Efficient Clustering Algorithm Research Articles

A Survey of Energy Efficient Clustering Algorithms in Wireless Sensor Networks

To make the most of network lifetime in Wireless Sensor Networks (WSNs) the paths for data transfer are chosen in a manner that the total energy utilized along the path is minimized. To support high scalability and better data aggregation, sensor nodes are regularly assembled into disjoint, non overlapping subsets called clusters. Clusters create hierarchical WSNs which incorporate efficient utilization of limited resources of sensor nodes and thus extends network lifetime. The goal of this paper is to show a cutting edge review on clustering algorithms reported in the literature of WSNs. This paper presents various energy efficient clustering algorithms in WSNs.

Efficient evolutionary spectral clustering

Evolutionary spectral clustering (ESC) represents a state-of-the-art algorithm for grouping objects evolving over time. It typically outperforms traditional static clustering by producing clustering results that can adapt to data drifts while being robust to short-term noise. A major drawback of ESC is given by its cubic complexity, e.g. O(N3), and high memory demand, namely O(N2), that make it unfeasible to handle datasets characterized by a large number N of patterns. In this paper, we propose a solution to this issue by presenting the efficient evolutionary spectral clustering algorithm (E2SC). First we introduce the notion of a smoothed graph Laplacian, then we exploit the incomplete Cholesky decomposition (ICD) to construct an approximation of the this smoothed Laplacian and reduce the size of the related eigenvalue problem from N to m, with m ≪ N. Furthermore, in contrast to the standard ICD algorithm, a stopping criterion based on the convergence of the cluster assignments after the selection of each pivot is used, which is effective also when there is not a fast decay of the Laplacian spectrum. Overall, the proposed approach scales linearly with respect to the number of input datapoints N and has low memory requirements because only matrices of size N × m and m × m are constructed.

An energy efficient multi-level route-aware clustering algorithm for wireless sensor networks: A self-organized approach

Energy maintenance is one of the crucial characteristics for wireless sensor networks. Clustering techniques in WSNs is wildly used to cope with sensor network deficiencies. Organizing nodes in such clusters and specifying a particular node in each cluster to undertake the task of intra-cluster and inter-cluster data communications leads to alleviate the number of transmissions and hence longer lifetime of the Network. Most of decentralized clustering protocols are performed without any acknowledgement of a route which data traverse to reach the base station. In this paper, a new distributed energy efficient multi-level route-aware clustering algorithm for WSNs called MLRC is proposed. To establish tree among sensor nodes, MLRC applies a route conscious manner in which nodes could gain desired information about possible routes to the destination. The proposed protocol eliminates extra generation of routing control packets by implementing cluster formation and routing tree construction, concurrently. Cluster heads are elected based on effective parameters. The algorithm could moderate energy consumption of relays close to the base station with assigning probability to adjacent cluster head and avoiding the insistence on the nearest cluster head selection. Experimental results illustrate that the protocol improves network longevity in comparison with other known protocols.

CLIMP: Clustering Motifs via Maximal Cliques with Parallel Computing Design.

A set of conserved binding sites recognized by a transcription factor is called a motif, which can be found by many applications of comparative genomics for identifying over-represented segments. Moreover, when numerous putative motifs are predicted from a collection of genome-wide data, their similarity data can be represented as a large graph, where these motifs are connected to one another. However, an efficient clustering algorithm is desired for clustering the motifs that belong to the same groups and separating the motifs that belong to different groups, or even deleting an amount of spurious ones. In this work, a new motif clustering algorithm, CLIMP, is proposed by using maximal cliques and sped up by parallelizing its program. When a synthetic motif dataset from the database JASPAR, a set of putative motifs from a phylogenetic foot-printing dataset, and a set of putative motifs from a ChIP dataset are used to compare the performances of CLIMP and two other high-performance algorithms, the results demonstrate that CLIMP mostly outperforms the two algorithms on the three datasets for motif clustering, so that it can be a useful complement of the clustering procedures in some genome-wide motif prediction pipelines. CLIMP is available at http://sqzhang.cn/climp.html.

