The Performance Analysis for Distributed Collaborative Beamforming Transmission of Clustered Mobile Sensor Network

Sensor nodes cannot directly communicate with the distant unmanned aerial vehicle( UAV) for their low transmission power. Distributed collaborative beamforming from sensor nodes within a cluster is proposed to provide high speed data transmission to the distant UAV. The bit error ratio( BER) closed-form expression of distributed collaborative beamforming transmission with mobile sensor nodes has been derived. Furthermore,based on the theoretical BER analysis and the numerical results,we have analyzed the impacts of nodes 'mobility,number of sensor nodes,transmission power and the elevation angle of UAV on the BER performance of collaborative beamforming. And we come to the following conclusions: the mobility of sensor nodes largely decreases the BER performance; when the position deviation radius is large,incensement in power cannot improve BER anymore; the size of cluster should be bigger than 10 for the purpose of achieving good BER performance in Rayleigh fading channel.

Effects of internal noise on the spiking regularity of a clustered Hodgkin–Huxley neuronal network

Spiking regularity in a clustered Hodgkin–Huxley（HH） neuronal network has been studied in this letter. A stochastic HH neuronal model with channel blocks has been applied as local neuronal model. Effects of the internal channel noise on the spiking regularity are discussed by changing the membrane patch size. We find that when there is no channel blocks in potassium channels, there exist some intermediate membrane patch sizes at which the spiking regularity could reach to a higher level. Spiking regularity increases with the membrane patch size when sodium channels are not blocked. Namely, depending on different channel blocking states, internal channel noise tuned by membrane patch size could have different influence on the spiking regularity of neuronal networks.

AN ADAPTIVE-WEIGHTED TWO-DIMENSIONAL DATA AGGREGATION ALGORITHM FOR CLUSTERED WIRELESS SENSOR NETWORKS

In this paper,an Adaptive-Weighted Time-Dimensional and Space-Dimensional(AWTDSD) data aggregation algorithm for a clustered sensor network is proposed for prolonging the lifetime of the network as well as improving the accuracy of the data gathered in the network.AWTDSD contains three phases:(1) the time-dimensional aggregation phase for eliminating the data redundancy;(2) the adaptive-weighted aggregation phase for further aggregating the data as well as improving the accuracy of the aggregated data; and(3) the space-dimensional aggregation phase for reducing the size and the amount of the data transmission to the base station.AWTDSD utilizes the correlations between the sensed data for reducing the data transmission and increasing the data accuracy as well.Experimental result shows that AWTDSD can not only save almost a half of the total energy consumption but also greatly increase the accuracy of the data monitored by the sensors in the clustered network.

Blockchain-Based Data Acquisition with Privacy Protection in UAV Cluster Network

The unmanned aerial vehicle(UAV)self-organizing network is composed of multiple UAVs with autonomous capabilities according to a certain structure and scale,which can quickly and accurately complete complex tasks such as path planning,situational awareness,and information transmission.Due to the openness of the network,the UAV cluster is more vulnerable to passive eavesdropping,active interference,and other attacks,which makes the system face serious security threats.This paper proposes a Blockchain-Based Data Acquisition(BDA)scheme with privacy protection to address the data privacy and identity authentication problems in the UAV-assisted data acquisition scenario.Each UAV cluster has an aggregate unmanned aerial vehicle(AGV)that can batch-verify the acquisition reports within its administrative domain.After successful verification,AGV adds its signcrypted ciphertext to the aggregation and uploads it to the blockchain for storage.There are two chains in the blockchain that store the public key information of registered entities and the aggregated reports,respectively.The security analysis shows that theBDAconstruction can protect the privacy and authenticity of acquisition data,and effectively resist a malicious key generation center and the public-key substitution attack.It also provides unforgeability to acquisition reports under the Elliptic Curve Discrete Logarithm Problem(ECDLP)assumption.The performance analysis demonstrates that compared with other schemes,the proposed BDA construction has lower computational complexity and is more suitable for the UAV cluster network with limited computing power and storage capacity.

DNA Sequence Classification Based on the Side Chain Radical Polarity of Amino Acids

The features of DNA sequence fragments were extracted from the distribution density of the condons in the individual cases of DNA sequence fragments. Based on the polarity of side chain radicals of amino acids molecules, the amino acids were classified into five categories, and the frequencies of these five categories were calculated. This kind of feature extraction based on the biological meanings not only took the content of basic groups into consideration, but also considered the marshal ing sequence of the basic groups. The hierarchical clustering analysis and BP neural network were used to classify the DNA sequence fragments. The results showed that the classification results of these two kinds of algo-rithms not only had high accuracy, but also had high consistence, indicating that this kind of feature extraction was superior over the traditional feature extraction which only took the features of basic groups into consideration.

