International Journal of Autonomous and Adaptive Communications Systems (38 papers in press)
Optimization Method Of Hierarchical Heterogeneous Clustering Topology Based On Energy Iteration
by Yong Li
Abstract: In order to effectively optimize the balance of energy consumption and prolong the network life, this paper proposes an algorithm based on energy iteration for optimization of hierarchical heterogeneous clustering topology. Combined with Hadoop distributed structure and crawler technology, a network vulnerability detection system is constructed to realize network vulnerability scanning and network structure preliminary detection. In this paper, the idea of energy iteration is introduced to realize the hierarchical heterogeneous clustering routing and network topology optimization. In order to avoid premature failure or death of cluster head nodes, the energy consumption rate of nodes and the distance between nodes and sink nodes are considered to reduce the energy of iteration process. The experiment results show that the energy consumption balance of the network structure optimized by the proposed algorithm is strong and shows good reliability.
Keywords: Energy iteration; Heterogeneous clustering; Topology; Optimization.
The Intrusion Data Mining Method For Distributed Network Based On Fuzzy Kernel Clustering Algorithm
by Linlin LI
Abstract: In order to overcome the shortcomings of the previous intrusion data mining methods for distributed network, which are easy to fall into local extremum and lead to poor mining results, the intrusion data mining method for distributed network based on fuzzy kernel clustering algorithm is studied. By using fuzzy kernel clustering algorithm, a new feature vector is established by using Gauss kernel function which meets Mercer condition, so that the data pattern space in the distributed network can be effectively mapped to the high-dimensional feature space, and the target data is clustered in the high-dimensional space. The experimental results show that the accuracy of data mining for different types and samples is higher than 95%, the false alarm rate and the false alarm rate are lower than 2%, the total mining time is only 446ms, and the real-time mining is better.
Keywords: Fuzzy kernel clustering; Algorithm; Distributed; Network; Intrusion; Data mining.
Hotspot Prediction Of e-Commerce Network Users Based On Improved K-Nearest Neighbor Algorithm
by Gang Qiao
Abstract: Aiming at the problems of low accuracy and poor prediction effect in traditional e-commerce network users' hot spot prediction, this paper proposes an e-commerce network users' hot spot prediction method based on improved k-nearest neighbor algorithm. According to the users hotspot prediction impact indicator, the K-nearest neighbor algorithm is improved in pattern matching process. The key point method is used to remove the noise interference of the original time series. The dynamic time warping algorithm is used to measure the similarity of time series of users hotspot. The distance weight and trend coefficient are introduced according to the difference of users hotspot time series to deduce the future users hotspot and realize hotspot prediction of e-commerce network users. Experimental results show that the method in this paper greatly reduces the deviation of prediction results, which fully shows that the method has better prediction effect.
Keywords: Improved K-nearest neighbor algorithm; E-commerce network; Hotspot prediction.
Resource Monitoring Method Of The Expandable Cloud Platform Based On Micro-Service Architecture
by Dong He, Hongbing Huang, Yiyang Yao, Weiqiang Qi, Hong Li, Dong Mao
Abstract: In order to improve the resource scheduling and self-adaptive forwarding control capability of the expandable cloud platform, it is required to perform resource monitoring optimization design, so a resource monitoring method of the expandable cloud platform based on micro-service architecture is proposed. The micro-service architecture of the expandable cloud platform is constructed, and the optimized storage structure design is carried out on expandable cloud platform resources using the distributed cloud composite storage method. The simulation results show that this method has good information scheduling ability in resource monitoring of the expandable cloud platform, and can provide stable resource output balance. When the signal-to-noise ratio is -2dB, and the bit error rate is 0, which indicates that the method has good resource monitoring and scheduling ability.
Keywords: Micro-service architecture; expandable cloud platform; resource monitoring; scheduling.
Integration Method of Wireless Sensor Network Ciphertext Database Based on Internet of Things
by Xuefeng Ding, Xuehong Liu
Abstract: In order to improve the access and scheduling capability to wireless sensor network ciphertext data, an integration method of wireless sensor network ciphertext database based on Internet of Things was proposed. A distributed detection model for the wireless sensor network ciphertext database was constructed; the Internet of Tings was adopted for optimization deployment design of nodes during integration of the wireless sensor network ciphertext database; the fuzzy clustering method was adopted to realize optimal integration and adaptive scheduling of the wireless sensor network ciphertext database; a hierarchical scheduling and integration model for the wireless sensor network ciphertext database was established to realize optimization design of the database, and improve the access and scheduling efficiency of the database. The simulation results show that in integration of wireless sensor network ciphertext database, this method could provide relatively high fusion degree, good data clustering, and good data precision.