Adaptive trajectory analysis of replicator dynamics for data clustering

We study the use of replicator dynamics for data clustering and structure identification. We investigate that replicator dynamics, while running, reveals informative transitions that correspond to the significant cuts over data. Occurrence of such transitions is significantly faster than the convergence of replicator dynamics. We exploit this observation to design an efficient clustering algorithm in two steps: (1) Cut Identification, and (2) Cluster Pruning. We propose an appropriate regularization to accelerate the appearance of transitions which leads to an adaptive replicator dynamics. A main computational advantage of this regularization is that the optimal solution of the corresponding objective function can be still computed via performing a replicator dynamics. Our experiments on synthetic and real-world datasets show the effectiveness of our algorithm compared to the alternatives.

A modified two-stage Markov clustering algorithm for large and sparse networks

BackgroundGraph-based hierarchical clustering algorithms become prohibitively costly in both execution time and storage space, as the number of nodes approaches the order of millions. ObjectiveA fast and highly memory efficient Markov clustering algorithm is proposed to perform the classification of huge sparse networks using an ordinary personal computer. MethodsImprovements compared to previous versions are achieved through adequately chosen data structures that facilitate the efficient handling of symmetric sparse matrices. Clustering is performed in two stages: the initial connected network is processed in a sparse matrix until it breaks into isolated, small, and relatively dense subgraphs, which are then processed separately until convergence is obtained. An intelligent stopping criterion is also proposed to quit further processing of a subgraph that tends toward completeness with equal edge weights. The main advantage of this algorithm is that the necessary number of iterations is separately decided for each graph node. ResultsThe proposed algorithm was tested using the SCOP95 and large synthetic protein sequence data sets. The validation process revealed that the proposed method can reduce 3–6 times the processing time of huge sequence networks compared to previous Markov clustering solutions, without losing anything from the partition quality. ConclusionsA one-million-node and one-billion-edge protein sequence network defined by a BLAST similarity matrix can be processed with an upper-class personal computer in 100 minutes. Further improvement in speed is possible via parallel data processing, while the extension toward several million nodes needs intermediary data storage, for example on solid state drives.

Design and Implementation a New Energy Efficient Clustering Algorithm Using the Fuzzy Logic and Genetic Algorithm for Wireless Sensor Networks

Wireless Sensor Networks are consist of small battery powered devices with limited energy resources. Once deployed, the small sensor nodes are usually inaccessible to the user, and thus replacement of the energy source is not feasible. Hence, one of the most important issues that need to be enhanced in order to improve the life span of the network is energy efficiency. to overcome this demerit many research have been done. The clustering is the one of the representative approaches. In this paper, we introduce a dynamic clustering algorithm using Fuzzy Logic and genetic algorithm. In fact, using fuzzy system design and system optimization by genetic algorithm is presented approach to select the best cluster head in sensor networks. Using random data set has been addressed to evaluate of fuzzy-genetic system presented in this paper and finally, MSE rate or mean error of sending the messages using proposed fuzzy system in comparison with LEACH method is calculated in select the cluster head. The results of evaluations is representative of a reduction the MSE metric in proposed method in comparison with LEACH method for select the cluster head. Reduce of MSE directly is effective on energy consumption and lifetime of wireless sensor network and can cause the reduce energy consumption and increase network lifetime. KEYWORD Wireless sensor networks, fuzzy logic, fuzzy system, genetic algorithm, energy consumption, clustering.

A Trusted and Energy Efficient Approach for Cluster-Based Wireless Sensor Networks

How to improve both security and energy efficiency is one of the most prominent issues in wireless sensor networks (WSNs). In this paper, we present a trusted and energy efficient approach for cluster-based WSNs. The approach offers (i) trusted hardware module design; (ii) energy-efficient clustering algorithm; and (iii) network operation. In the cluster-based WSNs, every cluster head is equipped with a designed trusted hardware module (TM) to improve security. Then, for improving the energy efficiency of the network, a trusted hardware based energy efficient clustering (TEEC) algorithm is proposed to select appropriate cluster heads. In the network operation, we use TEEC algorithm to perform clustering and apply TMs to achieve security functions. The analysis shows that our approach does well in resisting the attacks such as data confidentiality attack, data integrity attack, and compromise node attack. In addition, experimental results show that the TEEC algorithm makes more balanced clusters with the variance value of loads around 50% and prolongs the life cycle of the network at least 10% compared to traditional methods.

