An Integrated Affinity Propagation and Machine Learning Approach for Interference Management in Drone Base Stations

In this paper, we develop a comprehensive statistical framework to characterize and model large-scale unmanned aerial vehicle-enabled post-disaster recovery cellular networks. In the case of natural or man-made disasters, the cellular network is vulnerable to destruction resulting in coverage voids or coverage holes. Drone-based small cellular networks (DSCNs) can be rapidly deployed to fill such coverage voids. Due to capacity and back-hauling limitations on drone small cells (DSCs), each coverage hole requires a multitude of DSCs to meet the shortfall coverage at a desired quality-of-service. Moreover, ground users also tend to cluster in hot-spots in a post-disaster scenario. Motivated by this fact, we consider the clustered deployment of DSCs around the site of a destroyed BS. Joint consideration partially operating BSs and deployed DSCs yields a unique topology for such public safety networks. Borrowing tools from stochastic geometry, we develop a statistical framework to quantify the down-link performance of a DSCN. Our proposed clustering mechanism extends the traditional Matern and Thomas cluster processes to a more general case, where cluster size is dependent upon the size of the coverage hole. We then employ the newly developed framework to find closed-form expressions (later verified by Monte-Carlo simulations) to quantify the coverage probability, area spectral efficiency, and the energy efficiency for the down-link mobile user. Finally, we explore several design parameters (for both of the adopted cluster processes) that address optimal deployment of the network (i.e., number of drones per cluster, drone altitudes, and transmit power ratio between the traditional surviving base stations and the drone base stations).

Spectrum sharing is a promising technology that improves the spectral utilization efficiency and provides the foundation stone to build up future drone cellular networks. This chapter presents an efficient transmission scheme and to optimally allocate resources in an underlay spectrum sharing between drone networks and cellular networks. Taking advantage of the stochastic geometry, the 3D blockage effect by random shape theory is determined. Under a blockage model with volume, the exact expression of the signal‐to‐noise ratio (SNR) meta‐distribution is derived. The chapter focuses on the deployment of aerial base stations in drone small cells (DSCs) while sharing the spectrum with cellular networks. Starting with a consideration of the spectrum sharing of single‐tier DSCs in a 3D network, the optimal DSC base station density by maximizing the DSC network throughput is derived. UAVs can act as aerial base stations or relays, namely DSCs, to provide communication services to areas after natural disasters.

In this study, a new intelligent fault diagnosis approach based on composite multi-scale dimensionless indicators (CMDIs) and affinity propagation (AP) clustering is proposed to identify working conditions of mechanical components. For this goal, CMDIs as fault features are for the first time proposed to search more informative fault information hidden in the original vibration signals and useful variational mode decomposition signals. Next, two feature evaluation methods, Fisher criterion (FC) and self-weight criterion (SW), are introduced to search the top ranked CMDIs, respectively. Subsequently, AP clustering is applied to select the important CMDIs. The most important CMDI of each cluster is chosen as the final fault feature. Finally, these important CMDIs are fed into the following supervised learning classifiers to identify working conditions of mechanical components: k-nearest neighbors, linear discriminant analysis, naive Bayesian and random forests. Two experimental examples for different fault types of mechanical components are conducted. Experimental comparison results demonstrate that the combination diagnosis model based on CMDIs-FC-AP with naive Bayesian classifier would be most suitable for complex samples with a high dimensional feature set.

In the field of mechanical fault diagnosis, in order to improve the diagnosis accuracy with high feature dimension, a method of features linear fusion method based on AP(Affinity propagation clustering) and AASC(Affinity Aggregation for Spectral Clustering) algorithm was proposed. Low dimension space was obtained by AP clustering, then using AASC algorithm to fuse this selected features, and gained a new feature, meanwhile which can be used independently as a new feature. In the phase of experiment, this method was compared with respectively LPP and PCA, it’s result indicates that this method is excellent in improving diagnosis accuracy.

