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Deep reinforcement learning for fresh data collection in UAV-assisted IoT networks

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arXiv 2020年

作者： Yi, Mengjie Wang, Xijun Liu, Juan Zhang, Yan Bai, Bo State Key Lab of Integrated Service Networks Information Science Institute Xidian University Xi’an Shaanxi710071 China Science and Technology on Communication Network Laboratory Shijiazhuang Hebei050081 China School of Electronics and Communication Engineering Sun Yat-sen University Guangzhou510006 China Key Laboratory of Wireless Sensor Network & Communication Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences 865 Changning Road Shanghai200050 China School of Electrical Engineering and Computer Science Ningbo University Zhejiang315211 China 2012 Labs Huawei Technologies Co. Ltd. Hong Kong Hong Kong

Due to the flexibility and low operational cost, dispatching unmanned aerial vehicles (UAVs) to collect information from distributed sensors is expected to be a promising solution in Internet of Things (IoT), especially for time-critical applications. How to maintain the information freshness is a challenging issue. In this paper, we investigate the fresh data collection problem in UAV-assisted IoT networks. Particularly, the UAV flies towards the sensors to collect status update packets within a given duration while maintaining a non-negative residual energy. We formulate a Markov Decision Process (MDP) to find the optimal flight trajectory of the UAV and transmission scheduling of the sensors that minimizes the weighted sum of the age of information (AoI). A UAV-assisted data collection algorithm based on deep reinforcement learning (DRL) is further proposed to overcome the curse of dimensionality. Extensive simulation results demonstrate that the proposed DRL-based algorithm can significantly reduce the weighted sum of the AoI compared to other baseline algorithms. Copyright © 2020, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

FAIR for AI: An interdisciplinary and international community building perspective

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arXiv 2022年

作者： Huerta, E.A. Blaiszik, Ben Brinson, L. Catherine Bouchard, Kristofer E. Diaz, Daniel Doglioni, Caterina Duarte, Javier M. Emani, Murali Foster, Ian Fox, Geoffrey Harris, Philip Heinrich, Lukas Jha, Shantenu Katz, Daniel S. Kindratenko, Volodymyr Kirkpatrick, Christine R. Lassila-Perini, Kati Madduri, Ravi K. Neubauer, Mark S. Psomopoulos, Fotis E. Roy, Avik Rübel, Oliver Zhao, Zhizhen Zhu, Ruike Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Department of Computer Science University of Chicago ChicagoIL60637 United States Globus University of Chicago ChicagoIL60637 United States Department of Mechanical Engineering and Materials Science Duke University DurhamNC27708 United States Scientific Data Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Biological Systems & Engineering Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Helen Wills Neuroscience Institute University of California Berkeley BerkeleyCA94720 United States Department of Physics University of California La Jolla San DiegoCA92093 United States Lund University Department of Physics Box 118 Lund221 00 Sweden School of Physics & Astronomy The University of Manchester ManchesterM13 9PL United Kingdom Leadership Computing Facility Argonne National Laboratory LemontIL60439 United States Biocomplexity Institute Department of Computer Science University of Virginia CharlottesvilleVA22904 United States Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Technical University Munich Arcisstraße 21 München80333 Germany Computational Science Initiative Brookhaven National Laboratory UptonNY11973 United States Electrical and Computer Engineering Rutgers The State University of New Jersey PiscatawayNJ08854 United States National Center for Supercomputing Applications University of Illinois Urbana-Champaign UrbanaIL61801 United States Department of Computer Science University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Electrical & Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States School of Information Sciences University of Illinois at Urbana-Champaign UrbanaIL61801 United States San Diego Supercomputer Center University of California La Jolla San DiegoCA

A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles were proposed in 2016 as prerequisites for proper data management and stewardship, with the goal of enabling the reusability of scholarly data. The principles were also meant to apply to other digital assets, at a high level, and over time, the FAIR guiding principles have been reinterpreted or extended to include the software, tools, algorithms, and workflows that produce data. FAIR principles are now being adapted in the context of AI models and datasets. Here, we present the perspectives, vision, and experiences of researchers from different countries, disciplines, and backgrounds who are leading the definition and adoption of FAIR principles in their communities of practice, and discuss outcomes that may result from pursuing and incentivizing FAIR AI research. The material for this report builds on the FAIR for AI Workshop held at Argonne National laboratory on June 7, 2022. Copyright © 2022, The Authors. All rights reserved.

