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A Hybrid Wormhole Attack Detection in Mobile Ad-Hoc Network (MANET)

IEEE ACCESS 2021年 9卷 11872-11883页

作者： Tahboush, Muhannad Agoyi, Mary Cyprus Int Univ Dept Comp Engn 10 Mersin Nicosia Turkey Cyprus Int Univ Sch Appl Sci Informat Technol Dept TR-99258 Nicosia Turkey

Mobile Ad-hoc Networks (MANET) are decentralized wireless networks that communicate without pre-existing infrastructure. MANETs are vulnerable to the most popular types of attacks and threats, such as wormhole attacks. A wormhole attacks is very challenging issues that records the packets from one location of the network and tunnels them to another location to undermines the performance of the wireless network and disrupt the most routing protocol. However, the existing solutions have been developed to overcome the wormhole attack, but still suffering from additional hardware, incur high delay delivery, or fail to provide high throughput, packet delivery ratio as well as consume higher energy. In this paper a hybrid wormhole attack detection (HWAD) algorithm is proposed, which is able to detect both in-band wormholes through performs round trip time (RTT) based on its hop count, and packet delivery ratio (PDR), also out-of-band wormholes through performs transmission range between successive nodes in a more optimistic manner than existing solutions. HWAD reduce the delay and energy through avoids performing wormhole detections for all available nodes in the network. HWAD does not rely on any special hardware and middleware. The proposed algorithm HWAD was executed using NS-2 network simulator. The performance metrics was taking into consideration to evaluate the performance of the proposed algorithm the throughput, end to end delay, packet delivery ratio, and consuming energy. The proposed algorithm utilized Ad-hoc On-Demand Distance Vector (AODV) routing protocol to improve the detection method. The experimental results have shown the performance metrics of the proposed approach HWAD outperformed in wormhole detection compared with other algorithms.

关键词： Mobile ad hoc networks Routing protocols Delays Wireless networks clustering algorithms Throughput Hardware Wormhole attack malicious node legitimate node AODV MANET

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Moving Object Detection by 3D Flow Field Analysis

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2021年第4期22卷 1950-1963页

作者： Jiang, Cansen Paudel, Danda Pani Fofi, David Fougerolle, Yohan Demonceaux, Cedric Univ Bourgogne Franche Comte ImViA VIBOT EMR CNRS 6000 F-71200 Le Creusot France Swiss Fed Inst Technol Comp Vis Lab CH-8092 Zurich Switzerland

Map-based localization and sensing are one of the key components in autonomous driving technologies, where high quality 3D map reconstruction is fundamentally utmost important. However, due to the highly dynamic and uncontrollable properties of real world environment, building a high quality 3D map is not straightforward and requires several strong assumptions. To address this challenge, we present a complete framework, which detects and extracts the moving objects from a sequence of unordered and texture-less point clouds, to build high quality static maps. To accurately detect the moving objects from data acquired with a possibly fast moving platform, we propose a novel 3D Flow Field Analysis approach in which we inspect the motion behaviour of the registered point sets. The proposed algorithm elegantly models the temporal and spatial displacement of the moving objects. Thus, both small moving objects (e.g. walking pedestrians) and large moving objects (e.g. moving trucks) can be detected effectively. Further, by incorporating the Sparse Subspace clustering framework, we propose a Sparse Flow clustering algorithm to group the 3D motion flows under both the constraints of motion similarity and spatial closeness. To this end, the static scene parts and the moving objects can be independently processed to achieve photo-realistic 3D reconstructions. Finally, we show that the proposed 3D Flow Field Analysis algorithm and the Sparse Flow clustering approach are highly effective for motion detection and segmentation, as exemplified on the KITTI benchmark, and yield high quality reconstructed static-maps as well as rigidly moving objects.

