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An energy-aware routing method using firefly algorithm for flying ad hoc networks

Scientific Reports 2023年第1期13卷 1-18页

作者： Lansky, Jan Rahmani, Amir Masoud Malik, Mazhar Hussain Yousefpoor, Efat Yousefpoor, Mohammad Sadegh Khan, Muhammad Umair Hosseinzadeh, Mehdi Department of Computer Science and Mathematics Faculty of Economic Studies University of Finance and Administration Prague Czech Republic Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 64002 Douliou Yunlin Taiwan School of Computing and Creative Technologies College of Arts Technology and Environment (CATE) University of the West of England Frenchay Campus Coldharbour Lane Bristol BS16 1QY United Kingdom Department of Computer Engineering Dezful Branch Islamic Azad University Dezful Iran School of Computing Gachon University 1342 Seongnam-daero Sujeong-gu Seongnam-si 13120 South Korea Institute of Research and Development Duy Tan University Da Nang Viet Nam Computer Science University of Human Development Sulaymaniyah Iraq

Flying ad-hoc networks (FANETs) include a large number of drones, which communicate with each other based on an ad hoc model. These networks provide new opportunities for various applications such as military, industrial, and civilian applications. However, FANETs have faced with many challenges like high-speed nodes, low density, and rapid changes in the topology. As a result, routing is a challenging issue in these networks. In this paper, we propose an energy-aware routing scheme in FANETs. This scheme is inspired by the optimized link state routing (OLSR). In the proposed routing scheme, we estimate the connection quality between two flying nodes using a new technique, which utilizes two parameters, including ratio of sent/received of hello packets and connection time. Also, our proposed method selects multipoint relays (MPRs) using the firefly algorithm. It chooses a node with high residual energy, high connection quality, more neighborhood degree, and higher willingness as MPR. Finally, our proposed scheme creates routes between different nodes based on energy and connection quality. Our proposed routing scheme is simulated using the network simulator version 3 (NS3). We compare its simulation results with the greedy optimized link state routing (G-OLSR) and the optimized link state routing (OLSR). These results show that our method outperforms G-OLSR and OLSR in terms of delay, packet delivery rate, throughput, and energy consumption. However, our proposed routing scheme increases slightly routing overhead compared to G-OLSR. © 2023, The Author(s).

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On a network SIS model with opinion dynamics 21st

On a network SIS model with opinion dynamics

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21st IFAC World Congress 2020

作者： Xuan, Weihao Ren, Ruijie Paré, Philip E. Ye, Mengbin Ruf, Sebastian Liu, Ji School of Mechanical Engineering University of Leeds United Kingdom Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden Faculty of Science and Engineering ENTEG University of Groningen Netherlands Center for Complex Networks Research Department of Psychology Northeastern University United States Department of Electrical and Computer Engineering Stony Brook University United States

This paper proposes a network continuous-time susceptible-infected-susceptible (SIS) model coupled with individual opinion dynamics, where the opinion dynamics models an individual's perceived severity of illness or perceived susceptibility. The effects of opinion dynamics on the network SIS model are studied by analyzing the limiting behaviors of the system model, equilibria of the system and their stability. Copyright © 2020 The Authors. This is an open access article under the CC BY-NC-ND license

关键词： Dynamics

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Joint multi-object detection and tracking with camera-LiDAR fusion for autonomous driving

arXiv

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arXiv 2021年

作者： Huang, Kemiao Hao, Qi Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Sifakis Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen518055 China Pazhou Lab Guangzhou510330 China

Multi-object tracking (MOT) with camera-LiDAR fusion demands accurate results of object detection, affinity computation and data association in real time. This paper presents an efficient multi-modal MOT framework with online joint detection and tracking schemes and robust data association for autonomous driving applications. The novelty of this work includes: (1) development of an end-to-end deep neural network for joint object detection and correlation using 2D and 3D measurements;(2) development of a robust affinity computation module to compute occlusion-aware appearance and motion affinities in 3D space;(3) development of a comprehensive data association module for joint optimization among detection confidences, affinities and start-end probabilities. The experiment results on the KITTI tracking benchmark demonstrate the superior performance of the proposed method in terms of both tracking accuracy and processing speed. © 2021, CC BY.