Keywords: Internet of Things; wireless sensor network; ciphertext; database; integration.
Research On The Detection Of Privacy Information Sharing Behavior Of e-Commerce Users Based On Big Data
by Wei Chen, Dongmie Xia, YingJi Li, Xuan Fu
Abstract: In order to solve the problems of behavior data dimensionality reduction and confidence skewness in the detection process of traditional e-commerce users' privacy information sharing, an e-commerce users' information behavior detection method based on big data technology was proposed.Big data technology is used to complete the data storage activities in combination with MYSQL text. According to the storage big database, the storage format is divided, and the big data reduction activities are carried out. The big data reduction and dimensionality reduction operations are used to realize the big data reduction. Based on the low-dimensional data, the density point comparison of shared information is carried out, and the abnormal IP is queried according to the comparison results to realize the detection of data behavior. Experimental results show that the detection method has better effect and higher confidence in reducing the dimension of privacy big data of e-commerce users.
Keywords: Big data; E-commerce users; Privacy information; Sharing behavior; Dimensionality reduction of big data; Information density point.
The Automatic Positioning Method For Defect Data Of 5G Mobile Communication Based On Cloud Computing
by Fang Chen
Abstract: In order to overcome the problems of low positioning accuracy and long running time in the traditional automatic positioning method of communication defect data, this paper proposes a new automatic positioning method for defect data of 5G mobile communication based on cloud computing. The cloud computing method is used to locate the defect data of 5G mobile communication, and the automatic positioning model for defect data of 5G mobile communication is established. At the same time, the target positioning mechanism is transformed into solving the nonlinear least square optimization method. The experimental results show that the proposed method is not only safe and reliable, but also can effectively improve the positioning accuracy. The maximum positioning error is only 0.1%.
Keywords: Cloud computing; 5G mobile communication; Defect data; Automatic positioning.
Research On Digital Forensics Method Of 5G Communication System In The Future Based On Direct Intermediate Frequency Sampling
by Xian Luo, Rongtao Liao, Huanjun Hu, Yuxuan Ye
Abstract: In order to overcome the problems of low efficiency and poor security in the process of digital forensics, a digital forensics method based on direct if sampling for 5G communication system is proposed. In this method, the modulation parameters of the communication signal are obtained by the feature extraction algorithm of the receiver's in pulse signal, and the ontology model of digital forensics is constructed based on the ontology theory: analyze the disk image and judge the file category according to the pattern feature recognition results of the radiation source signal in 5g communication system; determine the hash value and information entropy, and generate the key index; summarize the digital forensics results and generate the digital forensics report. The simulation results show that the data processing efficiency is over 25 / Mbps, and the successful attack rate in the security performance test is 2.89%.
Keywords: Intermediate Frequency Sampling; 5G Communication System; Digital Forensics; Intra Pulse Feature; Mobile Terminal; Encryption Algorithm.
Research On Remote Monitoring Method Of Smart Classroom Based On Internet Of Things
by Dacong Jiang
Abstract: In view of the problems of insufficient security and poor user experience in traditional monitoring methods of smart classroom, a new remote monitoring scheme based on Internet of things technology is proposed. In the hardware part of the paper, the remote monitoring architecture of smart classroom is constructed, the network topology, the Internet of things gateway, the data forwarding network framework and the intelligent environment monitoring function module are designed, and the data collection is realized through the establishment of database entity to provide information data for monitoring and scheduling. In the software design part, the connection program and the running program of the server and the client are designed. The experimental results show that this method has good security, and the user interface function is obviously optimized after the application of the scheme, and the short response time also makes the method have a good application prospect.
Keywords: Internet of things technology; Smart classroom; Remote monitoring; Internet of things gateway;.
Security state monitoring method for perception node in power Internet of things based on low rank model
by Rongtao Liao, Zhihua Xiao, Yixi Wang, Dangdang Dai
Abstract: In order to overcome the problem of low precision and recall in the current power Internet of things security monitoring results, a low rank model based security monitoring method for power Internet of things sensor nodes is proposed. This method constructs the security monitoring platform of power Internet of things sensing node, designs the adaptive sensing mechanism of edge node data types under counting bloom filter, and realizes the adaptive recognition of sensing node data fields. The normal observation data is described according to the low rank part, and the abnormal data is described according to the sparse part. The augmented Lagrangian method is used to optimize the objective equation and realize anomaly detection. The experimental results show that the method has high accuracy and recall, and high reliability.