关键词： Three-dimensional displays Trajectory clustering algorithms Motion segmentation Image reconstruction Dynamics Tracking Motion flow detection motion segmentation dynamic scene analysis 3D map reconstruction

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Multiple Kernel k-Means With Low-Rank Neighborhood Kernel

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IEEE ACCESS 2021年 9卷 3291-3300页

作者： Ou, Qiyuan Gao, Long Zhu, En Natl Univ Def Technol Sch Comp Changsha 410073 Peoples R China

Multiple kernel clustering algorithms achieve promising performances by exploring the complementary information from kernel matrices corresponding to each data view. Most of the existing methods aim to construct a consensus kernel for the afterward clustering. However, they ignore that the desired kernel is supposed to reveal the cluster structure among samples and thus to be low rank. As a consequence, the corresponding clustering performance could decrease. To address this issue, we propose a low-rank kernel learning approach for multiple kernel clustering. Specifically, instead of regularizing the consensus kernel with low-rank constraints, we use a re-parameterize scheme for the kernel matrix. Meanwhile, the consensus kernel is located in the neighborhood area of the linear combination of base kernels. An alternate optimization strategy is designed to solve the resulting optimization problem. We evaluate the proposed method on 13 benchmark datasets with 9 state-of-the-art algorithms. As is demonstrated by experimental results, our proposed algorithm achieves superior clustering scores against the compared algorithms on the reported popular multiple kernel datasets.

关键词： Kernel clustering algorithms Optimization Task analysis Sparse matrices Matrices Licenses clustering algorithms multiple kernel clustering multiple view clustering

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Personalized Control Strategy of Electronic Brake Booster With Driving Behaviors Identification

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2021年第12期70卷 12593-12603页

作者： Zhu, Bing Zhang, Yihan Zhao, Jian Tian, Haijian Wu, Jian Jilin Univ State Key Lab Automot Simulat & Control Changchun 130012 Peoples R China Jilin Univ Coll Automot Engn Changchun 130012 Peoples R China

To improve the comfort and user acceptance of the intelligent vehicle braking system, this paper proposes a control strategy for the electronic brake booster (Ebooster) that considers differences in driving behaviors. First, a real-vehicle driving data collection platform is built to collect the driving behavior data of 84 drivers under typical vehicle-following braking conditions. Subsequently, the k-means clustering algorithm optimized by particle swarm optimization (PSO) is used to realize the cluster analysis of driving behaviors, and the support vector machine (SVM) algorithm optimized by the genetic algorithm (GA) is used to complete the identification of driving behaviors. Next, through the analysis of the boost characteristics of the self-designed Ebooster, driving behaviors are matched with boost characteristics, and a personalized basic boosting control strategy based on the driver behavior is designed. Finally, a hardware-in-the-loop (HiL) bench and a real-vehicle test platform are designed and built to verify the effectiveness of the personalized control strategy of the Ebooster. The results show that this method can effectively identify the type of driver and make personalized adjustments to the basic boosting strategy of the Ebooster, which effectively improves the personalization of the braking system, resulting in a higher pedal response and user acceptance.

关键词： Vehicles Brakes Hidden Markov models Support vector machines Genetic algorithms Data acquisition clustering algorithms Intelligent vehicle personalized electronic brake booster driving behaviors identification support vector machine genetic algorithm

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Improved Analysis of Online Balanced clustering 19th

Improved Analysis of Online Balanced Clustering

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19th International Workshop on Approximation and Online algorithms, WAOA 2021

作者： Bienkowski, Marcin Böhm, Martin Koutecký, Martin Rothvoß, Thomas Sgall, Jiří Veselý, Pavel Institute of Computer Science University of Wroclaw Wroclaw Poland Computer Science Institute of Charles University Faculty of Mathematics and Physics Prague Czech Republic University of Washington Seattle United States

ISBN: (纸本)9783030927011

In the online balanced graph repartitioning problem, one has to maintain a clustering of n nodes into clusters, each having k= n/ nodes. During runtime, an online algorithm is given a stream of communication requests between pairs of nodes: an inter-cluster communication costs one unit, while the intra-cluster communication is free. An algorithm can change the clustering, paying unit cost for each moved node. This natural problem admits a simple O(2· k2) -competitive algorithm Comp, whose performance is far apart from the best known lower bound of Ω(· k). One of open questions is whether the dependency on can be made linear;this question is of practical importance as in the typical datacenter application where virtual machines are clustered on physical servers, is of several orders of magnitude larger than k. We answer this question affirmatively, proving that a simple modification of Comp is (· 2O(k)) -competitive. On the technical level, we achieve our bound by translating the problem to a system of linear integer equations and using Graver bases to show the existence of a "small" solution. © 2021, Springer Nature Switzerland AG.