Improved Leach Algorithm for Energy Efficient Clustering of Wireless Sensor Network (WSN)

Improved Leach Algorithm for Energy Efficient Clustering of Wireless Sensor Network (WSN)

Topology control of tactical wireless sensor networks using energy efficient zone routing

The US Department of Defense (DoD) routinely uses wireless sensor networks (WSNs) for military tactical communications. Sensor node die-out has a significant impact on the topology of a tactical WSN. This is problematic for military applications where situational data is critical to tactical decision making. To increase the amount of time all sensor nodes remain active within the network and to control the network topology tactically, energy efficient routing mechanisms must be employed. In this paper, we aim to provide realistic insights on the practical advantages and disadvantages of using established routing techniques for tactical WSNs. We investigate the following established routing algorithms: direct routing, minimum transmission energy (MTE), Low Energy Adaptive Cluster Head routing (LEACH), and zone clustering. Based on the node die out statistics observed with these algorithms and the topological impact the node die outs have on the network, we develop a novel, energy efficient zone clustering algorithm called EZone. Via extensive simulations using MATLAB, we analyze the effectiveness of these algorithms on network performance for single and multiple gateway scenarios and show that the EZone algorithm tactically controls the topology of the network, thereby maintaining significant service area coverage when compared to the other routing algorithms.

Cooperative spectrum sensing based on side information for cognitive radio sensor networks in internet of things applications

In the internet of things IoT applications, cooperative spectrum sensing of cognitive radio sensor networks CRSN may use electronic objects or sensor nodes to cooperatively detect the spectrum of primary user. How to effectively fuse the local detection data and make a global decision is critical for CRSN. In order to improve the detection accuracy of CRSN, we propose a cooperative spectrum sensing scheme based on side information, and the scheme uses a cooperative spectrum sensing framework and an efficient clustering algorithm that can meet the specific requirements of IoT applications. Though mathematical modelling, minimising the missing detection probability is converted into clustering nodes. The clustering algorithm is to be used to find the optimal distance and localisation of nodes. Simulation results show that the proposed cooperative spectrum sensing scheme has better performance than the conventional cognitive radio models such as equal gain combining, and maximal ratio combining algorithm. In addition, the influence of sensor nodes' density and distance range on the sensing performance is also analysed.

Modified Clustering Algorithm for Energy Efficiency Utilizing Fuzzy Logic in WSN (MCF)

A number of research have taken place in the field of Wireless Sensor Networks (WSN) as there is continuous need of advancement in the field of wireless communication, digital technology and micro-electro-mechanical systems(MEMS) . So the need of growth of low cost, low power, multifunctional sensor nodes have been required. A Wireless Sensor Network is a collection of sensor nodes that have the capability of sensing any environmental phenomenon, processing that information and then sending that data to the base station. A single sensor node is not capable of capturing desired information from a particular region so a collection of nodes are arranged to get accurate and sufficient result. This collection of sensor nodes along with a base station will collaboratively form a network that is known as Wireless Sensor Network. As limited energy is one of the most important constraint of WSN so it must be assured that it is utilized in most efficient way. Clustering is best approach to remove redundant data transmission to base station. Each cluster has a cluster head that is responsible for transmitting data to base station for that cluster members. Cluster head (CH) collect the data from all members of its cluster and perform aggregation on these data to remove redundancy then send it to base station. So appropriate CH election is very important for improving efficiency. In this thesis we have presented a clustering approach that has taken a heterogeneous environment and uses fuzzy logic to elect CHs more efficiently. We have combined two parameters Distance and Residual Energy and apply fuzzy rules on that to find the priority of a node for being a CH. Simulation shows that using fuzzy logic in SEP (Stability Election Protocol) will improve the energy efficiency by providing better load distribution and utilizing the benefits of heterogeneity of network. We have shown our analysis on two parameters- Number of dead nodes and average energy of nodes