Keywords: Low rank model; Power Internet of things; Perception node; Security; Monitoring.
An Evaluation Model Of e-Commerce Credit Information Based On Social Big Data
by Yun Zhang
Abstract: In order to overcome the problems of low accuracy and poor stability in the evaluation of Internet trading activities, an evaluation model of e-commerce credit information based on social big data is proposed. The model will be composed of four layers: basic data layer, synthetic data layer, random model layer and integrated learning layer. The logical structure of the model is divided into social communication big data preprocessing, credit evaluation sub model establishment, evaluation sub model integration, so as to enhance the ability of model credit division. On this basis, the credit evaluation index system is established, and the e-commerce credit information is evaluated by BP neural network method. The results of model verification show that the model has good generalization ability and accuracy, can distinguish important variables effectively and stably, can acquire e-commerce credit situation more scientifically, and can control the security situation of e-commerce credit information under the social big data environment.
Keywords: Social big data; E-commerce; Credit; Information; Evaluation; Security situation.
An adaptive prediction model for sparse data forecasting
by Xuan Yao
Abstract: Sparse data generated by the limitation of data acquisition are ubiquitous for prediction. However, the general prediction model is challenging to deal with those sparse data. Therefore, this paper aimed to propose an adaptive sparse data prediction model. Firstly, we introduced the aXreme Gradient Boosting (XGBoost) algorithm to build an adaptive prediction model to correct sparse data constantly. Secondly, the sparsity perception of the XGBoost algorithm is used for parallel tree learning. Finally, we applied the model to the PM2.5 concentration forecasting of Nanjing, China. We trained the model and adjusted the parameters to get better prediction results, and compared the prediction results with actual data to prove the feasibility of the model.
Keywords: Adaptive Prediction model; XGBoost; Sparse data; PM2.5.
Capacity Detection Of Massive Mimo Channel In 5G Environment Based On Symmetric Correlation Matrix
by Bolu Lei, Liya Li
Abstract: The traditional Massive MIMO channel capacity detection method is especially poor in channel equalization, which leads to large channel capacity detection errors and high bit error rate. This paper proposes a new method for Massive MIMO channel capacity detection in 5G environment based on symmetric correlation matrix. Massive MIMO channel model is established and symmetric correlation matrix model is constructed. The Massive MIMO channel capacity under 5G environment can be obtained by obtaining the transmission coefficient correlation between the base station and the rooftop based on the symmetric matrix. In order to verify the effectiveness of the research method, a complete simulation experiment was designed. The experimental results show that the method can effectively detect the capacity of Massive MIMO channel in 5G mobile communication environment in the case of single cluster and double cluster, and the detection accuracy is higher than 99%.
Keywords: 5G mobile communication; Massive MIMO; Channel capacity detection; Channel transmission matrix; Laplace function.
Dynamic Acquisition Method Of Users Implicit Information Demand Based On Association Rule Mining
by Xiang Li
Abstract: In order to overcome the problems of low precision and poor recall in the current research results of user demand mining, a dynamic method based on association rule mining is proposed. Using association rules to get user behavior related data, analyzing user behavior through the crawler system, using different association strategies according to different business, combining with user browsing time, user interest attenuation factor to calculate user interest, build user dynamic interest model. Based on the analysis of user interest, in the initial stage of mining, support and trust are input respectively, and association rule mining algorithm is called to realize the dynamic mining of user implicit information demand. The experimental results show that the mining accuracy and recall rate of this method are higher than 95%, the whole method has strong scalability and practicality.
Keywords: Association rules; Implicit information demand; Dynamic acquisition.
Research on Spectral Clustering Algorithm for Network Communication Big Data Based on Wavelet Analysis
by Xinjian Dai, Zhichao Zeng
Abstract: : In order to classify the features of big data in network communication, improve clustering efficiency and reduce error classification rate, a spectrum clustering algorithm based on wavelet analysis is proposed. The multi-scale, one-dimensional wavelet analysis method is used to sample the network communication big data, extract the spectral feature quantity of the network communication big data, and construct the channel model of big data transmission of network communication. Combined with the fuzzy C-means clustering method, the spectral clustering is performed on network communication big data to mine association rules of the large data spectrum of network communication. Combined with wavelet decomposition method, the time-frequency conversion and feature separation of network communication big data spectrum are carried out to complete the spectrum clustering of network communication big data. Simulation results show that this method is more accurate for spectrum clustering of communication big data and improves clustering efficiency
Keywords: wavelet analysis; network communication; big data; spectral clustering; feature extraction.
