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Hephaistos: A fast and distributed outlier detection approach for big mixed attribute data

INTELLIGENT DATA ANALYSIS 2019年第4期23卷 759-778页

作者： Du, Haizhou Fang, Wei Wang, Yi Shanghai Univ Elect Power Sch Comp Sci Shanghai Peoples R China State Grid Zhejiang Hangzhou Xiaoshan Power Suppl Hangzhou Zhejiang Peoples R China

This paper tackles a new problem in outlier detection: how to promptly detect the local outlier of a large-scale mixed attribute data in the big data era. This poses significant challenges due to a lack of access to the entire mixed attribute dataset at any individual compute machine. Proposed approaches firstly form a mechanism that deletes the massive clear non-noise and extracts cluster-based pre-noise set. Furthermore, we analyze pre-noise set using multi-step distributed LOF computing method on the Spark platform. Finally, the ordered LOF list is the output result. Comprehensive experiments are implemented by large-scale Benchmark datasets and the Spark platform. Extensive results show that the performance of our approaches are superior to the previous ones (4X faster than baseline LOF/2X faster than DLOF) when compared to state-of-the-art techniques, and therefore is believed to be able to give better guidance to local outlier detection of mixed attribute data.

关键词： Mixed attribute data clustering algorithm local outlier detection distributed framework Spark platform

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A Hybrid Approach for Detecting and Resolving Conflicts in Air Traffic Routes

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IEEE ACCESS 2019年 7卷 169865-169887页

作者： Lovato, Agnaldo, V Fontes, Cristiano H. Embirucu, Marcelo Kalid, Ricardo State Univ Southwest Bahia Dept Sci & Technol BR-45208091 Jequie Brazil Univ Fed Bahia Escola Politecn Programa Engn Ind BR-40210630 Salvador BA Brazil

This paper presents a hybrid model for the detection and resolution of conflicts in air traffic routes involving flight level change actions and adjustment of the longitudinal acceleration of aircraft. The strategy comprises an integrated approach that uses a fuzzy model to quantify the level of longitudinal conflict between two aircraft on the same airway. In addition, optimum flight level change actions between aircraft are calculated through a global and dynamic analysis involving the recognition of clusters of aircraft in conflict and the search for the best scenario by means of a genetic algorithm that minimizes the sum of positive conflicts. The results show that the proposed approach is able to detect and remove longitudinal conflicts in advance, providing a potential tool to support decision-making, improve safety and optimize the use of airspace.

关键词： Aircraft Air traffic control Atmospheric modeling Genetic algorithms Acceleration Decision making Air traffic control fuzzy logic genetic algorithm optimization clustering algorithm support decision-making

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Dynamic cluster algorithm for improving percolation of targets in a sensor network (DC-AIPT)

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EGYPTIAN INFORMATICS JOURNAL 2019年第3期20卷 179-191页

作者： Toumi, Mohamed Maizate, Abderrahim Ouzzif, Mohammed Univ Hassan 2 RITM ESTC CED ENSEM Casablanca Morocco

A target tracking in wireless sensor networks consists of two main functions: The detection and the tracking of the target along its trajectory by means of sensors deployed in an area of interest. Generally, these sensors are not maintainable after deployments. Dynamic clustering algorithms seem to be an effective mechanism for increasing the network's lifetime. Indeed, this type of algorithms only activates the nodes that are on the trajectory of the target when the latter is at their reach. All other sensors must be in sleep mode. The effectiveness of a monitoring solution must take into account the quality of monitoring, connectivity, and the power consumption that are directly affected by the distribution and density of the nodes. We propose to construct optimal dynamic clusters on the target trajectory based on a probabilistic model integrating two fundamental parameters: energy consumption and accuracy. This last metric is evaluated, for the first time in the target tracking algorithms, by the notion percolation. (C) 2019 Production and hosting by Elsevier B.V. on behalf of Faculty of Computers and Information, Cairo University.

关键词： clustering algorithm Percolation Wireless sensor networks Target tracking Dynamic clustering

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Secure Multi-UAV Collaborative Task Allocation

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IEEE ACCESS 2019年 7卷 35579-35587页

作者： Fu, Zhangjie Mao, Yuanhang He, Daojing Yu, Jingnan Xie, Guowu Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing 210044 Jiangsu Peoples R China East China Normal Univ Software Engn Inst Shanghai 200062 Peoples R China Univ Calif Riverside Dept Comp Sci & Engn Riverside CA 92521 USA

