International Journal of Internet Protocol Technology (46 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.
Intelligent Switching Algorithm for Mobile Communication under Non-uniform Distribution of Scatterers
by Zhixiang Hou
Abstract: In the presence of non-uniformly distributed scatterer medium, mobile communication is prone to multipath effect, so in order to improve the communication stability by reducing multipath interference through intelligent switching of mobile communication channels, an intelligent switching algorithm for mobile communication under non-uniform distribution of scatterers based on time compression and spatial focusing is proposed. Simulation results show that the proposed method can provide good intelligence in the switching of mobile communication channels under non-uniform distribution of scatterers and it improves channel equalization, provides output communication signals with good spatial focusing capability and reduces transmission delay and inter-symbol interference.
Keywords: scatterer; non-uniform distribution; mobile communication; intelligent switching algorithm; channel.
CRAM: Clustering-based Resource Availability Measurement in Mobile Peer-to-Peer Systems
by Moufida Rahmani, Mahfoud Benchaiba
Abstract: The peer-to-peer (P2P) systems are an alternative to solve the scalability issue raised by the client/server systems. P2P systems are composed of a set of entities that communicate directly without any central server and constitute scalable and robust distributed systems. In these systems, an entity only has a partial knowledge about the overlay state. The challenge is to provide a global knowledge in the system regarding a feature as the resources availability. In this paper, we propose a Clustering-based Resource Availability Measurement called CRAM for mobile P2P networks which relies on knowledge from all entities. CRAM can be used in resource search and in replication strategies to improve the network performances. Simulation results show that our estimation of availability is close to the real one such as the deviation between them can equal 0.008. They also show that our algorithm reacts efficiently to the appearance or depletion of a resource replica in the system.
Keywords: Mobile peer-to-peer; Overlay; Resource Availability; Rare resource; Popularity resource; Clustering; Replication; Hybrid search; Social network.
Analysis of Spatial Information Service Composition Network Based on Ricci Curvature
by Yang Sun, Zuoqin Shi, Ling Zhao, Sumin Liu, Zhenshi Zhang, Ronghua Du, Zhixiang Hou
Abstract: In order to improve the efficiency of spatial information service composition and enhance its stability, this paper proposes to introduce the spatial information service combination network into the geometric space and use Ricci curvature to study the local structure of spatial information service chain and explore the way of connecting to the spatial information service network, and analyze the Ricci curvature and its distribution and excavate the network structure and analyze its meaning. It is found that negative curvature nodes are more heavily loaded and have more service invocations. They are prefer to be distributed over the edge of the graph structure composed of spatial information service chains, and more inclined to the intersection of local structures of the network,the positive curvature on the contrary. And the proportion of negative curvature of this service network is large,which indicates that there is strong connectivity and recall between these different service chains.
Keywords: spatial information service composition; Ricci curvature; complex network.
Research on Adaptive Recommendation Algorithm for Big Data Mining Based on Hadoop Platform
by Jinming Zhang
Abstract: Aiming at the problems of low data recall rate, poor data mining accuracy and poor redundant data interference ability of traditional data mining technology, an adaptive recommendation algorithm for big data mining based on Hadoop platform is proposed. In this algorithm, a distributed storage structure model of big data is constructed on Hadoop cloud platform; the statistical regression analysis method is adopted to construct a big data similarity mathematical model; the autocorrelation matching detection method is adopted to extract correlation features of big data and B
Keywords: Hadoop platform; big data; mining; recommendation algorithm; distributed storage.
Structural Optimization Algorithm of Weight Correlation Analysis for DBN
by Min Zhang
Abstract: The Deep Belief Network (DBN) algorithm has slow convergence rate and low learning efficiency, and it is sensitive to model hyperparameters. In view of the fact that DBN is a learning network model with a large number of network nodes and multiple hidden layers, this paper proposes a weight correlation analysis algorithm for DBN (WCA-DBN) to determine the approximately optimal network structure of DBN. This algorithm establishes a PS orthogonal projection space based on the arbitrary node set using the Linear Discriminant Analysis (LDA) method, projects the balanced node set onto the PS plane, and solves the weights of the network nodes between sets through the correlation principle, to adjust the network structure of the hidden layer adaptively. The real-time traffic flow of six different types of roads in Jinan City, Shandong Province was selected as experimental materials to verify that the WCA-DBN algorithm has more obvious advantages over traditional algorithms in terms of accuracy and time complexity.