Relationships and Evolving Networks of Rural Manufacturing Clusters: A Case Study in Yucheng County, Henan Province of China

The evolution of networks in rural industrial clusters,in particular in the context of China has been paid more attention to in the world.Applying the theory and techniques of social network analysis (SNA),this study is with particular regard to the business network relationships and their evolutionary dynamics of steel measuring tape manufacturing clustered in Nanzhuang Village,Yucheng County of Henan Province,China,which is important for better understanding the industrial and regional development in less developed rural areas.From data collected by comprehensive questionnaire survey in 2002 and mass interviews with 60 enterprises and assembling families and several government authorities in 2002,2003,2004,2005 and 2008,four types of networks are identified: spin-off,consulting,communication and cooperative.The characteristic of these networks is outlined in detail.Compared with the high-tech clusters of typical developed areas,the networks that have evolved in traditional manufacturing clusters are more affected by emotive linkages.The cluster networks are shown to exhibit a polycentric hierarchical structure.The family relationships are the dominate spin-off channels of enterprises,while the supply and demand relationships and the mobility of the skilled workers are also important paths of network learning,and the cooperation relationships are comparatively stable.Besides the root enterprises,the middle-sized enterprises are comparatively more active than small-sized enterprises,and the intermediary agencies and the service institutions act as bridges of the inter-enterprises cooperation.By analysis of the structure of networks and the interactions between the networks,the four stages of network evolution are also identified.The four stages are dominated by the family networks,the internal division production networks,the local innovation networks and the global supply networks respectively,and they play different roles in cluster development.

Image segmentation algorithm based on high-dimension fuzzy character and restrained clustering network

An image segmentation algorithm of the restrained fuzzy Kohonen clustering network （RFKCN） based on high- dimension fuzzy character is proposed. The algorithm includes two steps. The first step is the fuzzification of pixels in which two redundant images are built by fuzzy mean value and fuzzy median value. The second step is to construct a three-dimensional （3-D） feature vector of redundant images and their original images and cluster the feature vector through RFKCN, to realize image seg- mentation. The proposed algorithm fully takes into account not only gray distribution information of pixels, but also relevant information and fuzzy information among neighboring pixels in constructing 3- D character space. Based on the combination of competitiveness, redundancy and complementary of the information, the proposed algorithm improves the accuracy of clustering. Theoretical anal- yses and experimental results demonstrate that the proposed algorithm has a good segmentation performance.

Storage Assignment Optimization in a Multi-tier Shuttle Warehousing System

The current mathematical models for the storage assignment problem are generally established based on the traveling salesman problem（TSP）,which has been widely applied in the conventional automated storage and retrieval system（AS/RS）.However,the previous mathematical models in conventional AS/RS do not match multi-tier shuttle warehousing systems（MSWS） because the characteristics of parallel retrieval in multiple tiers and progressive vertical movement destroy the foundation of TSP.In this study,a two-stage open queuing network model in which shuttles and a lift are regarded as servers at different stages is proposed to analyze system performance in the terms of shuttle waiting period（SWP） and lift idle period（LIP） during transaction cycle time.A mean arrival time difference matrix for pairwise stock keeping units（SKUs） is presented to determine the mean waiting time and queue length to optimize the storage assignment problem on the basis of SKU correlation.The decomposition method is applied to analyze the interactions among outbound task time,SWP,and LIP.The ant colony clustering algorithm is designed to determine storage partitions using clustering items.In addition,goods are assigned for storage according to the rearranging permutation and the combination of storage partitions in a 2D plane.This combination is derived based on the analysis results of the queuing network model and on three basic principles.The storage assignment method and its entire optimization algorithm method as applied in a MSWS are verified through a practical engineering project conducted in the tobacco industry.The applying results show that the total SWP and LIP can be reduced effectively to improve the utilization rates of all devices and to increase the throughput of the distribution center.

HTTP-sCAN:Detecting HTTP-Flooding Attack by Modeling Multi-Features of Web Browsing Behavior from Noisy Web-Logs

HTTP-flooding attack disables the victimized web server by sending a large number of HTTP Get requests.Recent research tends to detect HTTP-flooding with the anomaly-based approaches,which detect the HTTP-flooding by modeling the behavior of normal web surfers.However,most of the existing anomaly-based detection approaches usually cannot filter the web-crawling traces from unknown searching bots mixed in normal web browsing logs.These web-crawling traces can bias the base-line profile of anomaly-based schemes in their training phase,and further degrade their detection performance.This paper proposes a novel web-crawling tracestolerated method to build baseline profile,and designs a new anomaly-based HTTP-flooding detection scheme(abbr.HTTP-sCAN).The simulation results show that HTTP-sCAN is immune to the interferences of unknown webcrawling traces,and can detect all HTTPflooding attacks.

