Honey Badger Algorithm Based Clustering with Routing Protocol for Wireless Sensor Networks

Wireless sensor network(WSN)includes a set of self-organizing and homogenous nodes employed for data collection and tracking applications.It comprises a massive set of nodes with restricted energy and processing abilities.Energy dissipation is a major concern involved in the design of WSN.Clustering and routing protocols are considered effective ways to reduce the quantity of energy dissipation using metaheuristic algorithms.In order to design an energy aware cluster-based route planning scheme,this study introduces a novel Honey Badger Based Clustering with African Vulture Optimization based Routing(HBAC-AVOR)protocol for WSN.The presented HBAC-AVOR model mainly aims to cluster the nodes in WSN effectually and organize the routes in an energy-efficient way.The presented HBAC-AVOR model follows a two stage process.At the initial stage,the HBAC technique is exploited to choose an opti-mal set of cluster heads(CHs)utilizing afitness function involving many input parameters.Next,the AVOR approach was executed for determining the optimal routes to BS and thereby lengthens the lifetime of WSN.A detailed simulation analysis was executed to highlight the increased outcomes of the HBAC-AVOR protocol.On comparing with existing techniques,the HBAC-AVOR model has outperformed existing techniques with maximum lifetime.

Cooperative Subcarrier Sensing Using Antenna Diversity Based Weighted Virtual Sub Clustering

The idea of cooperation and the clustering amongst cognitive radios(CRs) has recently been focus of attention of research community, owing to its potential to improve performance of spectrum sensing(SS) schemes. This focus has led to the paradigm of cluster based cooperative spectrum sensing(CBCSS). In perspective of high date rate 4th generation wireless systems, which are characterized by orthogonal frequency division multiplexing(OFDM) and spatial diversity, there is a need to devise effective SS strategies. A novel CBCSS scheme is proposed for OFDM subcarrier detection in order to enable the non-contiguous OFDM(NC-OFDM) at the physical layer of CRs for efficient utilization of spectrum holes. Proposed scheme is based on the energy detection in MIMO CR network, using equal gain combiner as diversity combining technique, hard combining(AND, OR and Majority) rule as data fusion technique and antenna diversity based weighted clustering as virtual sub clustering algorithm. Results of proposed CBCSS are compared with conventional CBCSS scheme for AND, OR and Majority data fusion rules. Moreover the effects of antenna diversity, cooperation and cooperating clusters are also discussed.

Clustering algorithm based on density function and nichePSO

This paper introduces niching particle swarm optimiza- tion （nichePSO） into clustering analysis and puts forward a cluster- ing algorithm which uses nichePSO to optimize density functions. Firstly, this paper improves main swarm training models and in- creases their ability of space searching. Secondly, the radius of sub-swarms is defined adaptively according to the actual clus- tering problem, which can be useful for the niches＇ forming and searching. At last, a novel method that distributes samples to the corresponding cluster is proposed. Numerical results illustrate that this algorithm based on the density function and nichePSO could cluster unbalanced density datasets into the correct clusters auto- matically and accurately.

Fast density peak-based clustering algorithm for multiple extended target tracking

The key challenge of the extended target probability hypothesis density (ET-PHD) filter is to reduce the computational complexity by using a subset to approximate the full set of partitions. In this paper, the influence for the tracking results of different partitions is analyzed, and the form of the most informative partition is obtained. Then, a fast density peak-based clustering (FDPC) partitioning algorithm is applied to the measurement set partitioning. Since only one partition of the measurement set is used, the ET-PHD filter based on FDPC partitioning has lower computational complexity than the other ET-PHD filters. As FDPC partitioning is able to remove the spatially close clutter-generated measurements, the ET-PHD filter based on FDPC partitioning has good tracking performance in the scenario with more clutter-generated measurements. The simulation results show that the proposed algorithm can get the most informative partition and obviously reduce computational burden without losing tracking performance. As the number of clutter-generated measurements increased, the ET-PHD filter based on FDPC partitioning has better tracking performance than other ET-PHD filters. The FDPC algorithm will play an important role in the engineering realization of the multiple extended target tracking filter.

Effective search with saliency-based matching and cluster-based browsing

Similarity matching and information presentation are two key factors in information retrieval.In this paper,a saliency-based matching algorithm is proposed for user-oriented search based on the psychological studies on human perception,and major emphasis on the saliently similar aspect of objects to be compared is placed and thus the search result is more agreeable for users.After relevant results are obtained,the cluster-based browsing algorithm is adopted for search result presentation based on social network analysis.By organizing the results in clustered lists,the user can have a general understanding of the whole collection by viewing only a small part of results and locate those of major interest rapidly.Experimental results demonstrate the advantages of the proposed algorithm over the traditional work.

