And the energy management control system of prefabricated construction was developed under the low-power consumption Visual DSP++ 4.5 development environment according to the instruction loading results. The control constraint parameters of energy management control system of prefabricated construction were determined. The construction temperature, power consumption and electricity consumption were used as constraint indexes. Combined with the indexes, the inversion integral control law and the fuzzy PID control law were adopted to optimize and improve the prefabricated construction control system. In order to verify the effectiveness of the system designed in this paper, a simulation comparison experiment was carried out. The experimental results showed that the root mean square (RMS) error of energy management under the prefabricated construction energy management control system was convergent to 0.0101, which was 12.23% and 21.87% lower than that of traditional methods. In the condition of reducing energy consumption cost of construction, the control system designed in this paper also had the advantages of high accuracy and shorter time-consumption.
Keywords: Internet of Things (IoT) Technology; Prefabricated construction; Energy management; Control; Energy consumption.
Mathematical Model Construction of Communication Security Assessment Method based on TST Switching Network Matrix
by Jingbo Hu
Abstract: In view of the shortcomings of the existing communication security assessment methods, such as long communication transmission delay and high packet loss rate, a communication security assessment method integrating TST(timeslot switching) switching network matrix is proposed. Firstly, a TST switching network matrix needs to be created, then control measures are selected according to the communication security standards, and the weight values corresponding to the control measures are solved. According to the weight value, the communication control measures are classified. Finally, the best communication scheme is obtained by calculating the compromise rate, and the mathematical model of the communication security assessment method is constructed. Experimental results show that this method has short communication delay, low packet loss rate and high security.
Keywords: TST matrix; multiplexing; digital switching; communication security assessment.
Research on resource allocation algorithm of wireless network based on game feedback
by Juefu Liu
Abstract: In order to solve the problems of poor denoising low resource utilization, low fairness and serious interruption in resource allocation algorithm of wireless network based on chaotic immune algorithm,, a wireless network resource allocation algorithm based on game feedback is proposed in this paper, which combines wavelet transform domain interpolation with low-pass filter combination.Effective denoising of transmission resources ; classification of users based on game theory,, establishment of optimization utility function,.analysis of channel distribution. By comparing the two existing algorithms, the simulation environment is constructed, and the performance comparison experiments are carried out from four aspects: denoising effect, resource utilization, fairness and interruption. The results show that the algorithm is highly de-noising , improve the efficiency of resource utilization and fairness of allocation, and allocates 0 interruption rate. Therefore, the method can flexibly configure and dynamically adjust the available resources of wireless transmission part to maximize transmission efficiency.
Keywords: Game feedback; Wireless network; Resource allocation.
An Extended Mechanism to Prevent Distributed Denial of Service Attack in DV-Hop Localization Algorithm in Wireless Sensor Networks
by Simarjeet Kaur, Navdeep Kaur, Kamaljit Singh Bhatia
Abstract: Node localization is the procedure for finding the physical position of the nodes in a Wireless sensor network (WSN). The network performance is enhanced with nodes precise location. DV (Distance Vector) - Hop localization scheme is one of the most successful localization schemes. It executes competently with the assistance of beacon nodes. However, when network is interrupted with Distributed Denial of Service (DDoS) attack then the localization process of DV-Hop scheme is badly affected. This paper has examined the effects of the DDoS attack on the DV-Hop algorithm. So, a mechanism consisting of swarm optimization and machine learning is proposed to guard against DDoS attack. The architecture also designs a novel fitness function for Artificial Bee Colony (ABC). To check the appropriate effectiveness of the proposed fitness function of ABC, Feed Forward Neural Network with Gradient satisfaction is applied. The cross validation architecture further uses Mean Square Error (MSE) for the back propagation model and effectiveness is checked through a regression model as well. The evaluation is done in terms of Localization error and Transmission loss. The simulation results are obtained using Matlab and show the reduction of 33% for localization error and 27% for transmission loss respectively.
Keywords: wireless sensor networks; node localization; DV-Hop; distributed denial of service attack; swarm intelligence; machine learning.
