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Energy-Efficient Data-Collection with Rendezvous-Based Approaches in Large-Scale WSNs with Multi-UGV 1

5th International Conference on Artificial Intelligence and Security, ICAIS 2019

作者： Zhang, Jian Tan, Ling Zhuang, Wei College of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China Jiangsu Engineering Center of Network Monitoring Nanjing210044 China

ISBN: (数字)9783030242657

ISBN: (纸本)9783030242640

Prolonging the lifetime of wireless sensor networks (WSNs) is a crucial issue referring to energy conservation or balancing of data collection. In this paper, we propose an optimal algorithm for data collection using UGVs based on clustering by definition of rendezvous nodes (RNs). Due to the considerable large difference of long-distance transmission, the rendezvous-based clustered model for load balancing with multiple UGVs is proposed. Furthermore, considering different delay-tolerant applications, we design a data transmission mode called Two-Orbit Back-Propagation algorithm (TOBPA). In these process, we utilized utility function on energy to realize the optimal energy consumption in the sense of energy transmission model and take energy balancing into account meanwhile. As a result, we analyze the cluster lifetimes in different situations and achieve balance to save significant. © 2019, Springer Nature Switzerland AG.

关键词： Energy efficiency

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BiSample: Bidirectional sampling for handling missing data with local differential privacy

arXiv

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arXiv 2020年

作者： Sun, Lin Ye, Xiaojun Zhao, Jun Lu, Chenhui Yang, Mengmeng Key Laboratory for Information System Security School of Software Tsinghua University Beijing China School of Computer Science and Engineering Nanyang Technological University Singapore

Local differential privacy (LDP) has received much interest recently. In existing protocols with LDP guarantees, a user encodes and perturbs his data locally before sharing it to the aggregator. In common practice, however, users would prefer not to answer all the questions due to different privacy-preserving preferences for different questions, which leads to data missing or the loss of data quality. In this paper, we demonstrate a new approach for addressing the challenges of data perturbation with consideration of users' privacy preferences. Specifically, we first propose BiSample: a bidirectional sampling technique value perturbation in the framework of LDP. Then we combine the BiSample mechanism with users' privacy preferences for missing data perturbation. Theoretical analysis and experiments on a set of datasets confirm the effectiveness of the proposed mechanisms. Copyright © 2020, The Authors. All rights reserved.

关键词： Machine learning

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Minimum Potential Energy of Point Cloud for Robust Global Registration

arXiv

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arXiv 2020年

作者： Wu, Zijie Wang, Yaonan Zhu, Qing Mao, Jianxu Wu, Haotian Feng, Mingtao Mian, Ajmal College of Electrical and Information Engineering Hunan University Changsha410082 China School of Computer Science and Technology Xidian University Xian710071 China Department of Computer Science and Software Engineering University of Western Australia Perth CrawleyWA6009 Australia

—In this paper, we propose a novel minimum gravitational potential energy (MPE)-based algorithm for global point set registration. The feature descriptors extraction algorithms have emerged as the standard approach to align point sets in the past few decades. However, the alignment can be challenging to take effect when the point set suffers from raw point data problems such as noises (Gaussian and Uniformly). Different from the most existing point set registration methods which usually extract the descriptors to find correspondences between point sets, our proposed MPE alignment method is able to handle large scale raw data offset without depending on traditional descriptors extraction, whether for the local or global registration methods. We decompose the solution into a global optimal convex approximation and the fast descent process to a local minimum. For the approximation step, the proposed minimum potential energy (MPE) approach consists of two main steps. Firstly, according to the construction of the force traction operator, we could simply compute the position of the potential energy minimum;Secondly, with respect to the finding of the MPE point, we propose a new theory that employs the two flags to observe the status of the registration procedure. The method of fast descent process to the minimum that we employed is the iterative closest point algorithm;it can achieve the global minimum. We demonstrate the performance of the proposed algorithm on synthetic data as well as on real data. The proposed method outperforms the other global methods in terms of both efficiency, accuracy and noise resistance. Copyright © 2020, The Authors. All rights reserved.