关键词： information management

Query-adaptive hash code ranking for large-scale multi-view visual search

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arXiv 2019年

作者： Liu, Xianglong Huang, Lei Deng, Cheng Lang, Bo Tao, Dacheng State Key Lab of Software Development Environment Beihang University Beijing100191 China School of Electronic Engineering Xidian University Xi an710071 China Centre for Quantum Computation and Intelligent Systems Faculty of Engineering and Information Technology University of Technology at Sydney SydneyNSW2007 Australia

Hash based nearest neighbor search has become attractive in many applications. However, the quantization in hashing usually degenerates the discriminative power when using Hamming distance ranking. Besides, for large-scale visual search, existing hashing methods cannot directly support the efficient search over the data with multiple sources, and while the literature has shown that adaptively incorporating complementary information from diverse sources or views can significantly boost the search performance. To address the problems, this paper proposes a novel and generic approach to building multiple hash tables with multiple views and generating fine-grained ranking results at bitwise and tablewise levels. For each hash table, a query-adaptive bitwise weighting is introduced to alleviate the quantization loss by simultaneously exploiting the quality of hash functions and their complement for nearest neighbor search. From the tablewise aspect, multiple hash tables are built for different data views as a joint index, over which a query-specific rank fusion is proposed to rerank all results from the bitwise ranking by diffusing in a graph. Comprehensive experiments on image search over three well-known benchmarks show that the proposed method achieves up to 17.11% and 20.28% performance gains on single and multiple table search over state-of-the-art methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Nearest neighbor search

Coverage Probability-Constrained Maximum Throughput in UAV-Aided SWIPT Networks

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Coverage Probability-Constrained Maximum Throughput in UAV-A...

IEEE International Conference on Communications Workshops, ICC

作者： Ruihong Jiang Ke Xiong Tong Liu Duohua Wang Zhangdui Zhong School of Computer and Information Technology Beijing Jiaotong University Beijing China Beijing Computing Center Beijing China ZTE Corporation Xi'an China State Key Lab of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China Beijing Engineering Research Center of High-speed Railway Broadband Mobile Communications Beijing Jiaotong University Beijing China

ISBN: (数字)9781728174402

ISBN: (纸本)9781728174419

This paper investigates the coverage provability-constrained throughput in unmanned aerial vehicle (UAV)-assisted simultaneous wireless information and power transfer (SWIPT) networks, where UAVs are used as aerial base stations and their positions are modeled by the 2-dimension Poisson point process (2-D PPP). The ground users (GUs) decode information as well as harvest energy from the transmitted signals from UAVs. Both power splitting (PS) and time switching (TS) architectures are employed at GUs. By using a stochastic geometry approach, the explicit expressions of the information-energy (I-E) coverage probabilities are derived. To describe the optimal deployment density of UAVs, an optimization problem is formulated to maximize the system throughput subject to the I-E coverage probability constraint. By using Karush-Kuhn-Tucker (KKT) conditions, the closed-form solution is derived. Simulation results demonstrate the correctness of our derived analytical results and show that compared with traditional linear EH model, the nonlinear EH model yields significant difference performance behaviors of the system. Moreover, the nonlinear EH model has a greater impact on EH for the system with TS-enabled GU than that with PS-enabled one. With the increment of the outage threshold, the required density of UAVs should be increased and both the throughput and the energy first increase and then decrease.