New Media Fine Art Education Platform Based on Internet of Things Technology
by Ruixia Feng, Tinghua Li, Junliang Dong
Abstract: In order to solve the problems of poor real-time output performance, poor resource recall performance and high execution time cost of the new media art information platform, this paper proposes to build a new media art education platform based on Internet of Things technology. Firstly, the overall framework of the Internet of Things platform is designed and the functional modules are divided for optimization. Secondly, the VXI bus (vmebus extension for instrumentation) technology is used to optimize the resource acquisition. The comparative experiments show that the new media art education platform in this paper has good resource recall performance, and provides a relatively high the recall rate of resource retrieval, 21% and 14% higher than the other two methods respectively. The execution time cost increases with the increase of the amount of educational resources data, but the execution time cost of this method is obviously less than that of the other two methods.
Keywords: Internet of Things (IoT) Technology; new media; fine art education platform; embedded.
Simulation on Dynamic Load Balancing of Distributed Parallel Computing Network System
by Huawei Wu, Chuan Sun, Yicheng Li, Yong Kuang
Abstract: In order to improve the dynamic load balancing scheduling capability of distributed parallel computing network system and the throughput performance of network, a dynamic load balancing scheduling algorithm of distributed parallel computing network system is proposed. In this algorithm, a channel transmission model for task scheduling of distributed parallel computing network is constructed; the adaptive link equalization method is adopted to perform balanced design of distributed parallel computing network transmission channel; the fractional interval interpolation method is adopted to construct the priority list decision function for dynamic load scheduling in balanced transmission channel; based on the decision function, the balancing scheduling threshold of dynamic load of distributed computing network is determined to reach dynamic load balancing scheduling effect. The experimental results show that the method has strong anti-interference performance and the maximum amplitude of equilibrium state is 0.2. The data transmission throughput performance of distributed parallel computing network is improved.
Keywords: distributed parallel computing network; load; channel; transmission link; balancing scheduling.
Deep Mining Method for High-dimensional Big data based on Association rule
by Shu Xu
Abstract: Existing high dimensional deep data mining methods have the problems of low precision and high energy consumption. Therefore, a deep mining method of high-dimensional big data based on association rules is proposed. Ealat algorithm is used to change the format of high-dimensional large data set. On this basis, MapRdeuce computing model is introduced to divide parallel tasks into map and reduce phases to realize the construction of operation platform. Hadoop's distributed file system is used to store distributed data. The input and output of the algorithm are converted into the form required by the MapRdeuce computing model to realize the deep mining of high-dimensional big data. Experimental results show that this method has higher mining accuracy and lower energy consumption. The result of practical application is good.
Keywords: Association rule; high-dimensional big data; deep mining.
Research on security evaluation system of network information system based on Rough Set Theory
by Yingkai Miao, Jia Chen
Abstract: In view of the poor stability and long response time of the traditional network information system security evaluation system, a rough set based network information system security evaluation system is proposed. Based on the related principles of system safety engineering, the paper constructed the evaluation model of ANN and AHP by using the network topology structure, extracted the basic indexes of related safety evaluation, and processed the basic index data by using the multi-level gray comprehensive evaluation method. Construct the network information system security evaluation index system, use rough set theory to simplify the knowledge, obtain the initial index set, delete the redundant index, and obtain the optimized evaluation index system. The experimental results show that the designed system is more than 80% stable and the system response time is less than 1.32ms, which proves that the designed system is more efficient and stable.
Keywords: rough set; network information system; security evaluation system; evaluation index.
Iterative Learning Feedback Control of Network Inverse System
by Xuelian Yang
Abstract: The traditional iterative learning feedback control has the problems of large feedback error and long searching time. Based on instruction set operation, the hardware part constructs the administrator module, the teacher module and the student module, and carries on the detailed analysis. In the software design of the system, based on the system dynamics method and genetic algorithm, an iterative learning feedback controller for the network inverse system is proposed, which can effectively improve the anti-interference performance of the system. The experimental results show that the feedback error of the designed system is less than 1% and the searching time of learning materials is less than 200 ms, which proves the practicability of the designed system and lays a theoretical foundation for the further development of iterative learning feedback control of network inverse system.