Layered Routing Algorithm For Wireless Sensor Networks Based On Energy Balance
by Danxia Luo, Changan Ren
Abstract: Aiming at the shortcomings of traditional LEACH Routing Protocol in wireless sensor network data transmission applications, such as high total energy consumption, low residual energy and short network life, a hierarchical routing algorithm based on energy balance is proposed. This algorithm is based on the energy consumption model of wireless sensor network communication, and adopts the non-uniform clustering algorithm to introduce the threshold. According to the relationship between the distance between cluster head node and sink node and the threshold, the implementation method of network communication is selected. In addition, a simple correlation multi-path route is designed to realize the multi hop communication between clusters. By considering the communication cost and the residual energy value of nodes, the hierarchical route with balanced energy consumption is realized. Experimental results show that the algorithm has obvious advantages in balancing network energy consumption and prolonging network lifetime.
Keywords: Library and Information Management; Coding Information; Automatic Extraction.
Personalized Recommendation Algorithm For e-Commerce Network Information Based On Two-Dimensional Correlation
by Enwei Cao
Abstract: In view of the poor accuracy and low efficiency of the traditional e-commerce personalized recommendation algorithm, a two-dimensional correlation based personalized recommendation algorithm for e-commerce network information was proposed. Using two-dimensional correlation, categorize e-commerce user relevancy analysis to measure the personality interests of users in the electronic commerce network, e-commerce project through the Jaccard similarity coefficient, the similarity calculation between the interest spread model was constructed, differentiate the importance of data push grades, numerical characteristics of e-commerce behavior is influenced by the importance level is calculated, using the sorting result to realize e-commerce personalized recommendation. The experimental results show that the proposed method has high accuracy, diversity and efficiency.
Keywords: Two-dimensional correlation; E-commerce network; Personalized recommendation of information; Interest dissemination.
Research On Coverage Holes Repair In Wireless Sensor Networks Based On Improved Artificial Fish Swarm Algorithm
by Dongliang Li
Abstract: In WSN, holes are formed when nodes become invalid. To resolve this problem, Holes Repairing Algorithm based on Fish Swarm Optimization in Wireless Sensor Network (HRFSO) is proposed in this paper. In the algorithm, network coverage is served as the objective function, and biological behaviors of artificial fish are used to simulate nodes movements. The new actions of jumping and survival of the fittest are defined besides foraging, rear-ending and grouping to improve the convergence of optimization. Self-adaptive vision and step length are used when updating the status of artificial fish. Failed holes are repaired by moving sensor node with the shortest distance. The simulation results show that the algorithm is suitable for repairing holes with fast speed by moving fewer nodes. It can increase WSN coverage with better repairing result, faster convergence, higher accuracy, efficiency, and robustness. The results also show lifetime of the network can be prolonged.
Keywords: Network Coverage; Artificial Fish Swarm Algorithm (AFSA); Wireless Sensor Network; Hybrid network; Robustness.
A Hole Repair Algorithm For Wireless Sensor Networks Based On Virtual Attractive Force Constraint
by Ting Hu
Abstract: There are some problems in the traditional algorithm, such as long running time and poor coverage effect. In this paper, a new algorithm based on virtual attractive force constraint is proposed. Based on the virtual attractive force model of intensity-based virtual force algorithm with boundary forces (IVFA-B), aiming at the particularity of ideal distance between heterogeneous network nodes, this paper analyzes the relationship between the perception radius of two heterogeneous nodes and the optimal distance between nodes when realizing the maximum coverage in grid. By combining the best distance and the best distance threshold of virtual force algorithm, the adaptability of heterogeneous network is provided. At the same time, the node moving probability is introduced into the nodes moving distance formula to repair the hole in wireless sensor network node. The simulation results show that the proposed algorithm can achieve better coverage effect and reduce the running time effectively, which proves that the proposed algorithm has better application performance.
Keywords: Virtual attractive force constraint; Wireless sensor; Hole repair of network node.
