Wireless sensor networks are usually made up of a large number of sensor nodes. Such large networks require algorithms which can maintain their performance while the network size gets larger and larger. clustering is ...
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ISBN:
(纸本)9781424427505
Wireless sensor networks are usually made up of a large number of sensor nodes. Such large networks require algorithms which can maintain their performance while the network size gets larger and larger. clustering is a very efficient method which can help many algorithms become scalable to networks of large sizes. Recently, Irregular Cellular Learning Automata is proposed as a suitable modeling tool for many sensor networks' applications and a clustering algorithm is given for proving this suitability. In this paper, we improve the proposed clustering algorithm which leads to more efficient clusters in terms of number of clusters, number of sparse clusters, and energy level of cluster heads.
A large number of small sensors in wireless sensor network are battery-powered;one of the most important design criteria for this type of network is energy efficiency. clustering provides an effective way for extendin...
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ISBN:
(纸本)9781424421077
A large number of small sensors in wireless sensor network are battery-powered;one of the most important design criteria for this type of network is energy efficiency. clustering provides an effective way for extending the lifetime of a sensor network. In this paper, we propose a maximum-Votes and Load-balance clustering algorithm (VLCA) for wireless sensor network. Each sensor collects votes from their neighbors and calculates the total vote received. The more votes a sensor accumulates, the more important it is in the whole network. During the clustering phase, sensors compete with each other based on the total votes each has received. The algorithm is completely distributed, locating-unaware and independent of network size and topology. Simulation results show that our VLCA can reduce the number of clusters by 20-50% and prolong the lifetime of a sensor network.
The tongue intelligent diagnosis and inference system Of Traditional Chinese Medicine (TCM) is a big, complex one, its data is of great amount and many types, also the data cluster has uncertainty. The clustering anal...
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ISBN:
(纸本)9787121074370
The tongue intelligent diagnosis and inference system Of Traditional Chinese Medicine (TCM) is a big, complex one, its data is of great amount and many types, also the data cluster has uncertainty. The clustering analysis can solve these difficult problems. In this article, the author introduced an advanced hybrid algorithm for artificial immune clustering and Radial-Basis Function (RBF) neural networks, by analyzing the weaknesses of the former artificial immune clustering algorithm. The algorithm based on artificial immune clustering was applied in Tongue Diagnosis (TD) of TCM (TCMTD) and the diagnosis model was constructed. It used the hepatic disease symptom as simulation. The experimental result demonstrated that the advanced artificial immune clustering algorithm could fleetly cluster on the large data and high dimension sample data and then determine optimal clustering centers to ensure the RBF neural network had good generalization ability, and the TCMTD model had good diagnostic ability, fast convergence rate and good generalization ability. So the modified clustering algorithm in digitization of TCMTD was feasible and valid.
In MANETs, the scalability problem has been solved by the clustering mechanism. However, current clustering algorithms consider on the network stability only in terms of some metrics affecting innercluster structure&#...
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ISBN:
(纸本)9781424435296
In MANETs, the scalability problem has been solved by the clustering mechanism. However, current clustering algorithms consider on the network stability only in terms of some metrics affecting innercluster structure's stability, and neglect some metrics affecting intercluster structure's stability which are more favorable to global stability. To solve this problem, a clustering algorithm is proposed in this paper. It gives a comprehensive measurement on stability metrics of the innercluster structure and the intercluster structure. For a better comprehension of our algorithm, an explanatory example is given. To compare the performance of our algorithm to that of clustering algorithms with clusterheads, we simulate the structural adjusting times and network overheads during the process of the cluster formation and maintenance. The conclusion shows that our algorithm is more favorable to the stability of the global hierarchical structure and reduces network overheads a lot, which improves the global network performance.
It is very important to maximize the lifetime of Wireless Sensor Networks (WSN) operated on limited power. In order to improve the energy efficiency and prolong the network lifetime, a new clustering algorithm based o...
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ISBN:
(纸本)9780769532875
It is very important to maximize the lifetime of Wireless Sensor Networks (WSN) operated on limited power. In order to improve the energy efficiency and prolong the network lifetime, a new clustering algorithm based on the optimum one-hop distance is presented. The relationship among the energy consumption, the device electronic energy and the one-hop transmission distance is analyzed, and then the inverse ratio of the energy consumption increment to the cube of the optimal one-hop distance is gained. So, the clustering algorithm based on the optimal one-hop distance is given to reduce energy consumption. All sensor nodes are divided into different static clusters based on the optimum one-hop distance, and the distance between the fore-and-aft adjacent duster heads equals or approximates to the optimum one-hop distance, which reduces the energy consumption for inter-cluster communication. At the same time, cluster head acts continuously as local control center and will not be replaced by the candidate cluster head until it almost exhausts its energy supply, which can lessen energy consumption for establishing the new cluster head. Finally, the simulation results demonstrate that the algorithm achieves good effect to prolonging the system lifetime.