Text Mining Using Metadata for Generation of Side Information

Text Mining is knowledge discovery process from large database to find out unknown patterns. In many metadata based text mining applications, side information also known as metadata which is associated with the text document. There are different types side information containing large amount of data i.e. metadata, weblogs and non-textual data (image, video, etc.). The side information is difficult to estimate when it contains noisy data. To achieve this, there is scope of improvement in generating side information i.e. selecting efficient classification and clustering algorithms, providing security for clustered side information, document organization, exploring filtering approaches. In future, there is a scope to design an extended approach for clustering using classical partitioning and probabilistic model.

Transmission-Order Optimization for Bidirectional Device-to-Device (D2D) Communications Underlaying Cellular TDD Networks—A Graph Theoretic Approach

Device-to-device (D2D) communications underlaying cellular networks is a promising concept that has several advantages over the traditional cellular networks. In the TDD system, the frame structure defines the order of uplink and downlink transmission slots. Typically, a TDD system is synchronized, and the same transmission order (TO) is used in all cells. In a direct D2D link, we have the freedom of selecting the TO of the devices freely. To our best knowledge, no paper has explicitly examined the TO optimization problem in D2D communications underlaying cellular network so far. In this paper, we focus exactly on this problem: once the proper co-channel D2D pairs are determined in the network, how do we minimize the network interference by optimally determining the TOs in all D2D links (together with co-channel cellular links) in the network, which is an NP-complete problem. In this paper, we formulate the TO optimization problem from a graph theoretic point of view: 1) we show that the TO optimization problem is equal to a constraint balanced min-cut graph partitioning problem of our defined augmented graph; and 2) we propose and analyze a distributed and a centralized efficient asynchronous clustering algorithm for solving the TO optimization problem equivalently for the min-cut of our proposed augmented graph. Computer simulations for the TDD-based D2D underlaying cellular network show that the proposed distributed and centralized algorithms, called ABCAMiC and CABCAMiC, respectively, 1) remarkably outperform the reference case where all TOs are fixed, 2) converge within a relatively small number of steps and generally converge in only a few epochs even for a large number of cellular and D2D users, and 3) the expected performance of the (partly/fully) distributed ABCAMiC is almost equal to that of the centralized solution CABCAMiC, which generally gives near-global optimal solution to the TO optimization problem.

An Efficient and Scalable Density-based Clustering Algorithm for Normalize Data

Data clustering is a method of putting same data object into group. A clustering rule does partitions of a data set into many groups supported the principle of maximizing the intra-class similarity and minimizing the inter-class similarity. Finding clusters in object, particularly high dimensional object, is difficult when the clusters are different shapes, sizes, and densities, and when data contains noise and outliers. This paper provides a new clustering algorithm for normalized data set and proven that our new planned clustering approach work efficiently when dataset are normalized.

A New Randomized Algorithm for Community Detection in Large Networks

The problem of community detection (or clustering) in graphs plays an important role in analysis of complex large-scale networks and big data structures, arising in natural, behavioral and engineering sciences. Examples of such networks include, but are not limited to, World Wide Web (WWW) and Internet, social networks, ecological networks and food webs, cellular and molecular ensembles. A community (or a module) in a graph is a subset of its nodes, whose members are "densely" connected to each other yet have relatively few connections with nodes outside this subset. A number of algorithms to subdivide the nodes of large-scale graphs into communities have recently been proposed; many of them hunt for the graph’s partitions of maximal modularity. One of the most efficient graph clustering algorithms of this type is the Multi-Level Aggregation (or "Louvain") method. In this paper, a randomized counterpart of this algorithm is proposed, which provides a comparable "quality" of graph’s clustering, being however much faster on huge graphs. We demonstrate the efficiency of our algorithm, comparing its performance on several "benchmark" large-scale graphs with existing methods.