Combined Density-based and Constraint-based Algorithm for Clustering

We propose a new clustering algorithm that assists the researchers to quickly and accurately analyze data. We call this algorithm Combined Density-based and Constraint-based Algorithm (CDC). CDC consists of two phases. In the first phase, CDC employs the idea of density-based clustering algorithm to split the original data into a number of fragmented clusters. At the same time, CDC cuts off the noises and outliers. In the second phase, CDC employs the concept of K-means clustering algorithm to select a greater cluster to be the center. Then, the greater cluster merges some smaller clusters which satisfy some constraint rules. Due to the merged clusters around the center cluster, the clustering results show high accuracy. Moreover, CDC reduces the calculations and speeds up the clustering process. In this paper, the accuracy of CDC is evaluated and compared with those of K-means, hierarchical clustering, and the genetic clustering algorithm (GCA) proposed in 2004. Experimental results show that CDC has better performance.

Capacity Research in Cluster-Based Underwater Wireless Sensor Networks Based on Stochastic Geometry

Underwater wireless sensor networks(UWSNs) have attracted wide attention in recent years.The capacity research on it is still in the initial stage,lacking adequate performance evaluation for network construction.This paper will focus on this subject by theoretical analysis and simulation,aiming to provide some insights for the actual UWSNs construction.According to the structure features of cluster-based UWSNs and the propagation characteristics of underwater acoustic signal,with the combination of signal to interference plus noise ratio,we define some capacity performance metrics,such as outage probability and transmission capacity.Based on the theory of stochastic geometry,a network capacity analytical model used in the cluster-based UWSNs is presented.The simulation results verify the validity of the theoretical analysis,and the cause of error between theoretical and simulation results has also been clearly explained.

A Novel Method of Deinterleaving Radar Pulse Sequences Based on a Modified DBSCAN Algorithm

A modified DBSCAN algorithm is presented for deinterleaving of radar pulses in modern EW environments.A main characteristic of the proposed method is that using only time of arrival of pulses,the method can sort the pulses efficiently.Other PDW information such as rise time,carrier frequency,pulse width,modulation on pulse,fall time and direction of arrival are not required.To identify the valid PRIs in a set of interleaved pulses,an innovative modification of the DBSCAN algorithm is introduced which is accurate and easy to implement.The proposed method determines valid PRIs more accurately and neglects the spurious ones more efficiently as compared to the classical histogram based algorithms such as SDIF.Furthermore,without specifying any input parameter,the proposed method can deinterleave radar pulses while up to 30%jitter is present in the associated PRI.The accuracy and efficiency of the proposed method are verified by computer simulations and real data results.Experimental simulations are based on different real and operational scenarios where the presence of missing and spurious pulses are also considered.So,the simulation results can be of practical significance.

Clustering Network Topology Control Method Based on Responsibility Transmission

The topology control is an effective approach which can improve the quality of wireless sensor network at all sides. Through studying the mechanism of sensor network data transmission, the nature of data transmission in wireless sensor network is concluded as a kind of responsibility transmission. By redefining the responsibility and availability of nodes, the strategy for cluster head selection is studied, the responsibility and availability is determined by the combination of the residual energy, location and current flow of nodes. Based on the above, new clustering network topology control algorithm based on responsibility transmission CNTCABRT and hierarchical multi-hop CNTCABRT is presented in this paper, whose algorithm structure is along the famous LEACH algorithm. Experimental result demonstrates its promising performance over the famous LEACH algorithm in the cluster head selection, the size of cluster, the deployment of nodes and the lifetime of nodes, and several innovative conclusions are proposed finally.

3D Model Retrieval Method Based on Affinity Propagation Clustering

In order to improve the accuracy and efficiency of 3D model retrieval,the method based on affinity propagation clustering algorithm is proposed. Firstly,projection ray-based method is proposed to improve the feature extraction efficiency of 3D models. Based on the relationship between model and its projection,the intersection in 3D space is transformed into intersection in 2D space,which reduces the number of intersection and improves the efficiency of the extraction algorithm. In feature extraction,multi-layer spheres method is analyzed. The two-layer spheres method makes the feature vector more accurate and improves retrieval precision. Secondly,Semi-supervised Affinity Propagation ( S-AP) clustering is utilized because it can be applied to different cluster structures. The S-AP algorithm is adopted to find the center models and then the center model collection is built. During retrieval process,the collection is utilized to classify the query model into corresponding model base and then the most similar model is retrieved in the model base. Finally,75 sample models from Princeton library are selected to do the experiment and then 36 models are used for retrieval test. The results validate that the proposed method outperforms the original method and the retrieval precision and recall ratios are improved effectively.