Unmanned aerial vehicle technology has made great progress in the past and is widely used in many fields. However, they are unable to meet large-scale and complex missions with a limited energy reserve. Only multiple unmanned aerial vehicles (multi-UAV) work together to better cope with this problem and have been extensively studied. In this paper, a new systematic framework is proposed to solve the problem of multi-UAVcollaborative task allocation. It is formulated as a combinatorial optimization problem and solved by the improved clustering algorithm. The purpose is to enable multi-UAV to complete tasks with lower energy consumption. As the number of UAVs rises, it also appears the flight safety issues such as collisions among the UAVs, an improved multi-UAV collision-resistant method based on the improved artificial potential field is proposed. Besides, the UAVs connected with the internet are vulnerable to the various type of network attacks, a method based on the intrusion detection system is proposed to resist the network attack during multi-UAV mission execution. We have also proposed an improved method to improve the accuracy of task allocation further. In addition, an online real-time path planning is proposed to enhance the robustness of multi-UAV to cope with sudden problems. Finally, the numerical simulations and real physical flying experiments showed that the proposed method could provide a viable solution for multi-UAV task allocation;moreover, compared with other task allocation methods, our method has great performance.

关键词： Multi-UAV task allocation collusion-resistant secure communication intrusion detection system clustering algorithm

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Study on application of data mining technology in university computer network educational administration management system

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019年第3期37卷 3311-3318页

作者： Sun, Hongxia Nantong Univ Xinglin Coll Nantong Jiangsu Peoples R China

0University educational administration management system is one of the core tasks of digital campus. Data mining is a technology that taps potential information from a large amount of data according to a specific algorithm for researchers to analyze. The experimental comparison between the improved algorithm and the unmodified algorithm shows that the improved algorithm has better performance and can improve the convergence speed of the clustering and the accuracy of the clustering results. The improved algorithm is applied to the mining of student achievement evaluation. Finally, according to the comparison of the results of the traditional rating criteria with the dynamic rating evaluation results, the results confirm the rationality and feasibility of the management of college computer network education according to the clustering algorithm. According to the cluster analysis of these two models, it shows that it is meaningful to introduce data mining into the management of college computer network education administration.

关键词： Data mining clustering algorithm teaching management

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A Cluster-Based Broadcast Scheduling Scheme for mmWave Vehicular Communication

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IEEE COMMUNICATIONS LETTERS 2019年第7期23卷 1202-1206页

作者： Zhang, Xinming Li, Yongzhen Miao, Qingliang Univ Sci & Technol China Sch Comp Sci & Technol Hefei 230027 Anhui Peoples R China Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Jiangsu Peoples R China

The development of mmWave vehicular communication helps many use cases of interest requiring higher throughput. However, the high attenuation of mmWave makes it essential to avoid the block of other vehicles and weaken the interference on concurrent mmWave links. In this letter, we first propose a hierarchical multi-hop clustering algorithm considering velocity, relative position, and the proportion of line-of-sight (LOS) neighbors to maintain the stability of the cluster head. Then, we propose a link availability prediction strategy to ensure that the mmWave links among vehicles are LOS as far as possible. Finally, we propose a broadcast scheduling scheme based on the cluster utilizing exclusive region to improve concurrency of mmWave links. The simulation results reflect that our scheme achieves higher throughput and average packets' delivery ratio for a varying number of vehicles and bandwidths of sub-6 GHz.

关键词： Broadcast scheduling clustering algorithm link availability prediction mmWave vehicular communication

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A Survey on Cluster-Based Routing Protocols for Unmanned Aerial Vehicle Networks

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IEEE ACCESS 2019年 7卷 498-516页

作者： Arafat, Muhammad Yeasir Moh, Sangman Chosun Univ Dept Comp Engn Gwangju 61452 South Korea

In recent years, unmanned aerial vehicles (UAVs) have gained popularity for various applications and services in both the military and civilian domains. Multiple UAVs can carry out complex tasks efficiently when they are organized as an ad hoc network, where wireless communication is essential for cooperation and collaboration between UAVs and the ground station. Due to rapid mobility and highly dynamic topology, designing a routing protocol for UAV networks is a challenging task. As the number of UAVs increases, a hierarchical routing called clustering is necessarily required to provide scalability because clustering schemes ensure the basic level of system performance such as throughput, end-to-end delay, and energy efficiency. For approximately a half-decade, several survey articles have been reported on topology-based routing and position-based routing for UAV networks. To the best of the authors' knowledge, however, there is no survey on cluster-based routing in the literature. In this paper, cluster-based routing protocols for UAV networks are extensively surveyed and qualitatively compared in terms of outstanding features, characteristics, competitive advantages, and limitations. Furthermore, open research issues and challenges on cluster-based routing are discussed.

关键词： Unmanned aerial vehicle drone unmanned aerial vehicle network flying ad hoc network routing protocol clustering algorithm scalability

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A clustering algorithm for Heterogeneous Sensor Networks with Multilevel Energies

A Clustering Algorithm for Heterogeneous Sensor Networks wit...

