Centralized Clustering Research Articles

Energy-Balanced On Demand Clustering Algorithm Based On Leach-C

As the use of Wireless Sensor Networks (WSNs) has grown enormously, the need for energy-efficient management has also risen. With advances in ubiquitous computing environment, WSNs have been broadly studied and many energy-efficient routing protocols had been proposed. LEACH (Low Energy Adaptive Clustering Hierarchy) is a popular cluster-based protocol, which provides distributed adaptive clustering and periodic cluster head (CH) selection rotation. As extension to LEACH, LEACH-C (LEACH Centralized) was proposed, in which the energy is utilized to select CH. However, both can’t guarantee cluster head distribution, in addition to considerable periodic clustering overhead. Furthermore, network topology change is a critical characteristic that has influence on communication path and load distribution among nodes. To resolve such problems, Energy-Balance on Demand Clustering Algorithm Based on LEACH-C is proposed. The algorithm adopts centralized cluster formation and distributed CH selection methods. Minimum energy clustering is used to divide the network into clusters, while energy and total communication distance are considered as secondary criteria to select optimal CH. From simulation results the proposed algorithm outperforms LEACH-C in life time, stability period and performance efficiency.

Analyzing the Performance of Centralized Clustering Techniques for Realistic Wireless Sensor Network Topologies

Clustering techniques in wireless sensor networks enables energy efficient coordination among the densely deployed nodes for data delivery till the base station. Many clustering protocols have been suggested in the recent past. The topology of the nodes, mostly seen in the literature, is of random type. This paper analyzes the performance aspects of various centralized clustering techniques for wireless sensor networks. LEACH-Centralized, KMeans-CP, FCM-CP and HSA-CP protocols have been compared with respect to clustering and data delivery process for various realistic topologies. The simulations were performed for these protocols and performance of the protocols has been critically analyzed. HSA-CP clustering method performs better compared to other techniques for almost each topology examined in the paper.

Cluster Head Selection Optimization Based on Genetic Algorithm to Prolong Lifetime of Wireless Sensor Networks

Abstract Wireless sensor networks gain ample interest because of their wide range of applications. Efficient energy consumption of nodes is the prime design issue for these networks. Clustering approaches prolong the network lifetime with the load balanced network. To achieve load balancing clustering algorithm rotate the role of cluster head among the nodes so, cluster head selection process is pivotal for clustering algorithms. Work of this paper presents a genetic algorithm based cluster head selection for centralized clustering algorithms to have a better load balanced network than the traditional clustering algorithm. Simulation shows that the proposed solution finds the optimal cluster heads and has prolonged network lifetime than the traditional clustering algorithms.

A New Energy-Efficient Cluster-Based Routing Protocol Using a Representative Path in Wireless Sensor Networks

Wireless sensor networks (WSNs) have been broadly studied with advances in ubiquitous computing environment. Because the resource of a sensor node is limited, it is important to use energy-efficient routing protocol in WSNs. The cluster-based routing is an efficient way to reduce energy consumption by decreasing the number of transmitted messages to the sink node. LEACH is the most popular cluster-based routing protocol, which provides an adaptive cluster generation and cluster header rotation. However, its communication range is limited since it assumes a direct communication between sensor nodes and a sink node. To resolve this problem, we propose a new energy-efficient cluster-based routing protocol, which adopts a centralized clustering approach to select cluster headers by generating a representative path. To support reliable data communication, we propose a multihop routing protocol that allows both intra- and intercluster communications. Based on a message success rate and a representative path, the sensor nodes are uniformly distributed in clusters so that the lifetime of network can be prolonged. Through performance analysis, we show that our energy-efficient routing protocol outperforms the existing protocols up to 2 times, in terms of the distribution of cluster members, the energy consumption, and the reliability of a sensor network.

