In vehicular ad hoc networks (VANETs), due to the high dynamics of the vehicles, clustering is an effective way to alleviate the problem of frequent communication interruption. In recent years, many pieces of research...
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ISBN:
(纸本)9781728112206
In vehicular ad hoc networks (VANETs), due to the high dynamics of the vehicles, clustering is an effective way to alleviate the problem of frequent communication interruption. In recent years, many pieces of research have pointed out that clustering can improve the stability of the cluster by using similar mobility patterns of the vehicles. In this paper, we propose a new distributed mobility-based multi-hop clustering algorithm (DMMCA) in highway scenarios. DMMCA uses the vehicle's moving direction, relative speed and relative position to construct the multi-hop cluster. In addition, in order to reduce the overhead of the clustering algorithm, we design a new hello packet rebroadcast mode. Extensive simulation experiments are performed using ns-3 to demonstrate our proposed algorithm. The results show that our proposed algorithm can improve the performance of the cluster in terms of average cluster head duration, average cluster member duration, average state changes. Moreover, the number of rebroadcast hello packets is also reduced.
Most density-based clustering algorithms have the problems of difficult parameter setting, high time complexity, poor noise recognition, and weak clustering for datasets with uneven density. To solve these problems, t...
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Most density-based clustering algorithms have the problems of difficult parameter setting, high time complexity, poor noise recognition, and weak clustering for datasets with uneven density. To solve these problems, this paper proposes FOP-OPTICS algorithm (Finding of the Ordering Peaks Based on OPTICS), which is a substantial improvement of OPTICS (Ordering Points To Identify the clustering Structure). The proposed algorithm finds the demarcation point (DP) from the Augmented Cluster-Ordering generated by OPTICS and uses the reachability-distance of DP as the radius of neighborhood eps of its corresponding cluster. It overcomes the weakness of most algorithms in clustering datasets with uneven densities. By computing the distance of the k-nearest neighbor of each point, it reduces the time complexity of OPTICS;by calculating density-mutation points within the clusters, it can efficiently recognize noise. The experimental results show that FOP-OPTICS has the lowest time complexity, and outperforms other algorithms in parameter setting and noise recognition.
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.
This paper proposes an optimal clustering algorithm considering performance deviation of parameters and data preprocessing method for reusing retired batteries. The proposed method regroups batteries by considering th...
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ISBN:
(纸本)9781665475396
This paper proposes an optimal clustering algorithm considering performance deviation of parameters and data preprocessing method for reusing retired batteries. The proposed method regroups batteries by considering the density and performance deviation of the retired battery dataset through a clustering algorithm using density-based spatial clustering of applications with noise (DBSCAN). Additionally, the performance of the algorithm was improved through data preprocessing using a principal component analysis (PCA) that prevents the computational complexity and overfitting of clustering algorithm. The feasibility of the proposed algorithm is verified by comparing with general clustering algorithms such as the k-means clustering and Gaussian mixture model.
Energy limited has an important effect on underwater acoustic sensor network (UASN). It has been a key and primary issue to develop an effective algorithm for reducing energy consumption of UASN. In this paper, a new ...
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ISBN:
(纸本)9781467344999;9780769548814
Energy limited has an important effect on underwater acoustic sensor network (UASN). It has been a key and primary issue to develop an effective algorithm for reducing energy consumption of UASN. In this paper, a new clustering algorithm based on low-energy adaptive clustering hierarchy (LEACH) protocol is proposed. The algorithm combines the factor of the cluster head's position, meanwhile, it involves not only the energy consumption between member nodes and cluster heads but also the energy consumption between cluster heads and base station when the member nodes choose which cluster to join in. Simulation results show that the new algorithm can balance the size of the cluster and the node's energy consumption, and reduce the total energy consumption of the network effectively.
An integration algorithm for clustering is presented, in which a maximization & minimum algorithm to determine the initial centers and BWP (Between-Within Proportion) index for input of optimal k. In theory, the b...