A Content-Based Parallel Image Retrieval System on Cluster Architectures

We propose a content-based parallel image retrieval system to achieve high responding ability. Our system is developed on cluster architectures. It has several retrieval. servers to supply the service of content-based image retrieval. It adopts the Browser/Server (B/S) mode. The users could visit our system though web pages. It uses the symmetrical color-spatial features (SCSF) to represent the content of an image. The SCSF is effective and efficient for image matching because it is independent of image distortion such as rotation and flip as well as it increases the matching accuracy. The SCSF was organized by M-tree, which could speedup the searching procedure. Our experiments show that the image matching is quickly and efficiently with the use of SCSF. And with the support of several retrieval servers, the system could respond to many users at mean time. Key words content-based image retrieval - cluster architecture - color-spatial feature - B/S mode - task parallel - WWW - Internet CLC number TP391 Foundation item: Supported by the National Natural Science Foundation of China (60173058)Biography: ZHOU Bing (1975-), male, Ph. D candidate, reseach direction: data mining, content-based image retrieval.

Preparation, Structures and Magnetic Properties of Two Co-based Clusters with Triangular Core and Cuboidal Core Geometries

Two novel Co-based clusters with the 2-(hydroxylmethyl)pyridine(hmpH)ligand,formulated as[Co3(hmp)6(hmpH)]×2NO3×3H2O(ZTU-3)and[Co4(hmp)4(CH3CO2)2(H2O)4]×2NO3(ZTU-4),have been successfully synthesized and structurally characterized.ZTU-3 features a triangular core geometry,while ZTU-4 exhibits a cuboidal core geometry.In addition,the magnetic properties of ZTU-3 and ZTU-4 are also all investigated.

A heuristic clustering algorithm based on high density-connected partitions

Clustering data with varying densities and complicated structures is important,while many existing clustering algorithms face difficulties for this problem. The reason is that varying densities and complicated structure make single algorithms perform badly for different parts of data. More intensive parts are assumed to have more information probably,an algorithm clustering from high density part is proposed,which begins from a tiny distance to find the highest density-connected partition and form corresponding super cores,then distance is iteratively increased by a global heuristic method to cluster parts with different densities. Mean of silhouette coefficient indicates the cluster performance. Denoising function is implemented to eliminate influence of noise and outliers. Many challenging experiments indicate that the algorithm has good performance on data with widely varying densities and extremely complex structures. It decides the optimal number of clusters automatically.Background knowledge is not needed and parameters tuning is easy. It is robust against noise and outliers.

A Semantic-based Clustering Method to Build Domain Ontology from Multiple Heterogeneous Knowledge Sources

In order to improve the efficiency of ontology construction from heterogeneous knowledge sources, a semantic-based approach is presented. The ontology will be constructed with the application of cluster technique in an incremental way. Firstly, terms will be extracted from knowledge sources and congregate a term set after pretreat-ment. Then the concept set will be built via semantic-based clustering according to semanteme of terms provided by WordNet. Next, a concept tree is constructed in terms of mapping rules between semanteme relationships and concept relationships. The semi-automatic approach can avoid non-consistence due to knowledge engineers having different understanding of the same concept and the obtained ontology is easily to be expanded.

A Silicon Cluster Based Single Electron Transistor with Potential Room-Temperature Switching

We demonstrate the fabrication of a single electron transistor device based on a single ultra-small silicon quantum dot connected to a gold break junction with a nanometer scale separation. The gold break junction is created through a controllable electromigration process and the individual silicon quantum dot in the junction is deter- mined to be a Si 170 cluster. Differential conductance as a function of the bias and gate voltage clearly shows the Coulomb diamond which confirms that the transport is dominated by a single silicon quantum dot. It is found that the charging energy can be as large as 300meV, which is a result of the large capacitance of a small silicon quantum dot （-1.8 nm）. This large Coulomb interaction can potentially enable a single electron transistor to work at room temperature. The level spacing of the excited state can be as large as 10meV, which enables us to manipulate individual spin via an external magnetic field. The resulting Zeeman splitting is measured and the g factor of 2.3 is obtained, suggesting relatively weak electron-electron interaction in the silicon quantum dot which is beneficial for spin coherence time.