关键词： Autonomous vehicles

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DMGCRN: Dynamic multi-graph convolution recurrent network for traffic forecasting

arXiv

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arXiv 2021年

作者： Qin, Yanjun Fang, Yuchen Luo, Haiyong Zhao, Fang Wang, Chenxing The School of Computer Science National Pilot Software Engineering School Beijing University of Posts and Telecommunications Beijing100876 China The Research Center for Ubiquitous Computing Systems Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

Traffic forecasting is a problem of intelligent transportation systems (ITS) and crucial for individuals and public agencies. Therefore, researches pay great attention to deal with the complex spatio-temporal dependencies of traffic system for accurate forecasting. However, there are two challenges: 1) Most traffic forecasting studies mainly focus on modeling correlations of neighboring sensors and ignore correlations of remote sensors, e.g., business districts with similar spatio-temporal patterns;2) Prior methods which use static adjacency matrix in graph convolutional networks (GCNs) are not enough to reflect the dynamic spatial dependence in traffic system. Moreover, fine-grained methods which use self-attention to model dynamic correlations of all sensors ignore hierarchical information in road networks and have quadratic computational complexity. In this paper, we propose a novel dynamic multi-graph convolution recurrent network (DMGCRN) to tackle above issues, which can model the spatial correlations of distance, the spatial correlations of structure, and the temporal correlations simultaneously. We not only use the distance-based graph to capture spatial information from nodes are close in distance but also construct a novel latent graph which encoded the structure correlations among roads to capture spatial information from nodes are similar in structure. Furthermore, we divide the neighbors of each sensor into coarse-grained regions, and dynamically assign different weights to each region at different times. Meanwhile, we integrate the dynamic multi-graph convolution network into the gated recurrent unit (GRU) to capture temporal dependence. Extensive experiments on three real-world traffic datasets demonstrate that our proposed algorithm outperforms state-of-the-art baselines. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolution

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SMedBERT: A knowledge-enhanced pre-trained language model with structured semantics for medical text mining

arXiv

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arXiv 2021年

作者： Zhang, Taolin Cai, Zerui Wang, Chengyu Qiu, Minghui Yang, Bite He, Xiaofeng School of Software Engineering East China Normal University Alibaba Group Shanghai Key Laboratory of Trsustworthy Computing School of Computer Science and Technology East China Normal University Dxy Shanghai Research Institute for Intelligent Autonomous Systems

Recently, the performance of Pre-trained Language Models (PLMs) has been significantly improved by injecting knowledge facts to enhance their abilities of language understanding. For medical domains, the background knowledge sources are especially useful, due to the massive medical terms and their complicated relations are difficult to understand in text. In this work, we introduce SMedBERT, a medical PLM trained on large-scale medical corpora, incorporating deep structured semantics knowledge from neighbours of linked-entity. In SMedBERT, the mention-neighbour hybrid attention is proposed to learn heterogeneousentity information, which infuses the semantic representations of entity types into the homogeneous neighbouring entity structure. Apart from knowledge integration as external features, we propose to employ the neighbors of linked-entities in the knowledge graph as additional global contexts of text mentions, allowing themto communicate via shared neighbors, thus enrich their semantic representations. Experiments demonstrate that SMedBERT significantly outperforms strong baselines in various knowledge-intensive Chinese medical tasks. It also improves the performance of other tasks such as question answering, questionmatching and natural language inference. Copyright © 2021, The Authors. All rights reserved.

关键词： Knowledge graph

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Sustainable ecosystems : Findings from the NaWerSys workshop series

Sustainable ecosystems : Findings from the NaWerSys workshop...