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ISBN:
(纸本)9783038350064
An integration algorithm for clustering is presented, in which a maximization & minimum algorithm to determine the initial centers and BWP (Between-Within Proportion) index for input of optimal k. In theory, the bigger the BWP index, the better the clustering effectiveness. Then a numerical example of air transport market segment is presented to show the effectiveness and efficiency of the method presented in the document.
Most existing clustering algorithms can be only applied to homogeneous wireless sensor networks with uniform maximal transmission power. And cluster heads elected by some algorithms can not distribute uniformly under ...
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ISBN:
(纸本)9781424408276
Most existing clustering algorithms can be only applied to homogeneous wireless sensor networks with uniform maximal transmission power. And cluster heads elected by some algorithms can not distribute uniformly under heterogeneous networks environment where the maximal transmission power of each node may be different. So an energy-efficient clustering algorithm which based on virtual area partition (VAP-E) for heterogeneous wireless sensor networks is proposed in this paper. The simulation results show that VAP-E can balance the load between clusters, reduce energy consumption of sensor nodes, prolong the stability period and lifetime of networks, and improve the efficiency of communications obviously in wireless sensor networks compared with current clustering algorithms.
For the limited application and shortcoming of FCM (Fuzzy C-Means) clustering algorithm, an improved automatic FCM clustering algorithm is put forward. First, the fuzzy equivalent matrix is achieved by fuzzier the sta...
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ISBN:
(纸本)9781424469772
For the limited application and shortcoming of FCM (Fuzzy C-Means) clustering algorithm, an improved automatic FCM clustering algorithm is put forward. First, the fuzzy equivalent matrix is achieved by fuzzier the standard uniform data sets;then, the objective function of the improved automatic FCM clustering algorithm is optimized by the amendment of membership function and distance measuring function;The Lagrange multiplier optimization algorithm is calculated to update iteration of membership degree and clustering center. Finally, the automatic clustering is obtained by the degree of cohesion and separation. The traffic flow data of an extra long highway tunnel in Shaanxi is taken as an actual example to apply the improved automatic FCM clustering algorithm. The clustering result shows that the validity of clustering is improved using the improved automatic FCM algorithm.
Sleep episodes are generally classified according to EEG, EMG, ECG, EOG and other signals. Many experts at home and abroad put forward many automatic sleep staging classification methods, however the accuracy of most ...
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ISBN:
(纸本)9783319707723;9783319707716
Sleep episodes are generally classified according to EEG, EMG, ECG, EOG and other signals. Many experts at home and abroad put forward many automatic sleep staging classification methods, however the accuracy of most methods still remain to be improved. This paper firstly improves the initial center of clustering by combining the correlation coefficient and the correlation distance and uses the idea of piecewise function to update the clustering center. Based on the improvement of K-means clustering algorithm, an automatic sleep stage classification algorithm is proposed and is adopted after the wavelet denoising, EEG data feature extraction and spectrum analysis. The experimental results show that the classification accuracy is improved and the sleep automatic staging algorithm is effective by comparison between the experimental results with the artificial markers and the original algorithms.
In recent years, with the increasingly widespread application of unmanned aerial vehicle (UAV), the network technology of UAV has also caused for concern. In this paper, according to the background of related technolo...
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ISBN:
(纸本)9781467300407
In recent years, with the increasingly widespread application of unmanned aerial vehicle (UAV), the network technology of UAV has also caused for concern. In this paper, according to the background of related technologies of UAV, a mobility prediction clustering algorithm (MPCA) relying on the attributes of UAV is proposed. The dictionary Trie structure prediction algorithm and link expiration time mobility model are applied in this clustering algorithm to solve the difficulty of high mobility of UAV. The simulation shows that the reasonable clusterhead electing algorithm and on-demand cluster maintenance mechanism guarantee the stability of the cluster structure and the performance of the network.
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