clustering algorithm in rechargeable wireless sensor network (RWSN) is critical before developing a charging strategy. To improve the charging efficiency, reduce the total charging time and energy consumption, this pa...
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ISBN:
(纸本)9781450365857
clustering algorithm in rechargeable wireless sensor network (RWSN) is critical before developing a charging strategy. To improve the charging efficiency, reduce the total charging time and energy consumption, this paper proposes an Energy Sensing clustering algorithm (ESCA) considering of location and residual energy information of nodes simultaneously. The concept of the hybrid center to calculate the Wireless Charging Vehicle (WCV) charging location is introduced to completes the clustering iteration with the improved k-means. The simulation results show that ESCA performs better in WCV total charging time and charging efficiency.
Cluster analysis technique, as a promising data analysis method, still faces many challenges when processing today's complex data set. There are mainly three effectivity problems which are considered in this paper...
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ISBN:
(纸本)9781538652145
Cluster analysis technique, as a promising data analysis method, still faces many challenges when processing today's complex data set. There are mainly three effectivity problems which are considered in this paper. First, the prior knowledge, which is usually unavailable, is needed. Second, parameter tuning is difficult. Third, it usually provides little knowledge about the structure of clusters. Therefore, we present a clustering algorithm based on statistics granular merging of data blocks. A database is broken down into many data granule and then be gradually merged into small statistics homonomous subsets. This approach has not imposed needs for prior knowledge. Its tuning parameters is relatively easy to use. It effectively discovers the clusters and the noises in a computationally efficient way. The behavior of the proposed algorithm is illustrated on several 2D shape data sets, and the state-of-the-art performance comparing with some popular algorithms is demonstrated on several real world data sets.
A gaussian naive bayesian data classification model based on clustering algorithm was proposed for fast recognition and classification of unknown continuous data containing a large number of non-priori ***, the unknow...
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ISBN:
(纸本)9781510891357
A gaussian naive bayesian data classification model based on clustering algorithm was proposed for fast recognition and classification of unknown continuous data containing a large number of non-priori ***, the unknown data were extracted from the representative samples according to the information entropy measure for clustering to generate class ***, the mapping relationship between data and class labels was established by using the gaussian naive bayes algorithm, and the classification model was obtained through *** results show that this unsupervised analysis process has a good classification effect on new data.
This study presents a density-based incremental clustering algorithm which incorporates the concept of fuzzy set in clustering. Unlike other existing fuzzy clustering algorithms which are c-mean clustering where the n...
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ISBN:
(纸本)9781538655382
This study presents a density-based incremental clustering algorithm which incorporates the concept of fuzzy set in clustering. Unlike other existing fuzzy clustering algorithms which are c-mean clustering where the number of clusters must be pre-defined, the proposed algorithm incorporates the concept of fuzzy set into density-based clustering where the number of clusters is not restricted. Moreover, the proposed algorithm uses incremental clustering usually employed in stream data clustering, leading to linear computation time, rather than quadratic computation time as in other density-based clustering. The proposed algorithm outperforms other existing density-based clustering algorithms in terms of both clustering results and computation time. As a result, the proposed algorithm can much efficiently process large data sets than other density-based clustering algorithms.
This paper takes the logistics distribution record of Yifeng Weiye Group for the past two years as the basic research unit. By exploring the relationship between data fields, we use the idea of adaptive clustering alg...
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This paper takes the logistics distribution record of Yifeng Weiye Group for the past two years as the basic research unit. By exploring the relationship between data fields, we use the idea of adaptive clustering algorithm and spatial clustering analysis to process the attribute data of transportation capacity[8]. Basing on the obtained clustering results, we use Python and PHP technology to optimize the distribution area, and finally design an effective visual expression method to obtain the traffic situation knowledge. We can provide relevant analysis and technical support for enterprises to improve the efficiency of distribution logistics and optimize the structure of the industrial chain.
Since studies on privacy-preserving database outsourcing have been spotlighted in a cloud computing, databases need to be encrypted before being outsourced to the cloud. Rao et al. proposed a k-Means clustering algori...
