International Journal of Internet Protocol Technology (45 papers in press)
A Cloud-Fog Scalable Video Streaming Architecture for Multimedia Internet of Things (IoT) Devices
by Tz-Heng Hsu
Abstract: Cloud computing can be used to provide guaranteed quality of video services. On the other hand, fog computing can lower the transmission costs by using collaborative machine-to-machine and near-user edge communications. In this paper, a cloud-fog scalable video streaming architecture for multimedia Internet of things (Iot) devices is proposed, where fog nodes can collaborate with the rented cloud servers for efficiently delivering the requiring video frames to multimedia Iot devices. The proposed architecture strengthens the stability of the video streaming transmission and improves the quality of service (QoS) of multimedia Iot services with cloud-fog computing techniques.
Keywords: Internet of Things (IoT); Video Streaming; Fog Computing.
The performance and QoE analysis of B2-DASH algorithm
by Tanapat Anusas-amornkul, Surawut Moonsin
Abstract: Dynamic Adaptive Streaming over HTTP (DASH) protocol provides several video bit rates for each video segment depending on the current network conditions and it can be used for several platforms. However, the adaptive algorithm in DASH can be further improved to gain better performance. Therefore, Bandwidth and Buffer-based (B2)-DASH algorithm is proposed to enhance DASH algorithm using both buffer and bandwidth-based approaches to provide high average video bit rate in fluctuated network conditions. In this paper, the B2-DASH is compared with FDASH algorithm, which is the best adaptive algorithm among others. This work compares the performance in three scenarios, i.e. Low Switching Rate, Fast Switching Rate, and Wi-Fi networks. The performance metrics are average video bit rates, number of interruptions, and resolution changes. B2-DASH parameters are analyzed and the recommended parameter settings are presented for the best performance for each scenario. In term of user experiences for video streaming, Quality of Experience (QoE) is analyzed for both B2-DASH and FDASH algorithms.
From the analysis, B2-DASH outperformed FDASH in all scenarios. The number of resolution changes was enhanced about 22% for Fast Switching Rate and Wi-Fi network scenarios. The parameter adjustment process is necessary when the actual bandwidth is highly fluctuated in order to give better performance to B2-DASH algorithm. Quality of Experience (QoE) was calculated for the FDASH and B2-DASH algorithms and B2-DASH algorithm improved the QoE about 13% over FDASH in the Wi-Fi network scenario.
Keywords: B2-DASH; QoE Analysis; Adaptive Video Streaming Algorithm.
Bandwidth Management Framework for Smart Homes Using SDN: ISP Perspective
by Hung-Chin Jang, Jian-Ting Lin
Abstract: With the increasing number of Internet of Things (IoT) devices and the advance of smart home technologies, not only smart environments like smart homes, smart cities and smart countries become feasible, but also all these devices consume more or less bandwidth in data transmission. In this paper, we propose an SDN (Software Defined Networking) based QoS (Quality of Service) aware bandwidth management framework for thousands of IoT enabled smart homes from ISP perspective. The operational scenario of this research assumes that an ISP should support thousands of smart homes. Each smart home equips with tens of IoT devices with a broad spectrum of functional capabilities, and each smart home exploits a variety of services. The overall system architecture consists of into SDN Smart Home Cloud at ISP side, and massive SDN enabled Smart Homes. The Smart Home Cloud interconnects with Smart Homes through OpenFlow protocol. With this architecture, we first prioritized smart home services into categories by adapting 3GPP LTE QoS Class Identifier (QCI). Then we propose a Bandwidth Allocation for Smart Home (BASH) strategy to calculate appropriate bandwidth for each service category according to distinct QoS requirements. ISP can thus use BASH to optimize bandwidth allocation of aggregated and classified services of smart homes. The experiments are conducted under an SDN-based network environment constructed by Linux based OpenvSwitch, Ryu controller, and Mininet. The experiment results show that the proposed framework can effectively enhance QoS and outperform the traditional ISP bandwidth allocation strategy in terms of average transfer ratio, average throughput, average delay and average jitter.
Keywords: Software-Defined Networking (SDN); Quality of Service (QoS); Internet Service Provider (ISP); smart home; bandwidth management.
Bio-inspired Approaches for OFDM Based Cognitive Radio
by Naziha Ali Saoucha, Badr Benmammar
Abstract: Link adaptation algorithms design for OFDM based cognitive radio networks is a challenging task. The main concern is to provide a high Quality of Service for the secondary user while the mutual interference between this last and the primary user persists within a tolerable range. This issue can be formulated as a multiobjective optimization constraint problem. To tackle this optimization problem in a multiobjective constraint framework, in this paper we exploit three of the most recent powerful bio-inspired algorithms: firefly, bat, and cuckoo search. Simulation results revealed that, in contrast to the classical genetic algorithm and particle swarm optimization based link adaptation, our proposed algorithms exhibit better performance in terms of convergence speed and solution quality with saving rates reaching over 98.93% and 46.60%, respectively.