Keywords: Modular teaching; Network inverse system; Iterative learning; Feedback control system.
The Quality Factor for detecting Node Isolation Attack in Mobile Ad hoc Networks Using OLSR Protocol
by Abdellah NABOU, My Driss LAANAOUI, Mohammed OUZZIF
Abstract: The Optimized Link State Routing (OLSR) protocol is a proactive routing protocol for Mobile Ad hoc Network (MANET), it is more suitable for large and dense mobile networks thanks to its new concept of Multi-Point Relay (MPR) nodes that reduce the overhead of the network, unfortunately the performance of OLSR routing protocol can be affected by various routing attacks. Node Isolation Attack is considered as a DOS (Denial of Service) attack that affects the function of OLSR routing protocols precisely by isolating the victim nodes and hiding them from the network. In this paper, we propose a new method to detect the Node Isolation Attack by using a modified version of the Quality Factor (Q) that is applied in the physical domain in order to calculate the Q of OLSR control messages and detect the attack. The experimental results show that when the attack is launched in the network between 30 and 70 second, our method can detect it without any modification in the algorithm of OLSR protocol.
Keywords: MANET; Node; Security; OLSR; MPR; Quality Factor; Node Isolation Attack.
Advance Routing Strategy for VANETs
by Amrit Suman, Chiranjeev Kumar, Preetam Suman
Abstract: The VANET is an autonomous kind of network in which the nodes can create a wireless connection without the control of base stations. In recent years VANET has become a massive point of attraction for researchers because of its unique characteristics that are link failure and frequent changes in topology because of its high mobility. This makes VANET unique from the mobile ad-hoc network. Intelligent transportation that is a part of VANET helps in increasing transportation efficiency, enhancing safety in traffic and, improving the driving experience. Apart from these benefits, Network instability is the main drawback of VANET that reduces network efficiency. Due to various reasons, a high range of security measures required from multiple network attacks to protecting the communication between the vehicular nodes. In this paper, a routing strategy is proposed, which is based on AODV and MAC to enhance the route discovery and to avoid the collision. The proposed routing strategy consists of automatic size adjustment of contention window depending on the network capacity, channel allocation for each node, priority assigning for different types of messages, and security checking for each packet using CRC; all these are based on MAC. Route discovery and table preparation are based on the AODV. The proposed routing protocol (P-Routing) has been implemented in Qualnet 5.0. The results with the performance of C-AODV then compared. The parameters taken for comparison is total byte received, the signal received with error, total packet loss, and throughput. The number of nodes and speeds of nodes kept in variation to create different scenarios. In all cases, the performance of the proposed protocol is better. The results are described with the graphs in the result section.
Keywords: MAC; AODV; Contention window; Message priority; Network Attacks; Collision avoidance; Channel Allocation.
Position-related Content Acquisition for CCM
by Yanli Li, Xiaonan Wang
Abstract: The content-centric networking (CCN) depends on reverse paths and limited broadcast to achieve content acquisition, but the reverse-path disruption due to node mobility leads to content acquisition failures and flooding results in considerable content acquisition costs. In this paper, we propose a Unicast-based content acquisition scheme for Content-Centric MANET (UCCM) to reduce content acquisition costs and improve success rates. UCCM employs the address-centric unicast instead of the content-centric broadcast to achieve the content acquisition, so the content acquisition cost is lowered. Moreover, a requester acquires the content from the nearest provider in the address-centric unicast way, so the content is returned to a requester based on the requesters address rather than the reverse paths. UCCM is evaluated, and the analysis results show that UCCM reduces the content acquisition cost by nearly 63.8% and improve the success rate by nearly 5.9%.
Keywords: Content-centric; address-centric; unicast; broadcast.