Transmission-Efficient Clustering Method for Wireless Sensor Networks Using Compressive Sensing

Compressive sensing (CS) can reduce the number of data transmissions and balance the traffic load throughout networks. However, the total number of transmissions for data collection by using pure CS is still large. The hybrid method of using CS was proposed to reduce the number of transmissions in sensor networks. However, the previous works use the CS method on routing trees. In this paper, we propose a clustering method that uses hybrid CS for sensor networks. The sensor nodes are organized into clusters. Within a cluster, nodes transmit data to cluster head (CH) without using CS. CHs use CS to transmit data to sink. We first propose an analytical model that studies the relationship between the size of clusters and number of transmissions in the hybrid CS method, aiming at finding the optimal size of clusters that can lead to minimum number of transmissions. Then, we propose a centralized clustering algorithm based on the results obtained from the analytical model. Finally, we present a distributed implementation of the clustering method. Extensive simulations confirm that our method can reduce the number of transmissions significantly.

Locating communities on graphs with variations in community sizes

A fundamental problem in networking and computing is community detection. Various applications like finding web communities, uncovering the structure of social networks, or even analyzing a graph's structure to uncover Internet attacks are just some of the applications for which community detection is important. In this paper, we propose an algorithm that finds the entire community structure of a network, represented by an undirected, unweighted graph, based on local interactions between neighboring nodes and on an unsupervised centralized clustering algorithm. The novelty of the proposed approach is the fact that the algorithm is based on the use of network coordinates computed by a distributed algorithm. Experimental results and comparisons with the Lancichinetti et al. method (Phys. Rev. E 80(5 Pt 2), 056117, 2009; New J. Phys. 11(3), 033015, 2009) are presented for a variety of benchmark graphs with known community structure, derived by varying a number of graph parameters. Emphasis is given on benchmark graphs with significant variations in the size of their communities. Further experimental results are presented for two real dataset graphs, namely the Enron, and the Epinions graphs, from SNAP, the Stanford Large Network Dataset Collection. The experimental results demonstrate the high performance of our algorithm in terms of accuracy to detect communities, and its computational efficiency.

Collaborative clustering of XML documents

Clustering XML documents is extensively used to organize large collections of XML documents in groups that are coherent according to structure and/or content features. The growing availability of distributed XML sources and the variety of high-demand environments raise the need for clustering approaches that can exploit distributed processing techniques. Nevertheless, existing methods for clustering XML documents are designed to work in a centralized way. In this paper, we address the problem of clustering XML documents in a collaborative distributed framework. XML documents are first decomposed based on semantically cohesive subtrees, then modeled as transactional data that embed both XML structure and content information. The proposed clustering framework employs a centroid-based partitional clustering method that has been developed for a peer-to-peer network. Each peer in the network is allowed to compute a local clustering solution over its own data, and to exchange its cluster representatives with other peers. The exchanged representatives are used to compute representatives for the global clustering solution in a collaborative way. We evaluated effectiveness and efficiency of our approach on real XML document collections varying the number of peers. Results have shown that major advantages with respect to the corresponding centralized clustering setting are obtained in terms of runtime behavior, although clustering solutions can still be accurate with a moderately low number of nodes in the network. Moreover, the collaborativeness characteristic of our approach has revealed to be a convenient feature in distributed clustering as found in a comparative evaluation with a distributed non-collaborative clustering method.

Social Network Analysis for the Study of Poverty and Marginalization

This paper studies the application of social network analysis to the study of poverty and marginalization in rural zones. The problem of marginalization is a persistent phenomenon in the developing countries. One of the latest developments in public policies to mitigate this problem has been the use of spatial units called microregions that cluster communities populated by ethnic groups that share culture, trade, natural resources, and history. Some of these communities have a larger potential of development due to their natural condition of being centers of confluence in the microregion, therefore, the policies are oriented towards the investment in these nodes that can enhance the microregion in a process of economic development by means of social networks. This work is framed in the case of the Mexican version of the microregions approach, and challenges the criteria employed by the policy designers to define which communities have more potential for development. We develop a computational model with behavioral principles in order to simulate real effects of investment in education. These effects generate new social network structures in the microregion, therefore, the need to study the centrality of the main communi- ties under the methodology of social network analysis. We analyze the artificial networks created by our computational model, and compare the results in terms of centrality measures and clustering analysis with the original social network created by the collected data of the official program in Mexico. We find that there exist central players and clusters in the network that are not captured neither by the data collected by the government, nor by the conceptual framework of the policy. The results provide an formal piece of evidence that behavioral simulations and SNA are necessary for a better understanding of the social dynamics in the microregions. Furthermore, SNA should be an integral part of the tools for policy design when it comes with economic development through third-party effects and social externalities.