Vulnerability Detection Of The Authentication Protocol In The Iot Based On Improved Wavelet Packet
by Shihong Chen
Abstract: In order to overcome the problems of long detection time and large detection error in traditional vulnerability detection methods for the authentication protocol in the IOT, this paper proposes a new method based on improved wavelet packet for vulnerability detection of the authentication protocol in the IOT. This method uses the improved wavelet packet to preprocess the data packet and form a small amount of original data. Combined with the method of protocol state diagram, it improves the coverage of traversal path and the effectiveness of trial cases. At the same time, it uses the method of sending TCP detection packets to detect whether there is vulnerability in the IOT authentication protocol. The experimental results show that the proposed method can effectively reduce the detection time and improve the detection accuracy, with the highest detection accuracy of 98.2%.
Keywords: Improved wavelet packet; IOT; Authentication protocol; Vulnerability detection; Traversal path.
Encryption Of Ciphertext Data In Internet Of Things Based On Hecrt Key Management
by Ying Li
Abstract: The current ciphertext data encryption system of the Internet of Things has some problems, such as long execution time and small probability of query-shared key. This paper proposes a new ciphertext data encryption method based on HECRT key management. CPU is used to calculate the password in the special engine, which can encrypt the relationship between the engine and other modules in ciphertext data encryption system. HECRT key management is used to construct the network model of the Internet of Things, and the Chinese residual equation is used to realize the encryption method of ciphertext data of the Internet of Things. The experimental results show that the throughput of the proposed method is more consistent with the SQL Server database, the execution time is lower than 0.35s, and the probability of discovering Shared keys is higher than 80%, which can prove that the proposed system has strong usability and encryption capability.
Keywords: Encryption system; HECRT; Key management; ciphertext data in Internet of Things.
State Scheduling Method Of Redundant Nodes In Power Communication Network Based On Least Square Method
by Liang Ma, Jie Zhou, Bintai Xu, Youxiang Zhu, Yingjie Jiang
Abstract: In order to overcome the problem of large energy consumption in traditional scheduling methods, a state scheduling method based on least square method is proposed for redundant nodes in power communication network. This method can identify and mark redundant nodes and obtain the location information of adjacent nodes in power system environment. Using the least square method and iterative method to find the location coordinates of redundant nodes in the power communication network, building the basic power communication network model, according to the work requirements of redundant nodes in the power communication network, to achieve the scheduling of redundant nodes. The experimental results show that the average energy consumption is 0.16kj less than that of the traditional method, which has better performance of coverage quality in the monitoring process and can extend the network monitoring time in the later stage of operation.
Keywords: Power communication; Communication network; Redundant node; Node state; State scheduling.
Optimum Design of Distance Education Assistant System based on Wireless Network
by Zixiang Yan
Abstract: Due to the constraints of various environments, the existing distance education assistant system can not meet the needs of the present stage. Aiming at the above problems, a new distance education assistant system based on wireless network is designed. Firstly, the function of the hardware part of the distance education assistant system is designed, the functions of several subsystems are introduced, and the business process of the hardware part of the system is analyzed. Combining the video and audio signal coding technology in the software design, the characteristics of the editing code are analyzed, and the software part of the system is optimized by using MMX technology. The simulation results show that the proposed system effectively reduces the response time of the system, improves the stability of the system, lays a solid foundation for the stable operation of the system, and realizes the optimization of the distance education assistant system.
Keywords: Wireless network; Distance education; Assistant system; Optimization.
Collaborative Variational Factorization Machine For Recommender System
by Jiwei Qin, HongLin Dai
Abstract: At present, the recommendation systems are confronting the huge challenge of data sparsity and high complexity of algorithm. Like the traditional collaborative filtering recommendation methods, they are difficult to adapt to the data sparse environment, resulting in low prediction accuracy. To address the aforementioned issues, this paper presents a novel Factorization Machine based on Collaborative filtering framework called Collaborative variational Factorization Machine (CVFM) that considers the user-user relations with the interaction data for Recommender systems. First, the user-item explicit ratings are used to build the user-user relationship by the similarity calculation. Next, we develop a variational Factorization Machine with a linear process to exploit the inherent relationship of latent variables from interaction information. The experimental results on three different datasets show that the presented CVFM is superior to other popular methods in prediction accuracy, at the same time, maintain the stability of our algorithm with dealing with sparse data.
Keywords: Service recommendation; factorization machine; collaborative filtering; Service calculation.