关键词： Throughput Wireless communication Unmanned aerial vehicles Wireless sensor networks Integrated circuit modeling Analytical models Fading channels

Resource-aware network topology management framework

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arXiv 2020年

作者： Abbasi, Aaqif Afzaal Shamshirband, Shahaboddin Al-qaness, Mohammed A.A. Abbasi, Almas ALJallad, Nashat T. Mosavi, Amir Department of Software Engineering Foundation University Islamabad44000 Pakistan Department for Management of Science and Technology Development Ton Duc Thang University Ho Chi Minh758307 Viet Nam Faculty of Information Technology Ton Duc Thang University Ho Chi Minh758307 Viet Nam School of Computer Science Wuhan University Bayi Road No. 299 Wuhan430072 China Department of Computer Science International Islamic University Islamabad44000 Pakistan School of Computer Science and Technology Wuhan University of Technology Wuhan430079 China School of Computer Science Palestine Technical University Tulkarem44864 Palestine School of the Built Environment Oxford Brookes University No.5 Jack Straws Lane OxfordOX3 0BP United Kingdom Kalman Kando Faculty of Electrical Engineering Obuda University Becsi ut 94-96 Budapest1034 Hungary Institute of Structural Mechanics Bauhaus University Weimar Marienstraße 15 WeimarD-99423 Germany

Cloud infrastructure provides computing services where computing resources can be adjusted on-demand. However, the adoption of cloud infrastructures brings concerns like reliance on the service provider network, reliability, compliance for service level agreements (SLAs), etc. software-defined networking (SDN) is a networking concept that suggests the segregation of a network’s data plane from the control plane. This concept improves networking behavior. In this paper, we present an SDN-enabled resource-aware topology framework. The proposed framework employs SLA compliance, Path Computation Element (PCE) and shares fair loading to achieve better topology features. We also present an evaluation, showcasing the potential of our *** Codes 68T05 Copyright © 2020, The Authors. All rights reserved.

关键词： information management

Strict pattern matching under non-overlapping condition

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Science China(information Sciences) 2017年第1期60卷 5-20页

作者： Youxi WU Cong SHEN He JIANG Xindong WU School of Computer Science and Engineering Hebei University of Technology Hebei Province Key Laboratory of Big Data Calculation School of Computer Science and Technology Tianjin University School of Software Dalian University of Technology School of Computer Science and Information Engineering Hefei University of Technology School of Computing and Informatics University of Louisiana at Lafayette

Pattern matching(or string matching) is an essential task in computer science, especially in sequential pattern mining, since pattern matching methods can be used to calculate the support(or the number of occurrences) of a pattern and then to determine whether the pattern is frequent or not. A state-of-the-art sequential pattern mining with gap constraints(or flexible wildcards) uses the number of non-overlapping occurrences to denote the frequency of a pattern. Non-overlapping means that any two occurrences cannot use the same character of the sequence at the same position of the pattern. In this paper, we investigate strict pattern matching under the non-overlapping condition. We show that the problem is in P at first. Then we propose an algorithm, called NETLAP-Best, which uses Nettree structure. NETLAP-Best transforms the pattern matching problem into a Nettree and iterates to find the rightmost root-leaf path, to prune the useless nodes in the Nettree after removing the rightmost root-leaf path. We show that NETLAP-Best is a complete algorithm and analyse the time and space complexities of the algorithm. Extensive experimental results demonstrate the correctness and efficiency of NETLAP-Best.

关键词： pattern matching sequential pattern mining gap constraint flexible wildcard non-overlapping occurrence Nettree

Dispersion analysis of periodic structures in anisotropic media: Application to liquid crystals

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arXiv 2020年

作者： Alex-Amor, Antonio Palomares-Caballero, Ángel Mesa, Francisco Quevedo-Teruel, Oscar Padilla, Pablo The Departamento de Teoría de la Señal Telemática y Comunicaciones Universidad de Granada Granada18071 Spain The Department of Information Technologies Universidad CEU San Pablo Madrid28003 Spain Department of Applied Physics 1 Escuela Técnica Superior de Ingenieria Informatica Universidad de Sevilla Sevilla41012 Spain The Division for Electromagnetic Engineering School of Electrical Engineering and Computer Science KTH Royal Institute of Technology StockholmSE-100 44 Sweden