Keywords: Deep learning; deep belief network; structural optimization; correlation analysis; linear discriminant analysis; traffic monitoring data of the arearnrn.
The Reliability Research of V2V based on Sample Judgment and Balanced Modulation
by Jian Gao, Shuyun Niu, Honghai LI, Meng WANG
Abstract: In IOV (internet of vehicle) system, V2V (vehicle-to-vehicle) data transmission is influenced by multipath interference, which often causes bad consequences.In order to improve the data transmission reliability, a new estimate algorithm is proposed, that is an evaluation algorithm for V2V communication reliability based on sample judgment and balanced modulation. This study structures a transmission channel model of V2V in IOV, and analyzes the characteristics of multipath transportation of communication channel. By adopting Potter interval balance to design the communication channel balance of V2V in IOV, to balance the self-adapting of sample judgment, according to the interference intensity between the communications transmissions of intersymbol, and to reduce the error code ratio of the communication output. By making the sequence spread spectrum directly at the end of the V2V in IOV, to enlarge communication channel bandwidth, and by combining self-adapting fraction interval modulation methods to estimate the reliability of communication transmission to satisfy the big bit stream data transmission and to reduce the risk of data loss. The simulation result indicates that the above-mentioned reliability estimate method can get an even better accuracy of V2V communication transmission and better effect of communication channel balance. Meanwhile, it can also reduce the error code ratio of the communication and improve the communication quality.
Keywords: IOV (internet of vehicle); V2V (vehicle to vehicle); multipath effect; transmission; reliability estimate; communication channel balance.
Video Encryption Scheme Using Hybrid Encryption Technology
by Qingqing Han, Liejun Wang, Yongming Lee, Jiwei Qin
Abstract: In recent years, researchers have proposed a number of encryption algorithms and encryption schemes to protect the privacy of individuals or businesses. However, these encryption algorithms and encryption schemes are difficult to achieve a certain balance in term of encryption efficiency and security. In order to achieve this goal as much as possible, we propose a hybrid encryption technique, which makes the best use of two encryption techniques to achieve higher security and faster speed of encryption algorithm. We introduce two techniques in this hybrid encryption scheme. The first is scrambling and the second is encryption. We evaluated the performance of encrypted video by comparing the PSNR and SSIM values of the scrambled video and the encrypted video. Experimental results show that the proposed scheme has high encryption efficiency and good security.
Keywords: encryption character; Scramble; Elliptic Curve Encryption(ECC); Advanced Encryption Standard(AES).
Reconstruction and Application of Flooding Routing Algorithm for Smart Street Light over Wireless Sensor Networks
by Zhichao Wang, Liang Tang, Jun Steed Huang, HongWei Shi, Li Bian
Abstract: For smart street light wireless sensor networks, there are huge amount of redundant packets flooding in the route discovery process, resulting in information explosion and high energy consumption. To solve these problems, this paper proposes an improved flooding routing protocol which is suitable for smart street light wireless sensor networks. The protocol can receive signals which include the serial number of copies of the packets, destination address, node types and so on, and it determines the received signal whether need to be forwarded again or not. It forward signal according to a probability which is selected from Golden Ratio (GR), Moon Age (MA), Lunar Month (LM), Euler-Mascheroni constant (EM), EmbreeFrefethen constant (EF), LandauRamanujan constant (LR), Prime Quadruplet (PQ) based on the environment factors such as the season. Thus the protocol can prevent the information explosion effectively and reduce the energy consumption of the entire network, while without lowering the reliability. Through the practical application test analysis, combined with Matlab simulation, we show that the improved algorithm can balance the efficiency versus the reliability, and it has a very broad application prospects.
Keywords: smart lighting; packets flooding; algorithm efficiency; network reliability.