Construction of Emergency Dispatching and Controlling Platform for Multi-elevator in Cloud Computing
by Junjun Liu, Jian Wu, Lanzhong Guo
Abstract: Traditional elevator emergency dispatching platforms are limited by their computing power, they cannot automatically collect knowledge and learning information, and they can easily fall into local optimization problems. Therefore, in the context of cloud computing, a multi-elevator emergency dispatching control platform is constructed through fuzzy control technology. According to the fuzzy control input amount, the weighting coefficients of waiting time, driving time and the congestion degree of the cabinet are continuously updated to obtain the optimal weighting coefficient. The maximum membership degree method is used to perform the deblurring processing, and the maximum value in the fuzzy output vector is used as the data after the deblurring processing to obtain the emergency dispatching result of multiple elevators. The results show that the average waiting time of the platform is 20.35s, less than other platform methods, which indicate that this platform has excellent dispatching performance and good application prospects.
Keywords: cloud computing background; multi-elevator; emergency dispatching; control platform; construction.
Analysis Method for Structured Big Data Feature based on Hypernetwork Model
by Shu Xu
Abstract: In view of the problems existing in current big data feature analysis methods, such as long data feature retrieval delay, low comparison fitting index and low success rate of abnormal data extraction, this paper proposes a structured big data feature method based on the hypernetwork model.Based on the commonality of different components, the abnormal data features in big data are extracted.The characteristics of structured big data are analyzed comprehensively by using the hypernetwork model, and the comprehensive data classification is carried out.The hypernetwork model is adopted to realize the structural and clustering analysis of big data features, so as to ensure the quality of data feature analysis.The experimental results show that the proposed method has shorter data feature retrieval delay, the comparison fitting index is basically above 0.9, the fitting effect is better than the literature method, and the success rate of abnormal data extraction is higher.
Keywords: Hypernetwork model; Structured; Data characteristics;.
Study on Network Security Intrusion Target Detection Method in Big Data Environment
by Yingkai Miao, Jia Chen
Abstract: In view of the traditional network security intrusion target detection method can not effectively estimate the trend of the intrusion target, resulting in poor detection accuracy, a new network security intrusion target detection method under the big data environment is proposed. Set up under the environment of big data sequence model of network intrusion in the invasion of the information collected from different data center, according to the binary feature of syntax tree for the intrusion information decomposition, the invasion of the target and get the feature sequences, with closed frequent search method, combining with the characteristics of sequence invasion of target extraction, using path, trends of binary weighted semantic of intrusion path direction get trend path set, exception path is obtained by covariance correction model trend estimation results, achieve network security intrusion detection. The experimental results show that this method has a better performance and better stability in the estimation of intrusion target path trend, with an estimated accuracy of between 94.9% and 98.6% and a detection time of 0.24-0.38s.
Keywords: Big data; network security; intrusion target; path trend; detection.
Real-Time Fault Diagnosis Method for Low-Voltage Power Line Carrier Communication Network Based On Network Topology
by Fangui Li, Hao Wang, Hai Huang, Anxin Chen, Xiaoqing Li
Abstract: In order to overcome the problems of long diagnosis time, high cost and low accuracy in traditional communication network fault diagnosis methods, a real-time fault diagnosis method based on network topology for low-voltage power line carrier communication network is proposed. Detect the fault features around the communication network, get the original fault feature set, input the support vector machine, get the optimal feature subset of the low-voltage power line carrier communication network, use the optimal feature subset to construct the network topology structure, collect different fault information on the protection line, form the fault matrix and calculate, so as to get the optimal feature of the low-voltage power line carrier communication network. It can realize the accurate diagnosis of faults. The experimental results show that the fault diagnosis time of this method is less than 10s, the accuracy of fault diagnosis is up to 95%, and the cost is low.
Keywords: Low-voltage power line carrier; communication network; characteristic set; fault diagnosis.
Design of Hole Repair System for Wireless Sensor Networks based on Triangle Partition
by Zheng Xie, DaQin Wu, HaiYan Hu
Abstract: Aiming at the problems of low coverage, high redundancy and low repair efficiency in the current design methods of network hole repair system, a design method of wireless sensor network hole repair system based on triangular partition method is proposed. The location and shape of holes in the bottleneck area of wireless sensor networks are determined ; the best interior points of the hole edge in the bottleneck area are obtained, and the moving direction and distance of sensor nodes in wireless sensor networks are calculated; the best interior points are selected according to the results of calculation, and the holes in the bottleneck area of wireless sensor networks are self-repaired. The experimental results show that the coverage level of the proposed method is always higher than 80%, and the redundancy can be stabilized below 20%, laying a foundation for the further application of wireless sensor networks in practice.
