In large scale MANETs, centerless clustering algorithms need to reduce topology and routing maintenance overheads by constructing a stable hierarchical topology. So attention should be focused on topology's stabil...
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ISBN:
(纸本)9780769535579
In large scale MANETs, centerless clustering algorithms need to reduce topology and routing maintenance overheads by constructing a stable hierarchical topology. So attention should be focused on topology's stability. At present, attentions are paid to the innercluster topology's stability, but the intercluster topology's stability is neglected. Therefore, we propose a fully distributed clustering algorithm for MANETs in which both the innercluster topology's stability and the intercluster topology's stability are concerned. The main objectives of this algorithm consist in stabilizing the topology as a long time as possible and in further reducing the topology and routing maintenance overheads. For a better comprehension of our algorithm, an explanatory example is given. To compare our algorithm to lowest LD based mobile clustering algorithm, a simulation is studied. The conclusion shows that:our algorithm is more favorable to the topology's stability and reduces network overheads a tot, which improves the network performance.
7clustering is such an algorithm which merges the most similar pair of samples into the same classification at every iteration. The traditional similarity evaluation function is manually designed, but the recent inter...
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ISBN:
(纸本)9781479937066
7clustering is such an algorithm which merges the most similar pair of samples into the same classification at every iteration. The traditional similarity evaluation function is manually designed, but the recent interest focuses on supervised or semi-supervised learning where the ground-truth clustered data can be available for training. This paper will first describes how to train a similarity function by regarding it as the action-value function in reinforcement learning. Then, the agglomerative clustering algorithm with superpixel is applied to segment a challenging dataset of brain images. The experimental results demonstrate the proposed method remarkably improved the segmentation accuracy.
Reducing node energy consumption is a vital requirement in wireless sensor networks. In this paper, we studied classic clustering algorithms in wireless sensor networks and find two main reasons causing unnecessary en...
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ISBN:
(纸本)9781424427994
Reducing node energy consumption is a vital requirement in wireless sensor networks. In this paper, we studied classic clustering algorithms in wireless sensor networks and find two main reasons causing unnecessary energy consumption, which are fixed operation periods and too much information exchanged in cluster-heads selection. Then a more energy-efficient clustering algorithm is proposed, whose kernels are adaptive operation period model and a new cluster-heads selection method. Simulation results show that the proposed protocol is more energy-efficient and suitable for wireless sensor network.
Energy-efficient data gathering is a common but critical operation in many applications of wireless sensor networks. clustering is a kind of key technique used to reduce energy consumption, which can decrease the comm...
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ISBN:
(纸本)9781424427994
Energy-efficient data gathering is a common but critical operation in many applications of wireless sensor networks. clustering is a kind of key technique used to reduce energy consumption, which can decrease the communication load and prolong the network lifetime by means of similar data aggregation in the cluster-heads. In this paper, we propose a novel clustering algorithm which better suit the periodical data gathering applications. Our approach first use genetic algorithm to partition the adjacent nodes which will sense similar target into one cluster, then elects cluster-heads with more residual energy and fewer intra-cluster communication cost. Since improving the rate of data aggregation in clusters, our approach can effectively reduce redundant data transmission and the whole energy consumed in the network. Our experimental results demonstrate that the proposed algorithms significantly outperform previous methods, in terms of system lifetime.
This paper focuses on clustering algorithm of many-dimensional objects, where only the distances between objects are used. Centers of classes are found with the aid of neuron-like procedure with lateral inhibition. Th...
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ISBN:
(纸本)9781424445165
This paper focuses on clustering algorithm of many-dimensional objects, where only the distances between objects are used. Centers of classes are found with the aid of neuron-like procedure with lateral inhibition. The result of clustering does not depend on starting conditions. Our algorithm makes it possible to give an clasters that really exist in the empirical data.
Most clustering algorithms, such as k-means and fuzzy c-means (FCM), are used to cluster a set of objects based on a function of dissimilarities between objects. However, clustering on attribute variables of objects m...