Hierarchically Distributed Peer-to-Peer Document Clustering and Cluster Summarization

In distributed data mining, adopting a flat node distribution model can affect scalability. To address the problem of modularity, flexibility and scalability, we propose a Hierarchically-distributed Peer-to-Peer (HP2PC) architecture and clustering algorithm. The architecture is based on a multi-layer overlay network of peer neighborhoods. Supernodes, which act as representatives of neighborhoods, are recursively grouped to form higher level neighborhoods. Within a certain level of the hierarchy, peers cooperate within their respective neighborhoods to perform P2P clustering. Using this model, we can partition the clustering problem in a modular way across neighborhoods, solve each part individually using a distributed K-means variant, then successively combine clusterings up the hierarchy where increasingly more global solutions are computed. In addition, for document clustering applications, we summarize the distributed document clusters using a distributed keyphrase extraction algorithm, thus providing interpretation of the clusters. Results show decent speedup, reaching 165 times faster than centralized clustering for a 250-node simulated network, with comparable clustering quality to the centralized approach. We also provide comparison to the P2P K-means algorithm and show that HP2PC accuracy is better for typical hierarchy heights. Results for distributed cluster summarization match those of their centralized counterparts with up to 88% accuracy.

A Tabu Search Algorithm for Cluster Building in Wireless Sensor Networks

The main challenge in wireless sensor network deployment pertains to optimizing energy consumption when collecting data from sensor nodes. This paper proposes a new centralized clustering method for a data collection mechanism in wireless sensor networks, which is based on network energy maps and quality-of-service (QoS) requirements. The clustering problem is modeled as a hypergraph partitioning and its resolution is based on a tabu search heuristic. Our approach defines moves using largest size cliques in a feasibility cluster graph. Compared to other methods (CPLEX-based method, distributed method, simulated annealing-based method), the results show that our tabu search-based approach returns high-quality solutions in terms of cluster cost and execution time. As a result, this approach is suitable for handling network extensibility in a satisfactory manner.

Immunological synapse arrays: patterned protein surfaces that modulate immunological synapse structure formation in T cells.

T cells are activated by recognition of foreign peptides displayed on the surface of antigen presenting cells (APCs), an event that triggers assembly of a complex microscale structure at the T cell-APC interface known as the immunological synapse (IS). It remains unresolved whether the unique physical structure of the synapse itself impacts the functional response of T cells, independent of the quantity and quality of ligands encountered by the T cell. As a first step toward addressing this question, we created multicomponent protein surfaces presenting lithographically defined patterns of tethered T cell receptor (TCR) ligands (anti-CD3 "activation sites") surrounded by a field of tethered intercellular adhesion molecule-1 (ICAM-1), as a model substrate on which T cells could be seeded to mimic T cell-APC interactions. CD4(+) T cells seeded on these surfaces polarized and migrated; on contact with activation sites, T cells assembled an IS with a structure modulated by the physical pattern of ligand encountered. On surfaces patterned with focal spots of TCR ligand, T cells stably interacted with activation sites, proliferated, and secreted cytokines. In contrast, T cells interacting with activation sites patterned to preclude centralized clustering of TCR ligand failed to form stable contacts with activation sites, exhibited aberrant PKC- clustering in a fraction of cells, and had significantly reduced production of IFN-gamma. These results suggest that focal clustering of TCR ligand characteristic of the "mature" IS may be required under some conditions for full T cell activation.

Improving availability and performance with application-specific data replication

The emerging edge services architecture promises to improve the availability and performance of Web services by replicating servers at geographically distributed sites. A key challenge in such systems is data replication and consistency, so that edge server code can manipulate shared data without suffering the availability and performance penalties that would be incurred by accessing a traditional centralized database. This work explores using a distributed object architecture to build an edge service data replication system for an e-commerce application, the TPC-W benchmark, which simulates an online bookstore. We take advantage of application-specific semantics to design distributed objects that each manages a specific subset of shared information using simple and effective consistency models. Our experimental results show that by slightly relaxing consistency within individual distributed objects, our application realizes both high availability and excellent performance. For example, in one experiment, we find that our object-based edge server system provides five times better response time over a traditional centralized cluster architecture and a factor of nine improvement over an edge service system that distributes code but retains a centralized database.