Integrated Radar Radio: Enabling technology for Smart Vehicle of Smart Cities
by MITHUN CHAKRABORTY, Debdatta Kandar, Bansibadan Maji
Abstract: The growing technological development in the field of information and communication technology has evolved the futuristic concept of smart cities, wherein the objects, embedded with high speed processors and memory, would be intelligent in the sense that they are capable to communicate among each other and can take decision. The smart cities will ensure increased road safety, traffic mobility, sustain environment and economic development. To ensure these features smart vehicle becomes an integral component of the smart cities. The smart vehicles should be equipped with simultaneous intelligent sensing and communication technologies at the back end to enable for increased road safety, traffic mobility etc. This requires the joint operation of radar and communication without interference. The aim of the paper is to develop an integrated radar radio platform without interference between the radar and the radio, facilitating smart vehicles. The concept substantiated here for integrated radar radio
Keywords: OFDM; radar radio; UWB; IV; IVC; V 2 V; V 2 I; FMCW; ICI; mmW.
Network Dynamic Routing And Spectrum Allocation Algorithm Based On Blockchain Technology
by Jue Ma
Abstract: To overcome the problems of low resource utilization rate and high bandwidth blocking rate of traditional network dynamic routing and spectrum allocation, a network dynamic routing and spectrum allocation algorithm based on blockchain technology is proposed. In this algorithm, a hybrid integer linear model of network dynamic routing and spectrum allocation is constructed to minimize spectrum consumption and frequency. Based on the extended static heuristic algorithm of blockchain, the link with the largest load is selected to optimize the spectrum allocation, and the linear model and extended static heuristic algorithm are combined to update the frequency gap state of the link where the path is located, so as to achieve the purpose of dynamic routing and spectrum allocation of the network. The experimental results show that the spectrum utilization rate is as high as 99.66%, and the bandwidth blocking rate is as low as 0.
Keywords: Blockchain technology; Network dynamic routing; Spectrum allocation; Bandwidth blocking.
A Multi Agent based Energy and Fault Aware Scheme for WSN of Hard-to-reach Territories
by RAJENDRA KUMAR DWIVEDI, Rakesh Kumar, Rajkumar Buyya
Abstract: There are various applications of sensor networks in hard-to-reach territories. Sensor has limited energy. Therefore, several energy efficient methodologies have been devised to minimize the energy consumption. The manuscript discovers and resolves limitations of the existing multiple agent schemes which include energy consumption, fault handling, itinerary planning and positioning of sink. Existing protocols placed sink at centre of sensing area. This job is very difficult in hard-to-reach territories. So, a multi agent based energy and fault aware scheme for hard-to-reach territories (MAHT) has been devised. It provides a method of Accumulated Impact Factor (AIF) for identifying the central node with high energy. Its novel agent migration scheme improves the energy efficiency. Planning the itinerary dynamically results in fault tolerance. Proposed protocol is simulated with Castalia simulator and its performance is evaluated on various metrics demonstrating that MAHT outperforms the existing schemes.
Keywords: Fault Tolerance; Energy Efficiency; Wireless Sensor Network (WSN); Accumulated Impact Factor; Mobile Agents.
Security Key Distribution Method of Wireless Sensor Network Based on DV-Hop Algorithm
by Fei Gao
Abstract: In order to overcome the problems of low security connectivity and poor distribution accuracy of traditional key distribution methods for network security, this paper proposes a security key distribution method for wireless sensor networks based on DV-Hop algorithm. In this method, the improved DV-Hop algorithm is used to locate the network security key distribution points, and the distributable points are separated according to the location results. According to the separation results, the key management tree is introduced to manage the distributable points in a centralized way, and the key management tree is used to complete the authentication, key distribution and update of wireless sensor network equipment. The experimental results show that the energy consumption of key establishment and update is low, and the minimum energy consumption of key update is only 25 ? J, which has strong anti-attack performance and high overall security.
Keywords: DV-Hop; Wireless sensor network; Key management tree; Key distribution.
Detection Of Malicious Rank Attack Nodes In Communication Network Based On Windowed Frequency Shift Algorithm
by Hao Yang, Yibo Xia, Wen Cai, Xin Xie
Abstract: In order to overcome the problem of low detection efficiency and accuracy in the existing detection methods of malicious nodes in communication networks, a detection method of malicious Rank attack nodes in communication networks based on windowed frequency shift algorithm is proposed. The original signal samples are collected, and the windowed signal spectrum is obtained by windowed truncation and DTFT processing. The frequency shift of signal is calculate, the direction of frequency shift is judged, the amplitude and frequency parameters of sampling signal are calculated, and the abnormal detection of communication network signal is realized according to the calculation results of parameters. The experimental results show that compared with the traditional methods, the proposed method has higher detection efficiency and accuracy, the highest detection rate can reach more than 98%, which can effectively protect the security of the communication network.