Hierarchical Routing with Energy Efficient Clustering Algorithm for Wireless Sensor Networks

Enormous changes in gathering, processing and disseminating data for varying applications and environments is brought down by the use of Wireless Sensor Networks (WSNs). The lifetime for WSNs plays an important role in survivability as energy efficiency is one of the serious concerns for WSNs. One of the energy efficient technique is Clustering, which divides sensor nodes into many clusters, each set of nodes has a cluster-head. The cluster formation and Cluster Head (CH) election are done by Energy Efficient Clustering (E2C). In E2C, the routing of data to the base station from cluster-head nodes is dealt, however, it is not clearly formulated. In cluster-based hierarchical routing, between cluster members and the BS, CH acts as an intermediate node. In the data forwarding phase, by considering both path cost and residual energy, CH selects a path from its route cache. This idea motivated us to know about the different protocols which suit for different applications and helped in developing suitable protocol used as robustly and efficient manner. The performance of E2C Single hop, CIDBR E2C, multi-tierE2C and multi-level hierarchical E2C are analyzed and compared through numerous simulation runs.

Enhanced Zone-Based Clustering Method for Energy Efficient Wireless Sensor Network

Wireless sensor network contains a number of tiny sensors to get data from the surface where it deployed. Clustering is done to increase energy of the node to some certain period. Enhanced network lifetime is an important task to be achieved by these sensor networks. In this paper, a procedure for evaluation of clustering efficiency, routing efficiency, energy efficiency and lifetime of two impenetrable wireless sensor network fields, using a distributed clustering approach, the hybrid energy efficient clustering algorithm has been proposed, which targets mainly on effectively prolonging the lifetime of wireless sensor networks. It is a well-distributed and energy-efficient clustering algorithm which employs three novel techniques: zone based transmission power, routing by means of distributed relay nodes and rapid cluster formation. The clustering process is effectively controlled, thus the number of cluster heads selection and the number of packets delivered to the base station is also found to be effective.

Exact Recovery in the Stochastic Block Model

The stochastic block model with two communities, or equivalently the planted bisection model, is a popular model of random graph exhibiting a cluster behavior. In the symmetric case, the graph has two equally sized clusters and vertices connect with probability p within clusters and q across clusters. In the past two decades, a large body of literature in statistics and computer science has focused on providing lower bounds on the scaling of | p - q| to ensure exact recovery. In this paper, we identify a sharp threshold phenomenon for exact recovery: if α = pn/log(n) and β = qn/ log(n) are constant (with α > β), recovering the communities with high probability is possible if (α + β/2) - √(αβ) > 1 and is impossible if (α + β/2) - √(αβ) <; 1. In particular, this improves the existing bounds. This also sets a new line of sight for efficient clustering algorithms. While maximum likelihood (ML) achieves the optimal threshold (by definition), it is in the worst case NP-hard. This paper proposes an efficient algorithm based on a semidefinite programming relaxation of ML, which is proved to succeed in recovering the communities close to the threshold, while numerical experiments suggest that it may achieve the threshold. An efficient algorithm that succeeds all the way down to the threshold is also obtained using a partial recovery algorithm combined with a local improvement procedure.

Zone-Based Clustering Approach for Separated Wireless Sensor Network Fields

Improved network lifetime is an important task to be achieved by these sensor networks. In this editorial note, a methodology for evaluation of clustering efficiency, routing efficiency, energy efficiency and lifetime of two dense sensor network fields by means of a distributed clustering approach, the hybrid energy efficient clustering algorithm (HEECA) has been proposed. It is a well-distributed and energy-efficient clustering algorithm which employs three innovative techniques: zone based transmission power (ZBTP) routing using distributed relay nodes (DRNs) and rapid cluster formation (RCF). The proposed scheme is compared with two well-evaluated existing distributed clustering algorithms O-LEACH and HEED.