Yellow jadeite is a kind of jadeite caused by secondary leaches. With a certain degree of transparency, its color is usually influenced by the objective conditions including light source and background. In this paper, 19 Munsell neutral value scales were selected to study the impact caused by background under the standard light source D65, where we found that the N9.5 Value Scale is suitable for color grading. All 33 yellow jadeites were tested by UV-VIS spectrophotometer and colorimetric analysis, and the colors were graded by the affinity propagation (AP) and K-means clustering methods. According to the result, the yellow jadeite color was classified into Fancy, Fancy Intense, Fancy Deep, and Fancy Light. It is also concluded that AP is more feasible to provide the color grading scheme, while K-means can be applied to the optimization of choosing clustering center.

The data-driven characterization of discretionary lane-changing behaviors has traditionally been hindered by the scarcity of high-resolution data that can precisely record lateral movements. In this study, we conducted an exploratory investigation leveraging the Third Generation Simulation (TGSIM) dataset to advance our understanding of discretionary lane-changing behaviors. In this paper, we developed a discretionary lane-changing extraction pipeline and scrutinized crucial factors such as gaps and relative speeds in leading and following directions. A dynamic time warping (DTW) analysis was performed to quantify the difference between any pair of lane-changing behaviors, and an affinity propagation (AP) clustering, evaluated on normalized DTW distance, was conducted. Our results yielded five clusters based on lead and lag gaps, enabling us to categorize lane-changing behaviors into aggressive, neutral, and cautious for both leading and following directions. Clustering based on relative speeds revealed two distinct groups of lane-changing behaviors, one representing overtaking and the other indicative of transitioning into a lane with stable and homogenous speed. The proposed DTW analysis, in conjunction with AP clustering, demonstrated promising potential in categorizing and characterizing lane-changing behaviors. Additionally, this approach can be readily adapted to analyze any driving behavior.

With the fast development of internet of things (IoT), a large amount of missing data is produced in the process of data collection and transmission. We call these data incomplete data. Many traditional methods use imputation or discarding strategy to cluster incomplete data. In this paper, we propose an improved incomplete affinity propagation (AP) clustering algorithm based on K nearest neighbours (IAPKNN). IAPKNN firstly partitions the dataset into complete and incomplete dataset, and then clusters the complete data set by AP clustering directly. Secondly, according to the similarity, IAPKNN extends the responsibility and availability matrices to the incomplete dataset. Finally, clustering algorithm is restarted based on the extended matrices. In addition, to address the clustering efficiency of large scale dataset, we give a distributed clustering algorithm scheme. Experiment results demonstrate that IAPKNN is effective in clustering incomplete data directly.

Automatic mode tracing of dispersion relations for guided waves in elastic waveguides via physics-driven affinity propagation (AP) clustering

Vehicular Communication Networks is a subcategory of Mobile Communications Networks that has the special characteristics of high node mobility and fast topology changes. In the current chapter, the authors outline the basic characteristics and concepts of vehicular communications and present the standardization and network deployment efforts carried out by the scientific community. In particular, they focus their attention on the vehicle-to-infrastructure component of the network; moreover, the authors specifically investigate security, quality of service, and routing, which constitute three of the most challenging aspects in the field of Vehicular Networks. The authors further examine the ways that infrastructure can provide efficient solutions to the problems that exist for each respective category and review several proposed solutions.

Vehicular Ad Hoc Networks is a class of special wireless ad hoc network with the characteristics of high node mobility and fast topology changes. The Vehicular Networks can provide wide variety of services, range from safety-related warning systems to improved navigation mechanisms as well as information and entertainment applications. So lot of research work is being conducted to study the problems related to the vehicular communications including network architecture, protocols, routing algorithms, as well as security issues. In order to stimulate the ‘beginners in research’, here we present a paper on an overview of Vehicular Ad Hoc Networks.

Vehicular plays an important role in day-to-day world in the field of automobiles. VANETs are the subclass of mobile ad-hoc Networks which have no central infrastructure. Nodes are highly mobile in VANET. VANET is distinguished from MANET by their features, design and applications. A Vehicular Ad-Hoc Network or VANET is a sub form of Mobile Ad-Hoc Network or MANET that provides communication between vehicles and between vehicles and road-side base stations with an aim of providing efficient and safe transportation. A vehicle in VANET is considered to be an intelligent mobile node capable of communicating with its neighbors and other vehicles in the network. VANET introduces more challenges aspects as compare to MANET because of high mobility of nodes and fast topology changes in VANET. In this paper, VANET basics structure, its architecture, challenges, applications are discussed. Data dissemination is a challenging task because by utilizing limited bandwidth, maximum data has to disseminate over the vehicular network. Many researchers have provided several techniques to disseminate data so that the data can be accessed more efficiently. Major stress is given to this topic where data dissemination types and its protocols are compared.