Certificateless Batch Verification Protocol to ensure data integrity in Multi-Cloud using Lattices
by Sasikala Chinthakunta, Shoba Bindu Chigarapalle
Abstract: Cloud storage provides flexible On-demand data storage services to users at anytime and anywhere. However, this new model of data storage introduces a number of new security threats in Cloud Computing environment, because of the loss of physical control over the outsourced data. One of the major challenging issues is data integrity protection. To address this problem several Remote Data Integrity Checking (RDIC) protocols have been proposed, but most of them do not support batch verification, or they rely on Public Key Infrastructure. In this work, we introduce a Certificateless Batch Verification Protocol using Lattices, which supports data integrity checking of multiple data files in a multi-cloud environment. Using, this protocol Third Party Auditor (TPA) can check the data integrity of the multiple data files stored on different cloud service providers at a time. By utilizing, random masking technique and Fibonacci based search approach the proposed protocol not only provides privacy preserving RDIC but also achieves rapid identification of the corrupted data. With this batch verification, we will reduce the computation overhead and communication overhead over the TPA. Through a security analysis we prove the storage correctness and privacy against the TPA of the proposed method. And the experimental results shows that the proposed batch verification decreases computation cost over the TPA approximately 10% compared to the individual verification.
Keywords: Data integrity; Cloud storage; Third Party Auditor; Batch Verification; Cloud computing.
A Low Cost Paging Scheme for Clustered PMIPv6 Protocol by Head-MAG Entity Utilization
by Adnan Jabir
Abstract: Clustered PMIPv6 (CPMIPv6) was proposed to reduce the PMIPv6 signaling cost by dividing the domain into clusters each with a Head-MAG used to control the intra-cluster operations. However, existing mobility protocols perform registration process even for idle nodes, which leads to degrade the protocol performance due to unnecessary location updates. On the other hand, paging technique is widely used in cellular networks to reduce the signaling required for idle nodes registration. Although several paging schemes were proposed to enhance the PMIPv6 performance, they are either based on central entity or they incur high signaling cost due to multicasting. Inspired by CPMIPv6 architecture, this paper proposes a low cost paging scheme where each cluster is considered as a paging area and Head-MAGs entities are utilized to perform the paging functions within the cluster. The analytical results show that the proposed scheme outperforms the existing paging schemes in terms of paging signaling cost.
Keywords: Clustered PMIPv6; Paging; Mobility Management Protocol; Proxy MIPv6.
Hybrid Encryption Model for Managing the Data security in Medical Internet of Things
by Purushotham Jyotheeswari, Jayanthi N
Abstract: Internet of things (IoT) creates communication between the sensor nodes and smart devices through the internet to collect the data. The utilization of IoT in the medical field brings more advantage to the patients and doctors for effective monitoring. The confidentiality of the medical data is a crucial factor need to be taken care of by the medical-IoT (M-IoT). This paper concentrates on developing a secure mechanism for medical data management. In this paper, we develop an architecture for managing the large volumes of medical data generated by the sensor nodes. This architecture will provide secure communication for data sharing among doctors and patients in normal and emergency conditions. As a next step, we propose the security mechanism for improving the data integrity and confidentiality. The proposed mechanism uses symmetric encryption and attribute-based encryption to achieve the performance. The simulation results prove the efficiency of the proposed system.
Keywords: Sensor nodes; data collection; security; confidentiality; Internet of Things.
Optimization 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 optimized 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 quantization 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: Queuing delay and Jitter are much more difficult to evaluate as all other components of end-to-end delay are adequately characterized in Internet. Jitter is particularly important to manage the QoS of real-time applications. Queuing delay and jitter are clearly related to congestion control and occur at the network layer, therefore the queuing delay and jitter are analyzed at the network layer in this paper. Initially, arrival and service processes for multiplexed TCP and UDP datagrams at the congested router output queue are modeled. The model accounts for the fraction of TCP and UDP datagrams (as they contend for resources), the arrival and service distributions of TCP and UDP with their respective datagram sizes. 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 analysis are then compared with NS2 simulation results. The interesting and intriguing result is that, delay related performance of the tagged flow is adversely affected by the fraction of TCP in the background traffic. This is particularly important because TCP dominates the overall traffic. The simulation results fairly agree with the analytical values. The conclusion is true for Cubic, Reno and Compound TCP flavors.
Keywords: Internet; IP; TCP; UDP; Mean Queuing Delay; End-to-End Delay; Jitter.