Keywords: Triangular partition; Wireless sensor network; Bottleneck area; Hole repair.
Small Area Purification and Recognition of Network Intrusion Signals Based On the Second Order Matching Filter Detection
by Yucai Zhou, Lianguang Mo
Abstract: In order to improve the ability of intrusion detection and recognition. This paper proposes a method of small area purification and recognition of network intrusion signal based on second-order matched filter detection. In this method, the time-frequency analysis of network intrusion signal is carried out, and Hilbert Huang transform is used to decompose the time-delay scale of small-scale network intrusion signal, and then the spectrum feature is input into the second-order lattice matched filter to improve the signal resolution, and adaptive weighting method is used to adjust the filter tap coefficient to improve the detection and recognition ability. The simulation results show that the method can accurately recover two groups of component information of network intrusion signal: sinusoidal signal and sinusoidal frequency modulation signal. The recognition accuracy of network intrusion signal can reach 100%, which shows that the method has good signal purification performance.
Keywords: network intrusion signal; detection; filter; recognition; spectral characteristic quantity extraction; time-frequency analysis.
Optimisation of classification algorithm of associated data features of large-scale network system
by Yu Cao
Abstract: Data feature extraction is vulnerable to external interferences during traditional classification of associated data of large-scale network system, resulting in low classification accuracy and large time consumption, so an optimised classification algorithm of large-scale network system associated data features based on deep learning is proposed. In this algorithm, associated data of large-scale network system is acquired through the multi-sensor quantisation fusion method, and the acquired data is done with spectral decomposition to obtain the convergence conditions for feature components of associated data of large-scale network system; then the feature components are processed based on a spectrum analysis model to extract features of associated data of large-scale network system; the features of the associated data are done with piecewise regression analysis, and data samples are output in classification based on the deep learning algorithm. Simulation results show that the proposed method can accurately classify associated data with relatively short learning steps with accuracy 24.7% higher than that of the ARMA classification method and 22.6% higher than that of the decision tree classification method. It is verified that the method proposed in this paper is obviously better than the traditional methods with higher classification accuracy and less time consumption.
Keywords: large-scale network system; deep learning; data classification; feature extraction.
Delay and jitter sensitivity analysis with varying TCP fraction for multiplexed internet communications
by Sneha Thombre, Lalit Patnaik, Anil Tavildar
Abstract: With the increasing usage of TCP protocol, the worsening of delay and jitter performance is a concern affecting QoS for internet communications. Queuing delay and jitter are related to congestion control and occur at the network layer; therefore, the queuing delay and jitter are analysed at the network layer in this paper. Datagrams at the routers are from number of multiplexed flows which constitute a stochastic process. Initially, arrival and service processes for multiplexed TCP and UDP datagrams at the congested router output are modelled. Mean queuing delay, average instantaneous queuing delay and jitter are quantified using queuing theory and the arrival and service process model. The findings of the analytical predictions are validated using NS2 simulations. The interesting observation is that multiplexing of flows hurts the performance of fellow flows. Delay and jitter of the tagged flow are adversely affected by the fraction of TCP in the background traffic.
Keywords: the internet; IP; TCP; UDP; mean queuing delay; end-to-end delay; jitter.
Optimal network selection algorithms under the multi-network coexistence environment based on attribute decision
by Chungeng Ma, Lixia Hou, Wenjing Ma
Abstract: In order to perform optimal network selection under the multi-network coexistence environment and realise adaptive switching of network to improve the stability of network transmission, an optimal network selection algorithm under the multi-network coexistence environment based on attribute decision is proposed. In this algorithm, a network channel distribution model under the multi-network coexistence environment is constructed; the adaptive link priority control method is adopted to perform network channel switching under the multinetwork coexistence environment, and a dynamic scheduling model for network transmission channel under multi-network coexistence is constructed. Simulation results show that when this proposed method is adopted for optimal network selection under the multi-network coexistence environment, the channel equalisation of network transmission and network stability are good and the output bit error rate is low.