This paper presents an efficient method to compute the dispersion diagram of periodic and uniform structures with generic anisotropic media. The method takes advantage of the ability of full-wave commercial simulators to deal with finite structures having anisotropic media. In particular, the proposed method ¡extends the possibilities of commercial eigenmode solvers in the following ways:¡ (i) anisotropic materials with non-diagonal permittivity and permeability tensors can be analyzed;(ii) the attenuation constant can easily be computed in both propagating and stopband regions and lossy materials can be included in the simulation;and (iii) unbounded and radiating structures such as leaky-wave antennas can be treated. The latter feature may be considered the most remarkable, since the structures must be forcefully bounded with electric/magnetic walls in the eigensolvers of most commercial simulators. In this work, the proposed method is particularized for the study of liquid crystals (LCs) in microwave and antenna devices. Thus, the dispersion properties of a great variety of LC-based configurations are analyzed, from canonical structures, such as waveguide and microstrip, to complex reconfigurable phase shifters in ridge gap-waveguide technology and leaky-wave antennas. Our results have been validated with previously reported works in the literature and with commercial software CST and HFSS. Copyright © 2020, The Authors. All rights reserved.

关键词： Liquid crystals

Incident detection based on mobile crowd sensing for smart city

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Journal of computers (Taiwan) 2019年第1期30卷 96-104页

作者： Zhang, Peng Zhang, Zhenjiang Chao, Han-Chieh Department of Electronic and Information Engineering Key Laboratory of Communication and Information Systems Beijing Municipal Commission of Education Beijing Jiaotong University Beijing100044 China Department of Software Engineering Key Laboratory of Communication and Information Systems Beijing Municipal Commission of Education Beijing Jiaotong University Beijing100044 China Department of Electrical Engineering National Dong Hwa University Hualien97401 Taiwan School of Information Science and Engineering Fujian University of Technology Fuzhou350118 China School of Mathematics and Computer Science Wuhan Polytechnic University Wuhan430023 China Department of Computer Science and Information Engineering National Ilan University Yilan County260 Taiwan

In recent years, mobile devices have taken a significant role in improving the quality of people’s life. In order to enhance the usability of those devices, more and more sensors have been built in. Furthermore, the data storage and data processing capacity are becoming larger and larger. To capture more information about the city conditions and make full use of the computing resource, mobile crowd sensing has been proposed. Incident detection is an important component of the safety monitoring system, however, existing incident detection methods are mostly based on computer vision which is vulnerability to environmental impacts. This paper proposes an automatic incident detection model based on Mobile Crowd Sensing to deal with the problem above. The incident detection algorithm in the model is based on the human activity recognition and the crowd density. The simulation results show that this model is feasible and effective. 2019 © computer Society of the Republic of China. All Rights Reserved.

关键词： Environmental impact

iGateLink: A gateway library for linking IoT, edge, fog and cloud computing environments

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arXiv 2019年

作者： Mancini, Riccardo Tuli, Shreshth Cucinotta, Tommaso Buyya, Rajkumar Lab School of Computing and Information System University of Melbourne Australia Scuola Superiore Sant'Anna Pisa Italy Department of Computer Science and Engineering Indian Institute of Technology Delhi India

In recent years, the Internet of Things (IoT) has been growing in popularity, along with the increasingly important role played by IoT gateways, mediating the interactions among a plethora of heterogeneous IoT devices and cloud services. In this paper, we present iGateLink, an open-source Android library easing the development of Android applications acting as a gateway between IoT devices and Edge/Fog/Cloud Computing environments. Thanks to its pluggable design, modules providing connectivity with a number of devices acting as data sources or Fog/Cloud frameworks can be easily reused for different applications. Using iGateLink in two case-studies replicating previous works in the healthcare and image processing domains, the library proved to be effective in adapting to different scenarios and speeding up development of gateway applications, as compared to the use of conventional methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Fog computing