Keywords: Windowed frequency shift; Communication network; Malicious Rank attack; Node detection.
Performance of RPL under various mobility models in IoT
by Spoorthi Shetty
Abstract: The Internet of Things is a system used primarily for communication where various devices are connected for the collection, analysis and execution of the task required The main challenge in IoT device is, they are resource-constrained Hence, usage of an effective data transmission routing protocol plays an vital role in IoT It is identified from the research that, IPv6 Routing Protocol for Low Power and Lossy Networks (RPL)is an effective routing protocol for static IoT network Along with static network, it is necessary to evaluate the effectiveness of the RPL for different mobility models The energy consumption of the Reference Point Mobility Model (RPGM) is compared in this document with the Column Mobility Model (CMM) for RPL at distinct concentrations of salability using Cooja simulator with Contiki operating system By the extensive experimental analysis, it is identified that the CMM is more energy efficient than the model of RPGM model.
Keywords: Reference Point Group Mobility Model; Column Mobility model; Internet of Things; Routing Protocol for Low power and Lossy networks.
PREDICTION OF BIRD SPECIES USING RANDOM FOREST ALGORITHM-INTERNET OF BIRDS
by VIMAL SHANMUGANATHAN, Kaliappan M, Vijayalakshmi K, Muthulakshmi S, Selva Ishwarya
Abstract: In our routine life, we tend to stumble upon several birds. Bird-watching may be an in-style hobby that offers relaxation in way of life. Infinite individuals look at the class of various bird species while visiting bird sanctuaries., to make the bird watchers easy tool for developed where we can assist birders to acknowledge 60 bird species however we tend to can not ready to acknowledge the kind of that bird species. To beat this downside we tend to stumble upon an answer of building a package as a project. From DCNN formula may be foreseen at 88. We can notice additional correct and stable prediction of the image exploitation random formula in Jupyter notebook.
Keywords: image recognition; random forest algorithm; internet of birds; deep learning; DCNN.
Research On Parallel Association Rules Mining Of Big Data Based On Improved K-Means Clustering Algorithm
by Li Hao, Tuanbu Wang, Chaoping Guo
Abstract: In order to overcome the problems of time-consuming, low precision and redundant rules in association rules mining of big data, a parallel association rule mining method based on improved K-means clustering algorithm is proposed. This paper introduces the matter-element theory of extension, combines matter-element theory and database, and constructs the matter-element relation database model of extension, to realize the mining of parallel association rules of big data on the basis of extension. Redundant algorithm and equivalent transformation are used to eliminate redundant association rules. The experimental results show that the proposed method has high mining efficiency, high mining accuracy and high rule association, which proves that the proposed method has better application performance.
Keywords: K-means clustering algorithm; Association rules; Data mining; Redundancy algorithm; Equivalence transformation.
Dynamic Key Distribution Method For Wireless Sensor Networks Based On Exponential Algorithm
by Yun ZHAO, Yong XIAO, Weibin LIN, Chao CUI, Di XU
Abstract: In order to overcome the problem of low robustness of key distribution of wireless sensor networks, a dynamic key distribution method for wireless sensor networks based on exponential algorithm is proposed in this paper. In this method, the collusion characteristics of newly added and cancelled nodes in wireless sensor networks are used to establish the wireless sensors security model. Based on the wireless sensors security model, the exponential algorithm is used to achieve dynamic key distribution through the five indicators of initialization, session key self-repair, session key mutual repair, joining node and withdrawing node. The experimental results show that when the number of dynamic key nodes is 600, the probability of communication failure is 47%; when the number of hops is 10, the energy cost is only 1.64mJ, and the network robustness is high.
Keywords: Exponential algorithm; Wireless sensor; Network; Dynamic key; Distribution; Method.