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ISBN:
(纸本)9781538672358
Since studies on privacy-preserving database outsourcing have been spotlighted in a cloud computing, databases need to be encrypted before being outsourced to the cloud. Rao et al. proposed a k-Means clustering algorithm that supports the protection of sensitive data by using a paillier cryptosystemi[1]. However, the existing algorithm is inefficient due to bit-array based comparison. To solve this problem, we propose an efficient privacy-preserving k-Means clustering algorithm. First, we provide a new secure comparison protocol that performs the fast comparison of encrypted data. Second, we select center points by considering the distribution of the entire data. Finally, we show from our performance analysis that our clustering algorithm achieves about 300% better performance on average than the existing algorithm.
The current railway transit information transformation system depends heavily on perfect landside communication facilities, which will face challenges in the region where landside communication infrastructure is under...
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ISBN:
(纸本)9781538645093
The current railway transit information transformation system depends heavily on perfect landside communication facilities, which will face challenges in the region where landside communication infrastructure is underdeveloped. In this paper, we introduce two kinds of high altitude platforms(HAPs), aerostat and unmanned aerial vehicle(UAV) to construct HAP-based vehicle ad hoc network, which is a promising solution to supplement to the undeveloped landside communication infrastructure. A location-aided connectivity and connection time-based clustering algorithm(LCCT-CA), which takes connectivity and connection time into account, is proposed for the network. Simulation results show that the algorithm perform better than traditional algorithms, especially in terms of rapid networking and reliability.
Underwater acoustic sensor networks (UASNs) provide new opportunities for exploring oceans and consequently improving our understanding of the underwater world. clustering is a key technical measure of UASNs, which ba...
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ISBN:
(纸本)9781538683033
Underwater acoustic sensor networks (UASNs) provide new opportunities for exploring oceans and consequently improving our understanding of the underwater world. clustering is a key technical measure of UASNs, which balances the network energy and improves the network survival time obviously. In this paper, we propose a clustering algorithm based on 3D mesh, and design a hierarchical network model, a node addressing method for UANSs. The performance of the algorithm is compared with DS-VBF, IAR and GEDAR. The simulation demonstrates that our algorithm has advantages in terms of the end-to-end delay and the time-to-live (TTL).
Due to limitations of resource in wireless sensor networks (WSNs) enhancing the network lifetime has been of great concern. An efficient routing algorithm is known as clustering algorithm based routing protocol. In wh...
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ISBN:
(纸本)9783319608341;9783319608334
Due to limitations of resource in wireless sensor networks (WSNs) enhancing the network lifetime has been of great concern. An efficient routing algorithm is known as clustering algorithm based routing protocol. In which getting optimal cluster heads (CHs) and a number of them has been defiance. In this paper, a new fuzzy clustering algorithm is proposed to maximize the lifetime of WSNs. Network field in this approach, contains two types of sensors: free sensors that communicate directly with sink, and clustered sensors that send the sensed data to the sink through CHs which are preselected. This approach uses fuzzy logic to select free sensor nodes and CHs with four fuzzy parameters. These parameters are energy level of sink and sensor proximity to the sink in terms of free sensors selection, and energy level of sensor node and centrality of sensors in terms of CHs selection. The main goal of our algorithm is to extend the lifetime of WSNs by minimizing distributing the workload on CHs. The simulation results show that our proposed is more efficient than SET protocol.
With the continuous development of the demand of electric power users, the analysis of users' electric power consumption(EPS) is becoming more and more important. The frequency of data acquisition and the dimensio...
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ISBN:
(纸本)9781538655009
With the continuous development of the demand of electric power users, the analysis of users' electric power consumption(EPS) is becoming more and more important. The frequency of data acquisition and the dimension of data analysis are increasing, which makes the analysis of EPS behavior more precise. The development of user tag and portrait technology brings a more intuitive and concise expression to the analysis of user's EPS. Based on massive user archives, power load and EPS data, considering the user's power consumption characteristics and influencing factors, a user behavior tag library is built, and k-means algorithm is used to tag clustering to achieve different types of EPS behavior. The tag clustering results of 2000 industrial and commercial users show that the selected tags of EPS behavior is reasonable and the clustering algorithm is effective. The research results of this paper can provide powerful data support for power companies to understand the user's EPS habits, mining user's electric power needs and improve the service level.
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