Routing is the main research issue in the development of Wireless Sensor Networks (WSNs). Many routing approaches have been borrowed from Mobile Ad Hoc Networks (MANETs) to achieve routing solutions in WSNs. Here, Ad ...
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ISBN:
(纸本)9789881563835
Routing is the main research issue in the development of Wireless Sensor Networks (WSNs). Many routing approaches have been borrowed from Mobile Ad Hoc Networks (MANETs) to achieve routing solutions in WSNs. Here, Ad Hoc On-Demand Distance Vector Routing Protocol (AODV) is chosen and some improvements are made on it, in order to be suitable for the application of our network. First, the clustering algorithm is employed to avoid network congestion and generate a tree-based topology structure that network needs. Then, the gateway-auxiliary algorithm is applied to avoid the cluster heads becoming the bottlenecks. Last, OMNET++ is used to test the proposed protocol. The simulation results demonstrate that it works well in large-scale WSNs.
Social media are one of the main contributors of user generated content;providing vast amounts of data in daily basis, covering a wide range of topics, interests and events. In order to identify and link meaningful an...
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ISBN:
(纸本)9783642401398
Social media are one of the main contributors of user generated content;providing vast amounts of data in daily basis, covering a wide range of topics, interests and events. In order to identify and link meaningful and relevant information, clustering algorithms have been used to partition the user generated content. We have identified though that these algorithms exhibit various shortcomings when they have to deal with social media textual information, which is dynamic and streaming in nature. Thus we explore the idea to estimate the algorithms' parameters based on observations on the clusters' properties' (like the centroid, shape and density) evolution. By experimenting with the clusters' properties, we propose a methodological framework that detects the evolution of the clusters' centroid, shape and density and explores their role in parameters' estimation.
Gaussian mixture model-based clustering algorithm is one of the advanced techniques applied to enhance the image segmentation performance. However, segmentation process is still encountering some critical difficulties...
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ISBN:
(纸本)9781479948970
Gaussian mixture model-based clustering algorithm is one of the advanced techniques applied to enhance the image segmentation performance. However, segmentation process is still encountering some critical difficulties: the model is quite sensitive to initialization, and easily gets trapped in local maxima. To address these problems in image segmentation, we proposed a novel clustering algorithm employing the arbitrary covariance matrices that uses particle swarm optimization for the estimation of Gaussian Mixture Models. Such model can be able to prevent the effective use of population-base algorithms during clustering, and the arbitrary covariance matrices allow independently updating individual parameters, while retaining the validity of the matrix. Then we present the solution that involves an optimization formulation to identify the correspondence between different parameter orderings of candidate solutions. The experimental results show that our method provides a simple segmentation process and the better quality of segmented images comparing to other methods. Furthermore, our method would provide an advanced technique for multi-dimensional image analysis and computer vision systems that can apply for various science and technology sector.
Nowadays the core of social aggregation service is still about polymerization and recommendation of content. Yet, the mining of the user association behind the shared information is unsatisfactory. Based on static soc...
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ISBN:
(纸本)9781479913909
Nowadays the core of social aggregation service is still about polymerization and recommendation of content. Yet, the mining of the user association behind the shared information is unsatisfactory. Based on static social tags, a new algorithmclustering users is proposed, which clusters users by their similarity. Firstly, relevant concepts are defined to be used in the algorithm. And then, by analyzing existing clustering algorithms, K-means is chosen to be improved. The improved K-means algorithm is proposed and the basic flow of the algorithm is represented. Next, data from a Folksonomy site is used to conduct our empirical study, comparing the clustering effects of the two algorithms by experiment. As a result, the improved K-means algorithm shows better clustering effects than that of original algorithm and is suitable for clustering users.
BACR is a new hierarchical routing protocol based on LEACH designed for WSN. It introduced energy-involved voting and back-off announcement to control the number of cluster-heads to vary within a small range and evenl...
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ISBN:
(纸本)9780769551227
BACR is a new hierarchical routing protocol based on LEACH designed for WSN. It introduced energy-involved voting and back-off announcement to control the number of cluster-heads to vary within a small range and evenly locate the cluster-heads thus to prolong the network lifetime. This work also employed the technology of cluster reappointment to minimize the energy consumed in cluster set-up phase so as to extend the lifetime further. Experiments on MATLAB have proved this more effective than LEACH.
Topology management is crucial to the efficiency of a wireless communication network in which all nodes are energy constraint. clustering is a kind of energy efficient algorithm, while using a virtual backbone constru...