关键词： Geometry

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Structural feature enhanced transformer for fine-grained image recognition

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Pattern Recognition 2026年 169卷

作者： Ying Yu Wei Wei Cairong Zhao Jin Qian Enhong Chen State Key Laboratory of Safety and Resilience of Civil Engineering in Mountain Area East China Jiaotong University Nanchang 330013 Jiangxi China School of Information and Software Engineering East China Jiaotong University Nanchang 330013 Jiangxi China School of Computer Science and Technology Tongji University ShangHai 200092 ShangHai China School of Data Science University of Science and Technology of China Hefei 230026 AnHui China

Existing fine-grained image recognition (FGIR) models mainly rely on high-level semantic features to extract discriminative information, ignoring the potential role of the overall structural information of objects and the structural relationships between key parts. To address this issue, we propose the Structural Feature Enhancement Transformer (SFETrans). SFETrans consists of a visual transformer backbone network responsible for extracting complex semantic features. Additionally, it includes a structural modeling (SM) branch and an amplitude component exchange (ACE) module, both dedicated to enhancing the learning of structural features. The SM branch actively models the structural relationships between key parts of objects and extracts corresponding structural features, while the ACE module guides the model to learn structural information in the phase spectrum by introducing implicit constraints during training. By synergizing the backbone network and the two modules, SFETrans exhibits competitive performance on four benchmark datasets and outperforms other comparison methods in terms of computational efficiency.

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Context-Aware Point-of-Interest Recommendation Algorithm with Interpretability 15th

Context-Aware Point-of-Interest Recommendation Algorithm wit...

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15th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2019

作者： Zhang, Guoming Qi, Lianyong Zhang, Xuyun Xu, Xiaolong Dou, Wanchun State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Information Science and Engineering Qufu Normal University Qufu China Department of Electrical Computer Engineering The University of Auckland Auckland New Zealand School of Computer and Software Nanjing University of Information Science and Technology Nanjing China Health Statistics and Information Center of Jiangsu Province Nanjing China

ISBN: (纸本)9783030301453

With the rapid development of mobile Internet, smart devices, and positioning technologies, location-based social networks (LBSNs) are growing rapidly. In LBSNs, point-of-interest (POI) recommendation is a crucial personalized location service that has become a research hotspot. To address extreme sparsity of user check-in data, a growing line of research exploits spatial-temporal information, social relationship, content information, popularity, and other factors to improve recommendation performance. However, the temporal and spatial transfers of user preferences are seldom mentioned in existing works, and interpretability, which is an important factor to enhance credibility of recommendation result, is overlooked. To cope with these issues, we propose a context-aware POI recommendation framework, which integrates users’ long-term static and time-varying preferences to improve recommendation performance and provide explanations. Experimental results over two real-world LBSN datasets demonstrate that the proposed solution has better performance than other advanced POI recommendation approaches. © 2019, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Location

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Mixed Noise Removal by Residual Learning of Deep CNN

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Journal of New Media 2020年第1期2卷 1-10页

作者： Kang Yang Jielin Jiang Zhaoqing Pan School of Computer and Software Jiangsu Engineering Center of Network MonitoringNanjing University of Information Science and TechnologyNanjing210044China Country Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science and TechnologyNanjing210044China

Due to the huge difference of noise distribution,the result of a mixture of multiple noises becomes very *** normal circumstances,the most common type of mixed noise is to add impulse noise(IN)and then white Gaussian noise(AWGN).From the reduction of cascaded IN and AWGN to the latest sparse representation,a great deal of methods has been proposed to reduce this form of mixed ***,when the mixed noise is very strong,most methods often produce a lot of *** order to solve the above problems,we propose a method based on residual learning for the removal of AWGN-IN noise in this *** training,our model can obtain stable nonlinear mapping from the images with mixed noise to the clean *** a series of experiments under different noise settings,the results show that our method is obviously better than the traditional sparse representation and patch based ***,the time of model training and image denoising is greatly reduced.

关键词： Mixed noise denoising residual learning deep convolutional neural

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Delay-Tolerant Rendezvous-Based Data Collection for Target Tracking in Large-Scale Wireless Sensor Networks with UGV 1

引用

5th International Conference on Artificial Intelligence and Security, ICAIS 2019

作者： Zhang, Jian Xu, Jiang Wang, Tianbao College of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China Jiangsu Engineering Center of Network Monitoring Nanjing210044 China School of Electrical and Information Engineering Jiangsu University Zhenjiang212013 China

ISBN: (数字)9783030242657

ISBN: (纸本)9783030242640

Energy efficiency receives significant attention in wireless sensor networks. In this paper, a UGV is employed as an energy-efficient solution to prolong the network lifetime in target tracking. Data collection strategies for target tracking are investigated including the amount of data and the transmitted distances. For contributed data, the quantization technology is exploited for energy efficiency. Considering the uncertainty of sensing data, we determine a group from intra-cluster members to gather data. And then, we formulate our design a selection optimization problem, maximizing the utilization of the quality of contributed data using information matrix. As a result, we develop an optimization algorithm named rendezvous-based data collection (RDC). Furthermore, two stages of data collection for target tracking are analyzed with a UGV. Simulations verify that the proposed scheme achieves network energy saving as well as energy balance in the framework of target tracking. © 2019, Springer Nature Switzerland AG.