关键词： clustering algorithms

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Hyperspectral Restoration and Fusion With Multispectral Imagery via Low-Rank Tensor-Approximation

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2021年第9期59卷 7817-7830页

作者： Liu, Na Li, Lu Li, Wei Tao, Ran Fowler, James E. Chanussot, Jocelyn Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China Univ Grenoble Alpes F-38000 Grenoble France Beijing Informat Sci & Technol Univ Sch Automat Beijing 100192 Peoples R China Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Beijing Key Lab Fract Signals & Syst Beijing 100081 Peoples R China Mississippi State Univ Dept Elect & Comp Engn Starkville MS 39762 USA Univ Grenoble Alpes Grenoble INP INRIA LJKCNRS F-38000 Grenoble France Chinese Acad Sci Aerosp Informat Res Inst Beijing 100094 Peoples R China

Tensor-based fusion that couples the high spatial resolution of a multispectral image (MSI) to the high spectral resolution of a hyperspectral image (HSI) is considered. The fusion problem is first formulated mathematically as a convex optimization of a tensor trace norm imposing low-rank spatially as well as spectrally, with an alternating-directions optimization featuring linearization providing the solution. Although prior tensor-based fusion approaches typically resort to tensor decomposition, the proposed algorithm exploits ideas from the field of tensor completion to directly impose a low-rank property spatially and spectrally while avoiding the computationally complex patch clustering and dictionary learning common to competing fusion techniques. Additionally, small modifications to the basic optimization permit a fusion process robust to missing hyperspectral values such as those that can result from dead stripes in real hyperspectral sensors. The experimental evaluations on both synthetic imagery as well as real imagery demonstrate that the resulting low-rank tensor-approximation (LRTA) fusion algorithm preserves both spatial details and texture, yielding significantly improved image quality when compared to other state-of-the-art fusion methods as well as effective restoration under conditions of missing stripes within the HSI.

关键词： Tensors Spatial resolution Degradation Hyperspectral imaging Image restoration Interpolation clustering algorithms Data fusion hyperspectral imagery (HSI) low-rank tensor

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A Dynamic Adaptive Subgroup-to-Subgroup Compatibility-Based Conflict Detection and Resolution Model for Multicriteria Large-Scale Group Decision Making

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IEEE TRANSACTIONS ON CYBERNETICS 2021年第10期51卷 4784-4795页

作者： Tang, Ming Liao, Huchang Herrera-Viedma, Enrique Chen, C. L. Philip Pedrycz, Witold Sichuan Univ Business Sch Chengdu 610064 Peoples R China King Abdulaziz Univ Fac Comp & Informat Technol Jeddah Saudi Arabia Univ Granada Andalusian Res Inst Data Sci & Computat Intellige Granada 18071 Spain King Abdulaziz Univ Fac Engn Dept Elect & Comp Engn Jeddah 21589 Saudi Arabia Univ Macau Fac Sci & Technol Dept Comp & Informat Sci Macau Peoples R China Dalian Maritime Univ Nav Coll Dalian 116026 Peoples R China Northwestern Polytech Univ Unmanned Syst Res Inst Xian 710072 Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 2R3 Canada

The current societal demands and technological developments have resulted in the participation of a large number of experts in making decisions as a group. Conflicts are imminent in groups and conflict management is complex and necessary especially in a large group. However, there are few studies that quantitatively research the conflict detection and resolution in the large-group context, especially in the multicriteria large-group decision making (GDM) context. This article proposes a dynamic adaptive subgroup-to-subgroup conflict model to solve multicriteria large-scale GDM problems. A compatibility index is proposed based on two kinds of conflicts among experts: 1) cognitive conflict and 2) interest conflict. Then, the fuzzy c-means clustering algorithm is used to classify experts into several subgroups. A subgroup-to-subgroup conflict detection method and a weight-determination approach are developed based on the clustering results. Afterward, a conflict resolution model, which can dynamically generate feedback suggestion, is introduced. Finally, an illustrative example is provided to demonstrate the effectiveness and applicability of the proposed model.