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32nd Chinese Control Conference (CCC)

作者： Zhang Ying Ji Chang-gang Li Jun-fu Shanghai Maritime Univ Coll Informat Engn Shanghai 201306 Peoples R China

ISBN: (纸本)9781479900305

Currently, most clustering algorithms are proposed for homogeneous network, which do not adapt to the heterogeneous network. Energy heterogeneity is ubiquitous in wireless sensor networks, because node initial energy of multi-level energy-heterogeneous wireless sensor network distributes within the scope of certain and randomly, in order to economize heterogeneous nodes energy and extend network stability period, this paper proposed an routing algorithm for multi-level energy-heterogeneous sensor networks which take into account the residual energy of nodes and node-to-base distance multi-stage energy. Resident energy of cluster head and the energy consumption of its base station are considered synthetically between clusters to select the appropriate next-hop route node. Simulation results show that this algorithm can balance network energy consumption efficiently and extend network stability period.

关键词： Heterogeneous Wireless Sensor Network Multi-level Energy clustering algorithm Energy-efficient

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A Bio-Inspired Solution to Cluster-Based Distributed Spectrum Allocation in High-Density Cognitive Internet of Things

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IEEE INTERNET OF THINGS JOURNAL 2019年第6期6卷 9294-9307页

作者： Li, Jiaxun Zhao, Haitao Hafid, Abdelhakim Senhaji Wei, Jibo Yin, Hao Ren, Baoquan Natl Univ Def Technol Coll Elect Sci & Engn Changsha 410073 Hunan Peoples R China Univ Montreal Dept Comp Sci & Operat Res Montreal PQ H3C 3J7 Canada Acad Mil Med Sci Inst Syst Engn Beijing 100091 Peoples R China

With the emergence of Internet of Things (IoT), where any device is able to connect to the Internet and monitor/control physical elements, several applications were made possible, such as smart cities, smart health care, and smart transportation. The wide range of the requirements of these applications drives traditional IoT to cognitive IoT (CIoT) that supports smart resource allocation, automatic network operation and intelligent service provisioning. To enable CIoT, there is a need for flexible and reliable wireless communication. In this paper, we propose to combine cognitive radio (CR) with a biological mechanism called reaction-diffusion to provide efficient spectrum allocation for CIoT. We first formulate the quantization of qualitative connectivity-flexibility tradeoff problem to determine the optimal cluster size (i.e., number of cluster members) that maximizes clustered throughput but minimizes communication delay. Then, we propose a bio-inspired algorithm which is used by CIoT devices to form cluster distributedly. We compute the optimal values of the algorithm's parameters (e.g., contention window) of the proposed algorithm to increase the network's adaption to different scenarios (e.g., spectrum homogeneity and heterogeneity) and to decrease convergence time, communication overhead, and computation complexity. We conduct a theoretical analysis to validate the correctness and effectiveness of proposed bio-inspired algorithm. Simulation results show that the proposed algorithm can achieve excellent clustering performance in different scenarios.

关键词： Bio-inspired solution clustering algorithm cognitive Internet of Things (CIoT) cognitive radio (CR) spectrum allocation

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A methodology to develop a geospatial transportation typology

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JOURNAL OF TRANSPORT GEOGRAPHY 2021年 93卷

作者： Popovich, Natalie Spurlock, C. Anna Needell, Zachary Jin, Ling Wenzel, Tom Sheppard, Colin Asudegi, Mona Lawrence Berkeley Natl Lab 1 Cyclotron Rd Berkeley CA 94720 USA Fed Highway Adm 1200 New Jersey Ave SE Washington DC 20590 USA

We introduce a methodology to develop a geo-typology (geotype) that categorizes each location in the United States in terms of their main drivers of transportation demand and supply. We develop the first comprehensive set of geotypes for both urban and rural areas across the entire United States. This typology is designed to facilitate national level modeling of multi-modal transportation system's response to alternative investment strategies differentiated across different types of locations. We develop a two-stage clustering procedure to systematically and quantitatively characterize the ways in which locations across the nation are similar or different with respect to their potential response to investment strategies of interest. First, we cluster all 73,057 census tracts, using factor analysis and the CLARA clustering algorithm into "microtypes" based on their street network and economic characteristics. Then we cluster regions (core-basic statistical areas and counties) into "geotypes" using PAM clustering according to their commute configurations, polycentricity and density. The resulting set captures both local and regional variation. These microtypes and geotypes are comparable across all locations, enabling a national level perspective, while maintaining sufficient heterogeneity to support a variety of transportation analyses capturing critical geographic variation.

关键词： Geo-spatial Neighborhood Typology Geotype Transportation investment Machine learning clustering algorithm

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