关键词： Target tracking

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Study on Earthquake Prediction Model Based on Traffic Disaster Data

Study on Earthquake Prediction Model Based on Traffic Disast...

引用

IEEE International Conference on software engineering and Service sciences (ICSESS)

作者： Wanjiang Han Yuanlin Gan Shuwen Chen Xiaoxiang Wang School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunication Beijing China School of Information and Communication Engineering Beijing University of Posts and Telecommunication Beijing China

ISBN: (数字)9781728165790

ISBN: (纸本)9781728165806

This paper collects data on the damage to the traffic system caused by earthquakes in China in the past two decades, and uses KNN algorithm, SVM algorithm, logistic regression algorithm, naive Bayes algorithm and decision tree algorithm to train the data, then establish earthquake prediction models. The paper introduces the process of preprocessing, modelling, evaluation, and visualization of disaster data. An earthquake disaster inversion model based on traffic data has been established, which can predict the earthquake intensity based on the relevant data provided by the traffic department. The prediction accuracy is relatively accurate, which is very helpful for earthquake prediction and rescue operations.

关键词： Earthquakes Classification algorithms Data models Roads Logistics Prediction algorithms Regression tree analysis

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Application of the Max-Min Ant System to Solve the Problem of Determining the Route of Vehicles in the Presence of Prohibited Areas

Application of the Max-Min Ant System to Solve the Problem o...

引用

International computer sciences and information Technologies (CSIT)

作者： Hennadii Khudov Oleksandr Makoveichuk Vladyslav Khudov Irina Khizhnyak Nazar Shamrai Dmytro Misiuk Department of Radar Troops Tactic Ivan Kozhedub Kharkiv National Air Force University Kharkiv Ukraine Department of Computer Science and Software Engineering Academician Y. Bugay International Scientific and Technical University Kyiv Ukraine Department of Electronic Computers Kharkiv National University of Radio Electronics Kharkiv Ukraine Department of Military Technical and Information Research Military Institute of Taras Shevchenko National University of Kyiv Kyiv

ISBN: (纸本)9798350334326

The article deals with the problem related to the optimization of the movement of various vehicles and cargo transportation. Analyzed specialized software in the field of transport logistics. Tasks related to the peculiarities of the movement of vehicles in the presence of a large number of intermediate points of the route and prohibited zones are not solved by the existing specialized software. Popular free mapping services also do not provide the ability to find the best path. Exact and inexact algorithms for solving the traveling salesman problem are considered, their advantages and disadvantages are determined. The analysis showed that the most promising methods for determining the optimal route of movement are evolutionary, genetic and swarm intelligence algorithms. Unlike known methods, it is proposed to use a modification of one of the swarm intelligence algorithms, namely ant colony optimization algorithm, to solve the problem of determining the route of vehicles in the presence of prohibited areas. The results of applying MMAS to solve the problem of determining the route of vehicles in the presence of restricted areas for a test example after performing 100 and 400 iterations are presented. A visual comparison of the results of applying MMAS to solve the problem of determining the route of vehicles in the presence of restricted areas with the results of route determination by the brute force method is carried out.

关键词： Visualization software algorithms Transportation Traveling salesman problems Minimax techniques software Particle swarm optimization

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Energy efficiency and hover time optimization in UAV-based hetnets

arXiv

引用

arXiv 2020年

作者： Muntaha, Sidra Tul Hassan, Syed Ali Jung, Haejoon Hossain, M. Shamim School of Electrical Engineering and Computer Science National University of Sciences and Technology Islamabad44000 Pakistan Department of Information and Telecommunication Engineering Incheon National University Yeonsugu Incheon Korea Republic of Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia

In this paper, we investigate the downlink performance of a three-tier heterogeneous network (HetNet). The objective is to enhance the edge capacity of a macro cell by deploying unmanned aerial vehicles (UAVs) as flying base stations and small cells (SCs) for improving the capacity of indoor users in scenarios such as temporary hotspot regions or during disaster situations where the terrestrial network is either insufficient or out of service. UAVs are energy-constrained devices with a limited flight time, therefore, we formulate a two layer optimization scheme, where we first optimize the power consumption of each tier for enhancing the system energy efficiency (EE) under a minimum quality-of-service (QoS) requirement, which is followed by optimizing the average hover time of UAVs. We obtain the solution to these nonlinear constrained optimization problems by first utilizing the Lagrange multipliers method and then implementing a sub-gradient approach for obtaining convergence. The results show that through optimal power allocation, the system EE improves significantly in comparison to when maximum power is allocated to users (ground cellular users or connected vehicles). The hover time optimization results in increased flight time of UAVs thus providing service for longer durations. Copyright © 2020, The Authors. All rights reserved.

关键词： Energy efficiency

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