Optimal Network Selection Algorithm 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 realize 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 multi-network 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 equalization 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 equalization; 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 optimization control system for buildings' indoor environment based on Internet of Things (IoT) is proposed. The system design is divided into two partsthe control algorithm design and the hardware structure design of the control systemto 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 optimize 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.
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.
An Equalization Control Method for Network Big Data Transmission Based on Parallel Computing
by Jiazhong Lu, Xiaosong Zhang, Xiaolei Liu, Teng Hu, Yu Cao
Abstract: In order to solve the problem of high output bit error rate and poor balance of data transmission of network big data, an equalization control method for network big data transmission based on parallel computing is proposed in this paper. In the method, firstly, the information flow in the network channel model is reconstructed through the coherent correlation detection method, and then the channel impulse response of network is calculated. Secondly, according to the channel impulse response, big data output channels are improved and scheduled through the link sequential allocation method. Finally, frequency multiplication processing is performed to the big data output channels through the frequency domain expansion method, and adaptive equalization allocation is performed according to the parallel computing conditions for channel output to achieve channel equalization. A simulation experiment shows that the method proposed in this paper can provide relatively good channel equalization in network bit data transmission and relatively strong adaptive allocation ability of output parallel computing, which reduces communication bit error rate of network.
Keywords: Network; Big data; Parallel computing; Channel; Adaptive allocation; Bit error rate.
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.
Optimal Channel Selection in Parallel Scheduling of Heterogeneous Network Based on Competitive Channel Non-cooperative Game Model
by Yucai Zhou
Abstract: In order to improve the parallel scheduling performance and channel balance of heterogeneous network, an optimal channel selection algorithm for parallel scheduling of heterogeneous network based on competitive channel non-cooperative game model is proposed in this paper. In this algorithm, the channel model of heterogeneous network is constructed, and the impulse response frequency doubling technology is adopted to spread the heterogeneous network channels. The simulation results show that this proposed method can improve equalization and anti-interference ability of channels in optimal channel selection in parallel scheduling of heterogeneous network and it provides the maximum channel transmission bit error rate of 0.35 and channel transmission bit error rate of 0 at signal-to-noise ratio of 50db, which indicates that the bit error rate of the receiving end is small. With this method, the end-to-end time delay in parallel scheduling of heterogeneous network is reduced and the channel impulse response time is about 0.4 ~ 0.6 seconds, which improves the stability and accuracy of parallel scheduling of heterogeneous network.
Keywords: heterogeneous network; parallel scheduling; channel; competitive channel non-cooperative game; spectrum spreading.
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.
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 single character recognition result is checked and corrected on the phrase level. The experimental results show that the hybrid Chinese character recognition 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: Image Processing; OCR; Phrase Processing; K-Nearest Neighbor.
Model analysis of traffic emergency dispatching in intelligent transportation system under cloud computing
by Minglei Song
Abstract: Aiming at the problem of low traffic capacity and low scheduling throughput of traditional intelligent transportation system traffic emergency dispatching model, a traffic emergency dispatching model based on cloud computing for intelligent transportation system is proposed. On the basis of the established cloud computing system, a time-constrained traffic emergency scheduling model of the intelligent transportation system is established. The improved genetic algorithm is used to solve the model and obtain the optimal scheduling scheme. The experimental results show that the traffic capacity of the proposed method varies from 0 to 1.0, which can ensure the smooth traffic of the vehicle. When the traffic congestion is light, the vehicle throughput of the three scheduling models is not significantly different. In the case of congestion and heavy congestion, the throughput of the proposed method is always in the range of 0.4-0.8, and the scheduling performance is the best.
Keywords: cloud computing; intelligent transportation system; vehicle; driving; emergency dispatch.
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.
Low-power Clustering Scheduling Algorithm for Wireless Sensor Nodes in the Internet of Things
by Liangguang Mo
Abstract: In the Internet of Things environment, in order to improve the clustering scheduling and data forwarding capabilities of wireless sensor nodes, 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 optimized deployment design of wireless sensor nodes and design of node transmission route of the Internet of Things; the full-network power consumption equalization 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 optimized 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 equalization 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.
Keywords: Internet of Things; wireless sensor node; low-power consumption; clustering scheduling; energy balance.
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.