Keywords: cognitive radio; OFDM; QoS; interference; firefly; bat; cuckoo; particle swarm
optimization(PSO); genetic algorithm (GA); binary.
Modeling and Analysis of Real Time and Reliability for WSN based CPS
by Junhua Zhang, Yi Zhu, Fangxiong Xiao
Abstract: WSN based CPS can be used to collect information from remote environment and control it. Delay and dependability directly affect the running quality of WSN based CPS. In this paper, we present a specified process calculus to model the running of the system, including its real time and reliability character. Then we design a group of algorithms to calculate the reliability of the system under certain responsible time constraint. Using these methods, we can formally depict the running of WSN based CPS, and can assess real time and reliability of WSN based CPS quantitatively. We illustrate our ideas through bank night automatic monitoring and emergency system, and we can improve the systems reliability from 0.58 to 0.87 and higher gradually.
Keywords: WSN based CPS; real time; reliability; process calculus.
Construction of Building Fire Information Monitoring Model Based on Adaptive Clustering Scheduling
by Lian Xue, Ying Li
Abstract: In order to solve the problem of unsatisfactory monitoring information transmission and large time overhead during conventional building fire monitoring, an optimization method of building fire information monitoring based on adaptive clustering scheduling is proposed. In this method, a channel model for building fire information monitoring is constructed through the bi-directional link transmission control method, and then node deployment for building fire information monitoring is optimized through the shortest path optimization method. The deployment of the largest coverage of fire information monitoring sensor nodes is designed through the self-adaptive rotation scheduling, and balance control of the output link layer of Internet of Things is performed through the adaptive clustering scheduling method to improve the accurate forwarding and real-time transmission capabilities of Internet of Things for fire detection information, and then a building fire information monitoring model is constructed. Experimental results show that the proposed method can effectively improve the success rate of fire information monitoring packet forwarding with an average increase of 24.7%, which greatly improves the monitoring information transmission efficiency, and it reduces the time overhead of fire information monitoring by 160s. The proposed method meets the actual needs and ensures the effectiveness of fire monitoring.
Keywords: Building; fire information; monitoring model; path optimization; node deploymentrnrn.
Real-time reliability allocation algorithm of high-throughput communication channel under strong interference
by Xian Luo, Rongtao Liao, Guoru Deng, Dangdang Dai, Xiaolan He, Zhixiang Hou
Abstract: In order to improve the anti-interference capability of high-throughput communication and improve the communication quality in performing real-time reliability allocation of highthroughput communication channel in the strong interference environment, a real-time reliability allocation algorithm of high-throughput communication channel under strong interference based on fractionally spaced equalisation and excess mean square error convergence criteria is proposed. In this algorithm, a high-throughput communication channel model is established; optimal symbol interval sampling is carried out to communication signals based on the expansion loss of the channel; the matched filter detection method is adopted for interference suppression of high-throughput communication channel to realise signal spectrum suppression; the fractionally spaced equalisation method is adopted for equalisation scheduling of the channel, and based on the excess mean square error convergence criteria, real-time reliability allocation of highthroughput communication channel under strong interference is realised. Simulation results show that in high-throughput communication channel allocation under strong interference, the method proposed in this paper provides good reliability, which improves real-time channel allocation capability, and channel equalisation, and improves communication quality with relatively low communication output error bit.
Keywords: strong interference; high-throughput communication; channel; equalisation; reliability allocation.
Weighted Moving Average based Differential UWB Indoor Localization System for High External Disturbance Environment
by Qian Gao, Chong Shen, Xiaosi Chen, Kun Zhang
Abstract: Time based Ultra-wideband (UWB) indoor localization system is affected by clock offset, electromagnetic interference, NLOS and other external conditions, the localization accuracy and stability will be greatly reduced. Differential GPS (DGPS) technology introduces a reference station, which transmits a pseudo range correction value to a GPS receiver for error correction, thereby largely reducing the localization error to improve GPS localization accuracy and localization stability. In this paper a Differential UWB localization algorithm combined with weighted moving average is proposed by drawing on the experiences of Differential GPS, focusing on clock offsets and propagation effects. At the same time, the Differential UWB indoor localization system based on TDOA algorithm is tested by Hainan EVK 2.0 experiments. The experiment results show that Differential UWB indoor localization system can effectively improve the localization precision and localization stability, especially in the case of external disturbances, the overall localization error is reduced by 23%.
Keywords: Differential UWB localization algorithm; TDOA algorithm; weighted moving average; external disturbances.