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ISBN:
(纸本)9789881563811
Topology management is crucial to the efficiency of a wireless communication network in which all nodes are energy constraint. clustering is a kind of energy efficient algorithm, while using a virtual backbone constructed by Connected Dominating Set (CDS) to organize the nodes is a better way. We propose an energy efficient clustering algorithm based on Local CDS construction (CLCDS) to form the CDS-based backbone with local information exchanges. In this algorithm, each node builds its local spanning tree independently and computes its degree flag to determine whether to act as a dominating node. As we elect the dominating nodes first and then connect them by a RNG [13] based algorithm, the number of border nodes and dominating nodes obtained by our algorithm is greatly reduced compared with previous researches. The correctness of the algorithm is proven and the efficiency is compared with other clustering heuristics using simulations. As the cluster balance will have a significant impact on the network performance, we also provide with a tunable scheme to achieve a fairly balanced cluster distribution across the networks.
The Local or remote grid computing resources have been normally assigned the task of executing user jobs in a distributed environment. Enumerable number of problems is likely to arise in execution process because of t...
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ISBN:
(纸本)9781467325943;9781467325929
The Local or remote grid computing resources have been normally assigned the task of executing user jobs in a distributed environment. Enumerable number of problems is likely to arise in execution process because of the existence of complicated heterogeneous virtuality in terms of processing nodes and network system. Another important factor affecting Grid processing is the network bandwidth that differs from network to network. In order to overcome this problem, a few typical algorithms are suggested keeping in view of computing grid resources based on load balancing across the grid system. These algorithms help in improving the performance to some extent, yet none of them has been found experimentally suitable to overcome the major short falls experienced in the grid computing system. We propose a hierarchically distributed Peer-to-Peer (HP2PC) architecture to overcome all major problems encountered due to virtuality and heterogeneity.
clustering spatial data is a well-known problem that has been extensively studied. In the real world, there are many physical obstacles such as rivers, lakes, highways, and mountains, whose presence may substantially ...
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clustering spatial data is a well-known problem that has been extensively studied. In the real world, there are many physical obstacles such as rivers, lakes, highways, and mountains, whose presence may substantially affect the clustering result. Although many methods have been proposed in previous works, very few have considered physical obstacles and interlinking bridges. Taking these constraints into account during the clustering process is costly, yet modeling the constraints is paramount for good performance. Owing to saturation in existing telephone networks and the ever increasing demand for wire and wireless services, telecommunication engineers are looking at technologies that can deliver sites and satisfy the demand and level of service constraints in an area with and without obstacles. In this paper, we study the problem of clustering in the presence of obstacles to solve the network planning problem. As such, we modified the Net-Plan algorithm and developed the COD-NETPLAN (clustering with Obstructed Distance - Network Planning) algorithm to solve the problem of 2D and 3D obstacles. We studied the problem of determining the location of the multi service access node in an area with many mountains and rivers. We used a reachability matrix to detect 2D obstacles, and line segment intersection together with geographical information system techniques for 3D obstacles. Experimental results and the subsequent analysis indicate that the COD-NETPLAN algorithm is both efficient and effective.
In recent years, with the development of e-commerce, the number of online products increases rapidly. Faced with such a mass of data, we need to establish an efficient unified product standard. However, in terms of th...
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ISBN:
(纸本)9780769550060
In recent years, with the development of e-commerce, the number of online products increases rapidly. Faced with such a mass of data, we need to establish an efficient unified product standard. However, in terms of the whole e-commerce industry there lacks a unified standard which can coordinate with existing standards. In this paper, we put forward an effective solution to unifying products, which is then applied to business intelligence data analysis. The main work and contributions are as follows: 1) an effective solution to unifying product data;2) a parallel data mining algorithms which solves the problem of identifying similar products from massive product data;3) the framework is universal, although our project is based on Taobao's data cloud, it can also be applied to other e-commerce area.
＜正＞In wireless sensor networks,the data-centric sensing task focuses more on the data *** this paper,a reliable clustering algorithm RCVC（Reliable clustering using Virtual Cluster-head） is *** provides multipath ...
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＜正＞In wireless sensor networks,the data-centric sensing task focuses more on the data *** this paper,a reliable clustering algorithm RCVC（Reliable clustering using Virtual Cluster-head） is *** provides multipath for nodes in the network by using a set of redundant cluster-heads which are marked with a unique virtual identification. When the active cluster-heads fail,according to a certain strategy,our algorithm repairs the communication failures by selecting a backup node from the set of redundant *** results show that RCVC can effectively reduce the influence of cluster-head failure on the performance of algorithm and improve the reliability of communication in the network.
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