Metamodel-based Global Optimization Using Fuzzy Clustering for Design Space Reduction

High fidelity analysis are utilized in modern engineering design optimization problems which involve expensive black-box models.For computation-intensive engineering design problems,efficient global optimization methods must be developed to relieve the computational burden.A new metamodel-based global optimization method using fuzzy clustering for design space reduction(MGO-FCR) is presented.The uniformly distributed initial sample points are generated by Latin hypercube design to construct the radial basis function metamodel,whose accuracy is improved with increasing number of sample points gradually.Fuzzy c-mean method and Gath-Geva clustering method are applied to divide the design space into several small interesting cluster spaces for low and high dimensional problems respectively.Modeling efficiency and accuracy are directly related to the design space,so unconcerned spaces are eliminated by the proposed reduction principle and two pseudo reduction algorithms.The reduction principle is developed to determine whether the current design space should be reduced and which space is eliminated.The first pseudo reduction algorithm improves the speed of clustering,while the second pseudo reduction algorithm ensures the design space to be reduced.Through several numerical benchmark functions,comparative studies with adaptive response surface method,approximated unimodal region elimination method and mode-pursuing sampling are carried out.The optimization results reveal that this method captures the real global optimum for all the numerical benchmark functions.And the number of function evaluations show that the efficiency of this method is favorable especially for high dimensional problems.Based on this global design optimization method,a design optimization of a lifting surface in high speed flow is carried out and this method saves about 10 h compared with genetic algorithms.This method possesses favorable performance on efficiency,robustness and capability of global convergence and gives a new optimization strategy for engineering design optimization problems involving expensive black box models.

Destination Based Stable Clustering Algorithm and Routing for VANET

Now in modern telecommunication, one of the big topic research is a Vehicle Ad-hoc Network “VANET” (V2V). This topic is one of an “issues of the day” because research has problematic topic due to its many application-questions, what we need to solve: avoid collisions, any accidents on a way, and notifications about congestions on the road, available car parking, road-side commercial-business ads, and etcetera. These like application forms creating big delay constraining’s i.e. the instant data should reach the destination within certain time limits. Therefore, we need a really efficient stable clustering method and routing in vehicle ad-hoc network which will be resistant to network delays and meets network requirements. The methods are proposed in the paper for optimization VANETs data traffic as well as to minimizing delay. First, here is presented, a stable clustering algorithm based on the destination, contextually take into consideration various physical parameters for cluster formation such as location of the vehicle and its direction, vehicle speed and destination, as well as a possible list of interests of the vehicle. And also the next main process is to depend on these “five parameters” we can calculate the “Cluster Head Eligibility” of each car. Second, based on this “Cluster Head Eligibility”, described cluster head selection method. Third, for efficient communication between clusters, present a routing protocol based on the “destination”, which considered an efficient selecting method of next forwarding nodes, which is calculated by using “FE” metric.

Improved Clustering Algorithm Based on Density-Isoline

An improved clustering algorithm was presented based on density-isoline clustering algorithm. The new algorithm can do a better job than density-isoline clustering when dealing with noise, not having to literately calculate the cluster centers for the samples batching into clusters instead of one by one. After repeated experiments, the results demonstrate that the improved density-isoline clustering algorithm is significantly more efficiency in clustering with noises and overcomes the drawbacks that traditional algorithm DILC deals with noise and that the efficiency of running time is improved greatly.

LeaDen-Stream: A Leader Density-Based Clustering Algorithm over Evolving Data Stream

Clustering evolving data streams is important to be performed in a limited time with a reasonable quality. The existing micro clustering based methods do not consider the distribution of data points inside the micro cluster. We propose LeaDen-Stream (Leader Density-based clustering algorithm over evolving data Stream), a density-based clustering algorithm using leader clustering. The algorithm is based on a two-phase clustering. The online phase selects the proper mini-micro or micro-cluster leaders based on the distribution of data points in the micro clusters. Then, the leader centers are sent to the offline phase to form final clusters. In LeaDen-Stream, by carefully choosing between two kinds of micro leaders, we decrease time complexity of the clustering while maintaining the cluster quality. A pruning strategy is also used to filter out real data from noise by introducing dense and sparse mini-micro and micro-cluster leaders. Our performance study over a number of real and synthetic data sets demonstrates the effectiveness and efficiency of our method.