A Coverage-Aware Unequal Clustering Protocol with Load Separation for Ambient Assisted Living Based on Wireless Sensor Networks

Ambient Assisted Living(AAL) is becoming an important research field. Many technologies have emerged related with pervasive computing vision, which can give support for AAL. One of the most reliable approaches is based on wireless sensor networks(WSNs). In this paper, we propose a coverage-aware unequal clustering protocol with load separation(CUCPLS) for data gathering of AAL applications based on WSNs. Firstly, the coverage overlap factor for nodes is introduced that accounts for the degree of target nodes covered. In addition, to balance the intra-cluster and inter-cluster energy consumptions, different competition radiuses of CHs are computed theoretically in different rings, and smaller clusters are formed near the sink. Moreover, two CHs are selected in each cluster for load separation to alleviate the substantial energy consumption difference between a single CH and its member nodes. Furthermore, a backoff waiting time is adopted during the selection of the two CHs to reduce the number of control messages employed. Simulation results demonstrate that the CUCPLS not only can achieve better coverage performance, but also balance the energy consumption of a network and prolong network lifetime.

Optimal Energy Efficiency Resource Allocation Strategy for Cognitive Clustering Network under PUEA Attack

5G has pushed the use of radio spectrum to a new level,and cognitive clustering network can effectively improve the utilization of radio spectrum,which is a feasible way to solve the growing demand for wireless communications.However,cognitive clustering network is vulnerable to PUEA attack,which will lead to the degradation of system detection performance,thereby reducing the energy efficiency.Aiming at these problems,this paper investigates the optimal energy efficiency resource allocation scheme for cognitive clustering network under PUEA attack.A cooperative user selection algorithm based on selection factor is proposed to effectively resist PUEA user attack and improve detection performance.We construct the energy efficiency optimization problem under multi-constraint conditions and transform the nonlinear programming problem into parametric programming problem,which is solved by Lagrangian function and Karush-Kuhn-Tucker condition.Then the sub-gradient iterative algorithm based on optimal energy efficiency under PUEA attack is proposed and its complexity is analyzed.Simulation results indicate that proposed method is effective when subjected to PUEA attacks,and the impact of different parameters on energy efficiency is analyzed.

A Power Efficient Aggregation of Encrypted Data in Wireless Sensor Network

In-network data aggregation in wireless sensor network has been shown to improve scalability, prolong sensor network lifetimes and diminish computational demands. However, the node that plays the role of data aggregation will consume much more energy than common nodes and may quit the mission in advance due to energy exhausting because of taxing decryption and re-encryption operation; moreover, it will bring complex key management to ensure the security of the data and corresponding keys. This paper was designed specifically to address above problem based on the thought of privacy homomorphism, It can achieve the perfect security level equal to one-time pad with much lower energy consumption; moreover, it can be proved to resist the attack of node capture. Using the simulation and analysis, we show that our scheme consume the energy only about 21% of AED scheme.

Network Traffic Clustering with QoS-Awareness

Network traffic classification is essential in supporting network measurement and management.Many existing traffic classification approaches provide application-level results regardless of the network quality of service(QoS)requirements.In practice,traffic flows from the same application may have irregular network behaviors that should be identified to various QoS classes for best network resource management.To address the issues,we propose to conduct traffic classification with two newly defined QoSaware features,i.e.,inter-APP similarity and intraAPP diversity.The inter-APP similarity represents the close QoS association between the traffic flows that originate from the different Internet applications.The intra-APP diversity describes the QoS variety of the traffic even among those originated from the same Internet application.The core of performing the QoS-aware feature extraction is a Long-Short Term Memory neural network based Autoencoder(LSTMAE).The QoS-aware features extracted by the encoder part of the LSTM-AE are then clustered into the corresponding QoS classes.Real-life data from multiple applications are collected to evaluate the proposed QoS-aware network traffic classification approach.The evaluation results demonstrate the efficacy of the extracted QoS-aware features in supporting the traffic classification,which can further contribute to future network measurement and management.