Classifying organometallic reactions into distinct reaction types is fundamentally important for understanding mechanisms and predicting reactions, for synthesis optimisation. Fundamental to classification of organometal reactions is reaction representation, but current methods often fail to capture organometal chemical transformation adequately. The study has adopted a hybrid fingerprinting approach, whereby new fingerprints were concatenated with permutation important Morgan fingerprints to create 49 to 63 bits fingerprints. The hybrid fingerprints were used to build KMeans clustering, Guassian mixtures, Affinity propagation and Heirarchical clustering models for classification of organometal reactions. The models successfully classified reactions into 6-8 major organometal reaction types. Of note, the fingerprints consistently outperformed Morgan fingerprints across all clustering models with good Davies–Bouldin Index (DBI) ranging from 0.3 to 0.6 and Silhouette score from 0.3 to 0.8. Furthermore, the clustering models were visualized using Principal Component Analysis. Affinity Propagation and KMeans demonstrated superior performance over Hierarchical and GMM algorithms in distinguishing major reaction categories. In contrast, the Hierarchical model excelled at identifying sub-level classifications compared to the other methods. Consequently, Affinity Propagation and KMeans are recommended for broad reaction type classification, while the Hierarchical approach is better suited for resolving detailed subclass distinctions. The observed variability in model performance further highlighted the importance of feature selection and representation in clustering models. Future studies should look into improving the fingerprints to recognize subcategories.

Affinity propagation (AP) algorithm, as a novel clustering method, does not require the users to specify the initial cluster centers in advance, which regards all data points as potential exemplars (cluster centers) equally and groups the clusters totally by the similar degree among the data points. But in many cases there exist some different intensive areas within the same data set, which means that the data set does not distribute homogeneously. In such situation the AP algorithm cannot group the data points into ideal clusters. In this paper, we proposed an extended AP clustering algorithm to deal with such a problem. There are two steps in our method: firstly the data set is partitioned into several data density types according to the nearest distances of each data point; and then the AP clustering method is, respectively, used to group the data points into clusters in each data density type. Two experiments are carried out to evaluate the performance of our algorithm: one utilizes an artificial data set and the other uses a real seismic data set. The experiment results show that groups are obtained more accurately by our algorithm than OPTICS and AP clustering algorithm itself.

We firstly present an image segmentation method based on affinity propagation clustering which needs not to initialize cluster centers and is more reliable than traditional clustering methods such as K-Means clustering and so on. However, it is very difficult to get good image segmentation results through adjusting the only parameter ¿preference¿ of affinity propagation clustering, and sometimes the segmentation results don't accord with human vision properties. To tackle the two problems, we propose two similarity measure methods based on human vision properties for measuring the similarities between pairs of data points of an image. The experiment results show that compared with the traditional Euclidean distance, the two similarities proposed can lower the level of difficulty of selecting parameters and make the segmentation results more according with human vision properties.

For large-scale data sets, the traditional AP clustering algorithm processes all data samples through the idea of iteration and three metrics calculation. This kind of processing will lead to poor clustering effect and high time consumption. Based on this problem, an algorithm which is called KCAP based on Affinity Propagation clustering and Canopy clustering algorithm was proposed. Firstly, KNN distance was used to calculate the local density for each data. Secondly, we used the Canopy algorithm to roughly cluster the data sets, and the center points were formed into a new data set \(Y\). Then, we applied affinity propagation clustering algorithm to cluster the data set \(Y\). Finally, we assigned the data to the nearest center. Compared with the traditional AP algorithm, K-means algorithm and FCM algorithm, the experimental results on synthetic data sets and UCI data sets show that algorithm KCAP not only improves the clustering accuracy, but also have higher efficiency.KeywordsK-Nearest Neighbors (KNN)Initial centerCanopy algorithmAffinity Propagation (AP)Rough clustering