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ISBN:
(纸本)9781424435968
Most clustering algorithms, such as k-means and fuzzy c-means (FCM), are used to cluster a set of objects based on a function of dissimilarities between objects. However, clustering on attribute variables of objects may give more cluster information. Thus, to have a clustering algorithm that can be designated to construct simultaneously an optimal partition of objects and also attribute variables into homogeneous block is important. This kind of clustering was called block clustering (see Duffy and Quiroz, 1991). Recently, Govaert and Nadif (2003) proposed a block classification EM (block CEM) algorithm and then proposed block fuzzy c-methods (block FCM) in 2006. In this paper, based on Huang and Ng's (1999) fuzzy k-modes (FKM) method, we propose a block FKM clustering algorithm. Several examples are used to make the comparisons between block FCM and the proposed block FKM.
With the energy constrained nature of wireless sensors, it is a substantial design issue to make efficient use of battery power in order to increase their lifetime. Focuses on reducing energy consumption of wireless s...
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ISBN:
(纸本)9780769535227
With the energy constrained nature of wireless sensors, it is a substantial design issue to make efficient use of battery power in order to increase their lifetime. Focuses on reducing energy consumption of wireless sensor network, this paper proposed CABCF-DCS (clustering algorithm based on communication facility with deterministic cluster-size) algorithm. By changing the cluster-size of the cluster based on CABCF (clustering algorithm based on communication facility) algorithm, the new algorithm achieves the purpose of saving energy ultimately. Simulation results validate the energy efficiency of the new algorithm.
The dynamic nature of mobile nodes in mobile ad-hoc networks (MANETs), causes their association and disassociation to and from clusters perturb the stability of network and problem becomes worse if nodes are clusterhe...
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ISBN:
(纸本)9781424442614
The dynamic nature of mobile nodes in mobile ad-hoc networks (MANETs), causes their association and disassociation to and from clusters perturb the stability of network and problem becomes worse if nodes are clusterheads (CH). Therefore cluster maintenance schemes are needed to handle new admissions and releases of node in the clusters. In this paper, we introduce a novel cluster maintenance algorithm which selects a new clusterhead from a trusty area that is defined previously based on some maintenance optimization rules. The election process is done before the current clusterhead leaves the cluster. So the routes which include this clusterhead as a middle node are less probable to break and will be more stable. Number of nodes belonging to a cluster is restricted in the proposed algorithm. In order to prevent of overusing of clusterheads' battery power, the CH selection process is invoked whenever the battery power of current CH goes below some threshold.
Modern distance education is a new Web-based form of education. Enhancing personalized teaching standard of distance learning site is an important and difficult research in the development of modern distance education...
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ISBN:
(纸本)9780769535630
Modern distance education is a new Web-based form of education. Enhancing personalized teaching standard of distance learning site is an important and difficult research in the development of modern distance education. Based on rough set (RS), Web learners clustering model, learning features reduction and clustering algorithm are presented, which provides a basis of personalized teaching strategies for distance learning website. Further research is to mine and process the dynamic personality of learner's knowledge, and then to provide services on achieving real-time personalized teaching requirement.
Advancement and refinement of technology has resulted in widespread deployment of sensor networks in various facets of human life. Body and home area networks are an embodiment of their steady infiltration in our dail...
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ISBN:
(纸本)9781424427864
Advancement and refinement of technology has resulted in widespread deployment of sensor networks in various facets of human life. Body and home area networks are an embodiment of their steady infiltration in our daily lives. Inherent energy issues with the sensor network technology paradigm have brought an emphasis towards the energy efficiency issue, thereby, propelling researchers to focus on developing topologies with minimal energy consumption constraints. Our work revolves around home area networking in which we have explored a self-organizing clustered topology with a low latency periodic and query-based data collection solution and have proved our proposition to have a significant reduction in terms of energy expenditures.
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