Realistic Efficiency Evaluations for Parallel Computations under Workstation Cluster

In recent years, high performance scientific computing under workstation cluster connected by local area network is becoming a hot point. Owing to both the longer latency and the higher overhead for protocol processing compared with the powerful single workstation capacity, it is becoming severe important to keep balance not only for numerical load but also for communication load, and to overlap communications with computations while parallel computing. Hence,our efficiency evaluation rules must discover these capacities of a given parallel algorithm in order to optimize the existed algorithm to attain its highest parallel efficiency. The traditional efficiency evaluation rules can not succeed in this work any more. Fortunately, thanks to Culler's detail discuss in LogP model about interconnection networks for MPP systems, we present a system of efficiency evaluation rules for parallel computations under workstation cluster with PVM3.0 parallel software framework in this paper. These rules can satisfy above acquirements successfully. At last, two typical synchronous,and asynchronous applications are designed to verify the validity of these rules under 4 SGIs workstations cluster connected by Ethernet.

Reliability Evaluation of Mobile Communication Networks

A new reliability evaluation measure, global clustering reliability (GCR), is proposed. Firstly, the common measures used in invulnerability and survivability evaluation of mobile communication networks are discussed, and the shortcomings of these measures are pointed out. Then a new reliability evaluation measure, GCR, which is applicable to mobile communication networks, is proposed. And some properties and theorem about this measure are put forward. Finally, simulation calculation of reliability evaluation that uses this measure to 12 kinds of topological networks is accomplished. And the comparison between this measure and link connected factor (LCF) measure is also given. The results proved that the design of GCR is reasonable, its computation is rapid, moreover, it can take into account of invalidation of both nodes and links, and it has good physical meanings

Four Types of Percolation Transitions in the Cluster Aggregation Network Model

We study the percolation transition in a one-species cluster aggregation network model, in which the parameter α describes the suppression on the cluster sizes. It is found that the model can exhibit four types of percolation transitions, two continuous percolation transitions and two discontinuous ones. Continuous and discontinuous percolation transitions can be distinguished from each other by the largest single jump. Two types of continuous percolation transitions show different behaviors in the time gap. Two types of discontinuous percolation transitions are different in the time evolution of the cluster size distribution. Moreover, we also find that the time gap may also be a measure to distinguish different discontinuous percolations in this model.

Optimization of satellite searching strategy of the non-stationary antenna

The tiny searching step length and the satellite distribution density are the major factors to influence the efficiency of the satellite finder,so a scientific and reasonable method to calculate the tiny searching step length is proposed to optimize the satellite searching strategy. The pattern clustering and BP neural network are applied to optimize the tiny searching step length. The calculated tiny searching step length is approximately equal to the theoretic value for each satellite. In application,the satellite searching results will be dynamically added to the training samples to re-train the network to improve the generalizability and the precision. Experiments validate that the optimization of the tiny searching step length can avoid the error of locating target satellite and improve the searching efficiency.

Exact Solution for Clustering Coefficient of Random Apollonian Networks

By means of the series method, we obtain the exact analytical solution of clustering coefficient in random Apollonian networks [Phys. Rev. E 71 （2005）046141]. Our exact analytical result is identical with the simulation, whereas in the original work, there is a deviation of about 4% between their approximate analytical result and the simulation.

Modeling and Mining the Temporal Patterns of Service in Cellular Network

Recent emergence of diverse services have led to explosive traffic growth in cellular data networks. Understanding the service dynamics in large cellular networks is important for network design, trouble shooting, quality of service(Qo E) support, and resource allocation. In this paper, we present our study to reveal the distributions and temporal patterns of different services in cellular data network from two different perspectives, namely service request times and service duration. Our study is based on big traffic data, which is parsed to readable records by our Hadoop-based packet parsing platform, captured over a week-long period from a tier-1 mobile operator's network in China. We propose a Zipf's ranked model to characterize the distributions of traffic volume, packet, request times and duration of cellular services. Two-stage method(Self-Organizing Map combined with kmeans) is first used to cluster time series of service into four request patterns and three duration patterns. These seven patterns are combined together to better understand the fine-grained temporal patterns of service in cellular network. Results of our distribution models and temporal patterns present cellular network operators with a better understanding of the request and duration characteristics of service, which of great importance in network design, service generation and resource allocation.

Effects of channel blocks on the spiking regularity in clustered neuronal networks

In this paper,we discuss the influences of channel blocks on the spiking regularity in a clustered neuronal network by applying stochastic Hodgkin-Huxley neuronal models as the building blocks.With the aid of simulation results,we reveal that the spiking regularity of the clustered neuronal network could be resonantly enhanced via fine-tuning of the non-blocked potassium channel fraction xK.While the non-blocked sodium channel fraction xNa can enhance the spiking regularity of the clustered neuronal network in most cases.These results indicate that not only sodium channel blocks but also potassium channel blocks could have great influences on the regularity of spike timings in the clustered neuronal networks.Considering the importance of spike timings in neuronal information transforming processes,our results may give some implications for understanding the nonnegligible role of randomness in ion channels in neuronal systems.