Keywords: multi-network coexistence environment; attribute decision; network selection; channel equalisation; bit error rate.
Research on intelligent scheduling strategy of elevator group under the big data platform
by Junjun Liu, Jian Wu, Lanzhong Guo, Mingyang Li, Ming Zhang
Abstract: The traditional elevator dispatching system has a long response time, resulting in poor scheduling performance. To this end, an elevator group intelligent dispatching system is designed under the large data platform. The overall structure of the system is given, and the elevator data is fused under the big data platform. The objective function is determined based on the results of the big data processing. The comprehensive evaluation function of the elevator group intelligent dispatching system is established by linear weighting method. The traffic mode is judged by the elevator group, the constraint rules are given, and the ant colony algorithm is applied to the elevator group intelligent scheduling to obtain the optimal scheduling scheme. The experimental results show that the system can respond to the time period and the elevator runs efficiently. This paper designs the elevator group intelligent dispatching system to have good overall performance.
Keywords: big data platform; elevator group; intelligence; scheduling; system.
Constant temperature control system for indoor environment of buildings based on internet of things
by Lu Wang, Difei Jiang
Abstract: In order to improve the stability of constant temperature control for indoor environment of buildings, a design method of constant temperature optimisation control system for buildings' indoor environment based on internet of things (IoT) is proposed. The system design is divided into two parts – the control algorithm design and the hardware structure design of the control system – to conduct the overall design framework analysis and functional component analysis of indoor environment constant temperature control system. The time-delay feedback error compensation method is adopted to optimise the design for temperature control law for indoor environment of buildings. The test results show that the minimum root-mean-square error of the designed constant temperature control system is 0.021, which significantly reduces the temperature error of the constant temperature control system, indicating that the system has good temperature output stability, strong adaptive adjustment ability and effectively improves the constant temperature control ability of indoor environment.
Keywords: internet of things; IoT; indoor environment of buildings; constant temperature control system; time-delay feedback.
Hybrid model for Chinese character recognition based on Tesseract-OCR
by Bo Wang, Yi-Wei Ma, Hong-Tao Hu
Abstract: Optical character recognition (OCR) is an important way to input information into a computer. And text information can be extracted by OCR from an image. Currently, the accuracy rate of Chinese OCR can also be improved. This study proposes a hybrid Chinese character recognition model based on the characteristics of Chinese. Before the OCR engine works, the model first filters the interference information in the image. Then the model adjusts the aspect ratio of the character. After an image is identified by OCR, single character recognition result is obtained. Then the result is checked and corrected on the phrase level. The experimental results show that the hybrid model improves the accuracy rate of Chinese OCR. Through image processing, the correct rate of recognition by the Tesseract-OCR engine is increased by about 12%, and the natural language processing improves the accuracy of the recognition result by about 5%.
Keywords: hybrid model; image processing; Chinese character; optical character recognition; OCR; phrase processing; K-nearest neighbour; KNN; Tesseract-OCR; single char recognition.
Low-power clustering scheduling algorithm for wireless sensor nodes in the internet of things
by Jianxin Qiu, Lianguang Mo
Abstract: Aiming at the problems of high energy consumption and long packet forwarding time existing in the existing wireless sensor node scheduling algorithm, a low-power scheduling algorithm for wireless sensor nodes in the internet of things based on clustering power consumption balanced scheduling is proposed. In this algorithm, a distributed balancing control model is used for optimised deployment design of wireless sensor nodes and design of node transmission route of the internet of things; the full-network power consumption equalisation model is adopted for path planning of wireless sensor nodes; based on an adaptive route forwarding protocol, clustering balancing control of wireless sensor nodes is performed; the minimum power consumption load is taken as the constraint cost to perform optimised design of clustering forwarding scheduling algorithm for wireless sensor nodes in the internet of things. Simulation results show that in clustering scheduling of wireless sensor nodes in the internet of things, this method costs relatively low power consumption, provides good energy equalisation of the full network, controls the network energy efficiency well, and provides high success forwarding rate of clustering scheduling of sensor nodes and good overall network performance, it has high application advantages.
Keywords: internet of things; wireless sensor node; low-power consumption; clustering scheduling; energy balance.