关键词： Decision making Indexes Adaptation models Cybernetics Classification algorithms clustering algorithms Compatibility index conflict fuzzy c-means clustering (FCM) multicriteria large-scale group decision making (LSGDM) subgroup-to-subgroup

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Iterative Local-Search Heuristic for Weighted Vehicle Routing Problem

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2021年第6期22卷 3444-3454页

作者： Wang, Xinyu Shao, Shuai Tang, Jiafu Dongbei Univ Finance & Econ Sch Management Sci & Engn Dalian 116025 Peoples R China

Solutions to the weighted vehicle routing problem (WVRP) find numerous applications, such as home-to-work bus service, toll-by-weight cargo transportation, perishable-food delivery, and hazardous waste collection. This paper presents mathematical WVRP models corresponding to both collection and delivery cases. This study demonstrates WVRPs to be NP-hard, and proposes an efficient heuristic method (RI-ILS) to solve WVRPs. RI-ILS is based on principles of regret-insertion and iterative local-search. Several computational experiments were performed in this study to demonstrate the utility of the RI-ILS approach. As observed, when solving traditional vehicle routing problems (VRPs), RI-ILS yields good results under all testing conditions. In particular, it provides new best-known solutions under 12 testing conditions. When solving WVRPs, the RI-ILS method outperforms two recently published state-of-the-art algorithms in terms of solution quality, computational time, and stability. Case studies have also been performed in this research based on real-world data obtained from two Chinese companies that provide home-to-work bus service to their employees.

关键词： Linear programming Routing Transportation Vehicle routing clustering algorithms Mathematical model Weighted vehicle routing problem (WVRP) home-to-work bus service collection and delivery cases regret-insertion iterative local-search

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Research on motion recognition algorithm based on bag-of-words model

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MICROSYSTEM TECHNOLOGIES-MICRO-AND NANOSYSTEMS-INFORMATION STORAGE AND PROCESSING SYSTEMS 2021年第4期27卷 1647-1654页

作者： Huang, Ting Ru, Sheng-Rong Zeng, Zhi-Hong Zhang, Long Longyan Univ Sch Math & Informat Engn 1 Dongxiaobei Rd Longyan 364012 Fujian Peoples R China

In this study, we proposed a motion recognition algorithm based on optimized temporal-spatial features and integrating BOW model. Firstly, detect the key points of the input video and then set up multiple 3D patches in the domain space, enhancing the spatiotemporal characteristic of the key points. With that, randomly sample the key point sets and carry out the description by histogram of oriented gradient (HOG) and optical flow histogram (OFH), thereby obtaining the composite descriptor for features. The clustering algorithm will be then applied to set up a visual dictionary and represent the input video samples. In the final step, a classifier is trained and generated by utilizing multi-core SVM;thus, the motion recognition has been completed. The experimental results suggested that the recognition rate and robustness of the proposed algorithm can outperform most existing motion recognition algorithms.

关键词： clustering algorithms

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Parallel and Distributed Implementation of Sine Cosine Algorithm on Apache Spark Platform

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IEEE ACCESS 2021年 9卷 77188-77202页

作者： Alfailakawi, Mohammad Gh. Aljame, Maryam Ahmad, Imtiaz Kuwait Univ Dept Comp Engn Khaldiyah 13060 Kuwait Minist Educ Educ Software Dept Kuwait 12013 Kuwait

The Sine Cosine Algorithm (SCA) has experienced wide spread use in solving optimization problems in many disciplines mainly due to its simplicity and efficiency. However, like many other meta-heuristics, SCA requires considerable amount of compute time when solving large size optimization problems. Therefore, in order to tackle such challenging problems efficiently, this work proposes Spark-SCA, a scalable and parallel implementation of SCA algorithm on Apache Spark distributed framework. Spark-SCA exploits Spark platform native support for iterative algorithms through in-memory computing to speed-up computations when handling large optimization problems. Both the design and implementation details of Spark-SCA are presented herein. The performance of Spark-SCA was compared to standard SCA on different benchmark functions with up to 1,000-dimension as well as three practical engineering design problems. Simulation experiments conducted on Amazon Web Services (AWS) public cloud demonstrated how Spark-SCA outperforms the standard version in terms of solution quality and run time as well as it competitiveness in exploring solution space of complex optimization problems.

关键词： Optimization Sparks Cluster computing clustering algorithms Search problems Standards Machine learning algorithms Apache spark cluster Hadoop meta-heuristics sine cosine algorithm

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