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.
Design a New Protocol and Comparison with B92 Protocol for Quantum Key Distribution
by Manish Kalra, Ramesh C. Poonia
Abstract: Quantum key distribution is the latest advancement in quantum cryptography. There are several QKD protocols like BB84, B92, Ekert91, COW, SARG04, etc. out of which B92 is the second protocol developed in 1992 based on Heisenbergs Uncertainty Principle. In this paper we are discussing first about the related work for the simulation of QKD protocols, second the simulation of B92 and proposed protocol is elaborated and then we compare the performance of B92 with the proposed protocol and proving proposed protocol much better in case of average key length and error rate. Object oriented approach is used in the simulation designing of new protocol and B92 protocol.
Keywords: B92 simulation; QKD protocols; Quantum cryptography; proposed protocol; variation of B92.
Acquiring users requirement and exploring users preference with Word2vec model
by Ji-Wei Qin, Yunpeng Jiang
Abstract: Traditional recommender algorithms mainly use structured data (resource tag, user feature etc.) to depict the user preference and ignore the semantic relations of resources. In this paper, we present a new idea for acquiring users requirement and exploring users preference with Word2vec model (RP- Word2vec) to find the interested and personal resource in the web service. We use Word2vec model to measure the sentiment among keywords and acquire users requirement as accurately as possible; and we treat resources as the input of Word2vec model based on history behaviors and adopt a semantic similarity measuring process to recommend interested and personal resource for the user. The experiments results that the presented RP- Word2vec supports more effective.
Keywords: user’s requirement; user’s preference; Word2vec model.
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.
Preventing Sybil Attacks in Chord and Kademlia Protocols
by Zied Trifa
Abstract: Structured p2p overlay networks, such as Chord, Kademlia, CAN, Pastry and Tapastry allow participant to generate multiple identities on shared physical node. This practice of Sybil attacks introduces the risks of damaging the routing protocol and blocking access to information by impeding queries. Most existing security monitoring mechanisms are inefficient when applied to structured p2p overlay networks. In this work we propose a monitoring strategy allowing the decrease of Sybil nodes rate. We investigate this problem and find ways to detect suspicious behaviors. The key idea of our solution is to use the attack against the attack. We use Sybil attacks to infiltrate and launch monitor peers under different strategies to maximize the likelihood of detection. We were able to infiltrate and monitor in-depth the overlay using a small number of Sybils introduced in strategic zones, which allows us to estimate the number of malicious nodes. The proposed strategy is evaluated against the use of multiple identities both in Chord and Kademlia protocols as most cited and popular p2p overlay networks. We find that adoption of limited number of monitors makes Sybil attacks ineffective.
Keywords: Sybil Attacks; Security; Monitoring; Chord; Kademlia.
High-speed Data Aggregation Storage Query Method
by Yicheng Mu
Abstract: Aiming at the problems of poor aggregation storage capacity and low query efficiency of high-speed data under cloud computing platform, a high-speed data aggregation storage query method based on joint probability density feature extraction and fuzzy C-means clustering under cloud computing platform is proposed. Under the cloud computing platform, the cloud storage data features are decomposed by constructing an overall distributed architecture model of high-speed data storage, the joint probability density features of cloud storage high-speed data are extracted, the data fusion is carried out by using discrete sampling and fuzzy clustering method, the fused data perturbation is detected and filtered by solving a limited set of vectors, and the fuzzy C-means algorithm is introduced for high-speed data feature clustering to achieve adaptive storage query data. The simulation results show that after 60 iterations the recall ratio of the proposed method can reach as high as 100%. When data volume reaches 2000Mbit, the time overhead is only 26.5ms. It indicates that when the proposed method is used to conduct adaptive query of the data under the cloud computing platform, it can effectively realize the classified querying of data with different attribute categories and the precision ratio and query speed are relatively high, having good application value.
Keywords: Cloud computing platform; Data; Fuzzy C-means clustering; Storage; Queryrnrn.
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).
Network Distance Education Platform Control System Based on Big Data
by Jiefeng Wang
Abstract: In this paper, an overall framework of the control system of the network distance education platform is designed. The RTCP control structure Word is used to handle the context-aware control system of the network distance education platform, and the centralized control method is used to design the user interaction experience, the function of each module of the control system is analyzed. The big data integration scheduling model of the network distance education platform is constructed. The experimental results show that the average computational cost of resource scheduling consumed by the proposed method was 32.34ms, which is 12.6% and 23.7% faster than the other two traditional methods. It shows that the designed network distance education platform control system consumes shorter execution time and can provide better output stability, higher accuracy of big data resource scheduling, and good control performance.
Keywords: big data; network distance education platform; control system; stability.
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.