DBSCAN is a density based clustering algorithm and its effectiveness for spatial datasets has been demonstrated in the existing literature. However, there are two distinct drawbacks for DBSCAN: (i) the performances of...
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DBSCAN is a density based clustering algorithm and its effectiveness for spatial datasets has been demonstrated in the existing literature. However, there are two distinct drawbacks for DBSCAN: (i) the performances of clustering depend on two specified parameters. One is the maximum radius of a neighborhood and the other is the minimum number of the data points contained in such neighborhood. In fact these two specified parameters define a single density. Nevertheless, without enough prior knowledge, these two parameters are difficult to be determined;(ii) with these two parameters for a single density, DBSCAN does not perform well to datasets with varying densities. The above two issues bring some difficulties in applications. To address these two problems in a systematic way, in this paper we propose a novel parameter free clustering algorithm named as APSCAN. Firstly, we utilize the Affinity Propagation (AP) algorithm to detect local densities for a dataset and generate a normalized density list. Secondly, we combine the first pair of density parameters with any other pair of density parameters in the normalized density list as input parameters for a proposed DDBSCAN (Double-Density-Based SCAN) to produce a set of clustering results. In this way, we can obtain different clustering results with varying density parameters derived from the normalized density list. Thirdly, we develop an updated rule for the results obtained by implementing the DDBSCAN with different input parameters and then synthesize these clustering results into a final result. The proposed APSCAN has two advantages: first it does not need to predefine the two parameters as required in DBSCAN and second, it not only can cluster datasets with varying densities but also preserve the nonlinear data structure for such datasets. Crown Copyright (C) 2011 Published by Elsevier B.V. All rights reserved.
clustering analysis is an active and challenge research direction in the field of data mining. In this paper we propose a new clustering algorithm based on dimensional reduction approach and K-harmonic means algorithm...
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ISBN:
(纸本)9781424421077
clustering analysis is an active and challenge research direction in the field of data mining. In this paper we propose a new clustering algorithm based on dimensional reduction approach and K-harmonic means algorithm. Numerical results illustrate that the new hybrid clustering algorithm has advantages in the computation time, iteration numbers and clustering results in most cases, and it is also an algorithm which is suitable for large scale data sets.
In existing clustering algorithms in mobile ad-hoc networks, some of them consider only stability of cluster heads, and some others take only, security, into account, while only a few of them consider both factors. We...
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ISBN:
(纸本)9780769534923
In existing clustering algorithms in mobile ad-hoc networks, some of them consider only stability of cluster heads, and some others take only, security, into account, while only a few of them consider both factors. We propose Voting-based clustering algorithm with subjective trust and stability (VCA) by accessing subjective trust of node through Bayesian method and by evaluating stability of node through computing the neighbor change ratio and the residual battery power of mobile nodes. The proposed algorithm implements electing cluster heads according to the subjective trust degree and the stability, of node. Compared with Lowest-ID, Highest-Degree and Weight-based distributed clustering algorithm (WCA), it can improve system performance, maintain network security, and have good generality, Simulation studies show that the proposed algorithm has less communication overhead and better efficiency than existing algorithms.
As a fundamental problem in data mining, pattern recognition and machine learning, clustering algorithm has been studied for decades, and has been improved in many aspects. However, parameter-free clustering algorithm...
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ISBN:
(纸本)9781424441983
As a fundamental problem in data mining, pattern recognition and machine learning, clustering algorithm has been studied for decades, and has been improved in many aspects. However, parameter-free clustering algorithms are still quite weak which makes their potential generalization to a lot of promising applications rather difficult. A parameter-free clustering algorithm based on density model is proposed in this paper. This algorithm explores in a dynamically constructed nearest neighbor graph to defect which points are of the same density model, and then agglomerates them into the same cluster. It requires neither previously nor interactively setting of pivotal parameters via range scaling and proportional criterion technique. Its overall computational complexity is O(n log n). And the experimental results demonstrate that the proposed algorithm can correctly recognize the arbitrary shaped clusters.
The coverage region of WLAN network is limited compare with cell phone system such as GSM and WCDMA. The favorable area to deploy WLAN is the area which has strong demand for wireless network. How to identify the need...
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ISBN:
(纸本)9781479927173
The coverage region of WLAN network is limited compare with cell phone system such as GSM and WCDMA. The favorable area to deploy WLAN is the area which has strong demand for wireless network. How to identify the needs and guide the deployment of WLAN, that isn't a easy issue. It will waste the investment if we deploy the WLAN in improper places. This paper proposes a solution which can collect customer feedback with the help of smart phone client software and affinity propagation algorithm is applied to determine which region should be deploy first according to giant user feedback from that client software. Actual results show that the method is feasible and effective. It can significantly improve the accuracy of deployment of WLAN and efficiency of operations.
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