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2024 Lock-in or log out? Wie digitale Souveranitat gelingt, INFORMATIK 2024

作者： Schoormann, Thorsten Kammler, Friedemann Gembarski, Paul Christoph Hagen, Simon Brinker, Jonas Bollenbach, Jessica Jussen, Ilka Keller, Robert Kortum-Landwehr, Henrik Möller, Frederik Petrik, Dimitri Schweihoff, Julia Stachon, Maleen Winkelmann, Stephanie TU Braunschweig Mühlenpfordtstraße 23 Braunschweig38106 Germany Fraunhofer-Institut für Software- und Systemtechnik ISST Speicherstraße 6 Dortmund44147 Germany UNEOS AG Bismarck-Allee 10 Freiburg im Breisgau79098 Germany Leibniz University Hannover Institute of Product Development An der Universität 1 Garbsen30823 Germany HARTING Technologiegruppe Marienwerderstraße 3 Espelkamp32339 Germany DFKI Osnabrück Hamburger Straße 24 Osnabrück49084 Germany Business & Information Systems Engineering of Fraunhofer FIT University of Bayreuth FIM Research Center for Information Management Alter Postweg 101 Augsburg86159 Germany University of Applied Sciences Kempten Bahnhofstraße 61 Kempten87435 Germany Keplerstr. 17 Stuttgart70174 Germany TU Dortmund University Joseph-von-Fraunhofer-Str. 2-4 Dortmund44227 Germany

ISBN: (纸本)9783885797463

Ecosystems have the promising potential to respond to economic but also environmental and social challenges, such as by enabling circular thinking, establishing fair supply chains, responsible collaborations, and ensuring resilience. In this report, we summarize findings from multiple data sources in the context of the NaWerSys workshop series which was held together with the INFORMATIK conference. The results are informed by experiences collected from full-day paper sessions in 2021, 2022, and 2023, and a world café-based discussion with 15 participants from different backgrounds, including sustainability, engineering, platform governance, product service systems, and artificial intelligence. We structured the results along with four main fields of action concerning the core, the value, the design, and the management of sustainable ecosystems. By jointly elaborating on the status quo and possible research directions, the findings aim to advance our understanding and boost the concept of sustainable ecosystems. © 2024 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Sustainable development goals

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Succinct Link Transformation-based Overlapping Community Detection Framework for Social Network Analysis

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IEEE Access 2025年

作者： Ryu, Seungwoo Lim, Sungsu Yoo, Seungsoo Kim, Sun Yong Agency for Defense Development Defense Rapid Acquisition Technology Research Institute Seoul Korea Republic of Konkuk University Department of Electronics Information & Communication Engineering Seoul Korea Republic of Chungnam National University Department of Computer Science and Engineering Daejeon Korea Republic of Konkuk University Department of Electrical and Electronics Engineering Seoul Korea Republic of

Detecting communities in real-world networks is a challenging and important problem. Numerous algorithms have been proposed in recent years, and significant progress has been made. Overlapping community detection, where individual nodes may belong to multiple communities, has gained attention for its applicability to real-world scenarios. However, accurately analyzing complex networks with high levels of overlap remains difficult. This study presents a succinct link transformation-based framework for overlapping community detection that addresses the challenges of dense and large-scale networks while supporting scalability. The framework transforms the original network into a succinct line graph, or edge-centric graph, by leveraging both node-node and edge-edge (referred to as "link") relationships. Within this transformed graph, edges and links are prioritized using minwise hashing, resulting in an efficient link transformation method. The proposed framework was evaluated against mainstream overlapping community detection algorithms. Experimental results showed superior performance on dense overlapping graphs, with up to 53% improvement in computational efficiency and up to 67% increase in detection accuracy compared to existing methods, demonstrating both scalability and extensibility. This framework enables high-quality overlapping community detection on dense and large-scale real-world networks using various existing algorithms. © 2013 IEEE.

关键词： Graph algorithms

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Reasoning like human: Hierarchical reinforcement learning for knowledge graph reasoning 29

Reasoning like human: Hierarchical reinforcement learning fo...