Heuristic Positioning Method Of Intrusion Nodes In Sensor Networks Based On Quantum Annealing Algorithm
by Yun ZHAO, Ziwen CAI, Tao HUANG, Bin QIAN, Mi ZHOU
Abstract: In order to overcome the problems of low positioning accuracy and long time-consuming in traditional heuristic positioning methods, a new heuristic positioning method of intrusion nodes in sensor network based on quantum annealing algorithm is proposed. This method analyzes the result graph of sensor network and node system, selects the multi-communication radius method to communicate and broadcast among each sensor node, at the same time, refines the number of hops of nodes, and selects the weighting factor to calculate the average hopping moment of unknown nodes. On the basis of the above, through quantum tunneling effect, combined with quantum annealing algorithm, the heuristic positioning of intrusion nodes in sensor network is completed. The simulation results show that the proposed method can effectively improve the positioning accuracy and reduce the running time. The maximum positioning time is only 0.2min.
Keywords: Quantum annealing algorithm; Sensor network; Intrusion node; Heuristic positioning.
Research on Abnormal Data Recognition Method of Optical Network Based on WIFI Triangular Location
by Bingchen Lin
Abstract: In order to overcome the problems of low recognition accuracy and poor reliability of traditional optical network abnormal data identification methods, a new optical network abnormal data recognition method based on WiFi triangulation positioning is proposed. Time series analysis method is used to analyze the channel model of optical network to obtain the temporal characteristics of abnormal data in optical network. Hyperbolic frequency modulation decomposition method is used to detect the time domain characteristics of abnormal data, and the total energy of abnormal data in time and frequency domain is obtained. The abnormal data signal model is established by the energy density characteristics of abnormal data, and the specific position of abnormal data in the abnormal data signal model after filtering is identified by using WiFi triangle positioning algorithm. The experimental results show that the accuracy of the method is higher than 95%, and the recognition performance is good.
Keywords: WiFi; triangulation; channel model; total time-frequency energy; energy density characteristics.
The Traffic Jam Management and Prediction using IoT & Deep Learning technique for Smart City Infrastructure
by Shubham Gupta, Shreyansh Dixit, Rohit Sharma
Abstract: In todays era, the roadways connect two cities and even countries. In the rapidly shifting world, we need to get our work done in a split of second but, due to the on growing population especially country like India has a plethora of vehicles and commuters who travel by road, these can be of different varieties, from four-wheeler to a two-wheeler. But, due to the huge population of vehicles around us, the traffic density on roads is increasing and this leads to traffic jams which are very frequent and can extend up to even hours to get into a smooth flow again. Thus, we require some specialized techniques to solve this issue which we have shown in our paper using YOLOv3 and some statistical-based analysis using images and video files for traffic prediction. This will help in ensuring traffic management by alerting the authorities on time to take necessary actions.
Keywords: IoT; Deep Learning; Artificial Intelligence; Big Data Analytics; Security; Smart City; Visualization; Traffic Jam.
Response Efficiency Optimization of Data Cube Online Analysis for Network user's behavior
by Hui Zhang, Su Zhang, Xiaoling Jiang
Abstract: Data cube plays an important role in online analysis and processing of multi-dimensional data warehouse. Aiming at the problem of long response time and poor compression performance of data query in current methods, the optimization performance of the method is reduced, and a response efficiency optimization method for online analysis data cube based on formal concept lattice is proposed. Firstly, the data of network user's behavior is analyzed and combined with the access frequency of network user. Secondly, the time-varying and stability of data warehouse are analyzed in detail. Finally, slicing and dicing operations in online analysis are analyzed. The experimental results show that the proposed method has a shorter query response time and can quickly retrieve data encoding with better compression performance when the number of fact tables and dimension tables is increasing.
Keywords: Network user's behavior; Data cube; Online analysis; Response efficiency optimization.
Social Media Based Deep Auto-Encoder Model for Clinical Recommendation
by Kretika Tiwari, Dileep Singh
Abstract: In recent years, systems that use deep learning and patient clinical information for drug and ADR recommendations have become one of the research hotspots in the medical community However, it is still a mind hunting task for the clinical communitys to establish a model that combines the recommendation system as a hybrid This paper proposed a hybrid model that expands deep self-decoder and Top-k co-patient information by constructing a joint optimisation function, as SAeCR For extracting implicit clinical semantic information, the network representation learning method is used To evaluate the SAeCR models performance, three experiments have been carried out on two real social network data sets The experimental results show that the proposed model performs better than the other classification technique in a more sparse and more extensive data set Furthermore, the social network information can better identify the clinical relationship between co-patient.
Keywords: Adverse Drug Reaction · Collaborative filtering · Deep Learning · Drug recommendation · Clinical Recommendation System · Recommendation System · Social Media.