引用

29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Wan, Guojia Pan, Shirui Gong, Chen Zhou, Chuan Haffari, Gholamreza School of Computer Science Institute of Artificial Intelligence National Engineering Research Center for Multimedia Software Wuhan University China Faculty of Information Technology Monash University Australia School of Computer Science and Engineering Nanjing University of Science and Technology China Department of Computing Hong Kong Polytechnic University Hong Kong Academy of Mathematics and Systems Science Chinese Academy of Sciences China

ISBN: (纸本)9780999241165

Knowledge Graphs typically suffer from incompleteness. A popular approach to knowledge graph completion is to infer missing knowledge by multi-hop reasoning over the information found along other paths connecting a pair of entities. However, multi-hop reasoning is still challenging because the reasoning process usually experiences multiple semantic issue that a relation or an entity has multiple meanings. In order to deal with the situation, we propose a novel Hierarchical Reinforcement Learning framework to learn chains of reasoning from a Knowledge Graph automatically. Our framework is inspired by the hierarchical structure through which a human being handles cognitionally ambiguous cases. The whole reasoning process is decomposed into a hierarchy of two-level Reinforcement Learning policies for encoding historical information and learning structured action space. As a consequence, it is more feasible and natural for dealing with the multiple semantic issue. Experimental results show that our proposed model achieves substantial improvements in ambiguous relation tasks. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Knowledge graph

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Acoustic jet based on acoustic metamaterial

arXiv

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arXiv 2021年

作者： Zhao, Liuxian Horiuchi, Timothy Yu, Miao Institute for Systems Research University of Maryland College ParkMD20742 United States Department of Electrical and Computer Engineering University of Maryland College ParkMD20742 United States Department of Mechanical Engineering University of Maryland College ParkMD20742 United States

In this paper, a novel gradient index (GRIN) acoustic metamaterial is proposed based on the concept of optical modified generalized Luneburg lens (MGLL). With the MGLL, double-foci and high energy density between the two foci can be achieved, which enables the realization of an ultra-long acoustic jet between the two foci. Compared to the generalized Luneburg lens with a single focus, which can only achieve short acoustic jet in the near field or ultra-long acoustic jet in the far field, the proposed MGLL can achieve an acoustic jet extending from the near field to the far field. This capability of the MGLL is theoretically and numerically demonstrated. Furthermore, acoustic metamaterial based MGLL is designed with lattice unit cells having a variable filling ratio. Numerical simulation results show that ultra-long acoustic jets can be achieved with a jet length of up to 30λ, covering both the near field and the far field. Copyright © 2021, The Authors. All rights reserved.

关键词： Lenses

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An Intelligent Age of Information Based Self-Energized Uav-Assisted Wireless Communication System

SSRN

引用

SSRN 2022年

作者： Jeganathan, Anandpushparaj Dhayabaran, Balaji Jayakody, Dushantha Nalin K. Ranchagodage Don, Sanjaya Arunapriya Muthuchidambaranathan, P. Department of Electronics and Communication Engineering National Institute of Technology Tiruchirappalli620015 India Senior Member Technical Staff Baseband Algorithm Development Team Saankhya Labs Private Limited Bengaluru560001 India Centro de Investigaçä em Tecnologias Autónoma TechLab Universidade Autónoma Lisboa Portugal School of Computer Science and Robotics National Research Tomsk Polytechnic University Tomsk Russia Centre for Telecommunication Research School of Engineering Sri Lanka Technological Campus Padukka 10500 Sri Lanka

Internet-of-things (IoT) is an enabling technology in the fourth generation industrial revolution. An unified performance metric named age of information (AoI) is introduced to quantify the freshness of data and its applications. Thus, this paper study the combination of AoI and full-duplex enabled unmanned aerial vehicle (FD-UAV) as a promising solution to improve latency and spectrum efficiency of a wireless powered sensor network. A sensor node (SN), located in a transport infrastructure, harvests energy from radio frequency signals transmitted by the FD-UAV. This uses to transmit real time sensor observations to the data sink via FD-UAV. The SN generates an update after replenished the battery and transmits by using the harvested energy. A closed-form expression for average AoI (AAoI) is derived as a function of time allocated for energy harvesting (EH). The optimal time allocation for EH that maximizes the freshness of data update is identified. Deep learning algorithms such as Long-Short Term Memory (LSTM) is used to predict the AAoI. Simulation results demonstrate the usefulness performance bounds in-terms of freshness of data updates. © 2022, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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