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Bidirectional multi-tier cognitive swarm drone 5G network

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Bidirectional multi-tier cognitive swarm drone 5G network

2020 IEEE INFOCOM Conference on computer Communications Workshops, INFOCOM WKSHPS 2020

作者： Jacob, Sunil Menon, Varun G. Shynu, P.G. Fathima Shemim, K.S. Mahapatra, Bandana Joseph, Saira SCMS Center for Robotics SCMS School of Engineeeirng and Technology Ernakulam India SCMS School of Engineeeirng and Technology Department of Computer Science and Engineering Ernakulam India School of Information Technology and Engineering Vellore Institute of Technology Vellore India University of Bolton Department of Computing and IT Ras Al Khaimah United Arab Emirates Symbiosis Skills and Open University Department of Data Science Kiwale Pune India SCMS School of Engineeeirng and Technology Department of Electronics and Communication Engineering Ernakulam India

ISBN: (纸本)9781728186955

The use of unmanned aerial vehicle (UAV) as a dynamic node in point to point communication of 5G and beyond 5G cellular networks is an emerging technology. It provides a reliable and efficient mode of wireless communication network in the sky. Existing architectures include single tier and multi-tier drone architecture. Major challenges in multi-tier architecture is in establishing an air traffic control system and in efficient resource sharing between the drones. To overcome the challenges associated with the multitier drone and cellular drone networks, a bidirectional multitier cognitive swarm drone network (BMCSDN) is proposed and investigated in terms of spectrum efficiency and allocation of optimized altitude and power for down link transmission. The proposed method uses the cognitive swarm drone (CSD) concept and provides enhancement of capacity and coverage with optimized transmission efficiency. The work also analyses the effect of interference when all CSDs transmit and receive with static and dynamic frequency allocations. © 2020 IEEE.

关键词： 5G mobile communication systems

VHS to HDTV video translation using multi-task adversarial learning

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arXiv 2021年

作者： Luo, Hongming Liao, Guangsen Hou, Xianxu Liu, Bozhi Zhou, Fei Qiu, Guoping College of Electronics and Information Engineering Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China School of Computer Science University of Nottingham Nottingham United Kingdom

There are large amount of valuable video archives in Video Home System (VHS) format. However, due to the analog nature, their quality is often poor. Compared to High-definition television (HDTV), VHS video not only has a dull color appearance but also has a lower resolution and often appears blurry. In this paper, we focus on the problem of translating VHS video to HDTV video and have developed a solution based on a novel unsupervised multi-task adversarial learning model. Inspired by the success of generative adversarial network (GAN) and CycleGAN, we employ cycle consistency loss, adversarial loss and perceptual loss together to learn a translation model. An important innovation of our work is the incorporation of super-resolution model and color transfer model that can solve unsupervised multi-task problem. To our knowledge, this is the first work that dedicated to the study of the relation between VHS and HDTV and the first computational solution to translate VHS to HDTV. We present experimental results to demonstrate the effectiveness of our solution qualitatively and quantitatively. Copyright © 2021, The Authors. All rights reserved.

关键词： Generative adversarial networks

UncertaintyFuseNet: Robust Uncertainty-aware Hierarchical Feature Fusion Model with Ensemble Monte Carlo Dropout for COVID-19 Detection

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arXiv 2021年

作者： Abdar, Moloud Salari, Soorena Qahremani, Sina Lam, Hak-Keung Karray, Fakhri Hussain, Sadiq Khosravi, Abbas Acharya, U. Rajendra Makarenkov, Vladimir Nahavandi, Saeid Deakin University Australia The Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States The Department of Electrical Engineering Sharif University of Technology Tehran Iran The Centre for Robotics Research Department of Engineering King’s College London London United Kingdom The Centre for Pattern Analysis and Machine Intelligence Department of Electrical and Computer Engineering University of Waterloo WaterlooON Canada The Department of Machine Learning Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates The System Administrator Dibrugarh University Dibrugarh India The Department of Electronics and Computer Engineering Ngee Ann Polytechnic Clementi Singapore The Department of Computer Science University of Quebec in Montreal MontrealQC Canada

The COVID-19 (Coronavirus disease 2019) pandemic has become a major global threat to human health and well-being Thus, the development of computer-aided detection (CAD) systems that are capable to accurately distinguish COVID-19 from other diseases using chest computed tomography (CT) and X-ray data is of immediate priority Such automatic systems are usually based on traditional machine learning or deep learning methods Differently from most of existing studies, which used either CT scan or X-ray images in COVID-19-case classification, we present a simple but efficient deep learning feature fusion model, called UncertaintyFuseNet, which is able to classify accurately large datasets of both of these types of images We argue that the uncertainty of the model’s predictions should be taken into account in the learning process, even though most of existing studies have overlooked it We quantify the prediction uncertainty in our feature fusion model using effective Ensemble MC Dropout (EMCD) technique A comprehensive simulation study has been conducted to compare the results of our new model to the existing approaches, evaluating the performance of competing models in terms of Precision, Recall, F-Measure, Accuracy and ROC curves The obtained results prove the efficiency of our model which provided the prediction accuracy of 99.08% and 96.35% for the considered CT scan and X-ray datasets, respectively Moreover, our UncertaintyFuseNet model was generally robust to noise and performed well with previously unseen data The source code of our implementation is freely available at: https://***/moloud1987/UncertaintyFuseNet-for-COVID-19-Classification. Copyright © 2021, The Authors. All rights reserved.

关键词： Coronavirus

[Retracted] An IoT-Enabled Intelligent and Secure Manufacturing Model Using Blockchain in Hybrid Cloud Communication System

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Security and Communication Networks 2023年第1期2023卷

作者： Sudipto Bhattacharyya Senthil Athithan Souvik Pal Bikramjit Sarkar D. Akila Subrata Chowdhury Karthik Chandran Saravanakumar Gurusamy Department of Computer Science and Engineering Global Institute of Management and Technology Krishnagar Indiagimt-india.in Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Indiakluniversity.in Department of Computer Science and Engineering Sister Nivedita University Kolkata Indiasnuniv.ac.in Sambalpur University Sambalpur Indiasuniv.ac.in Department of Computer Science and Engineering JIS College of Engineering Kalyani Indiajiscollege.ac.in Department of Computer Applications Saveetha College of Liberal Arts and Sciences SIMATS Deemed University Chennai *** Department of Computer Science and Engineering Sreenivasa Institute of Technology and Management Studies Chittoor Andra Pradesh India Department of Robotics and Automation Jyothi Engineering College Thrissur Kerala Indiajecc.ac.in Department of Electrical and Electronics Technology Federal TVET Institute Addis Ababa Ethiopiaftveti.edu.et

The smart manufacturing system can become a linked network with the help of the Internet of Things (IoT). Devices connected to the IoT are susceptible to various attacks and assaults. An effective protection plan is needed to ensure that the billions of IoT nodes are protected from these hazards. The security mechanisms on IoT devices are ineffective due to resource limitations. As a result, the academic community has recently paid attention to the cloud-, fog-, and edge-based IoT systems. A robust cloud provider is in the cloud or fog to perform computationally demanding activities, including safety, data analysis, decision-making process, and monitoring. Hash identities and upgraded Rivest–Shamir–Adleman (RSA) have been used to secure the IoT device’s data. A four-prime integer of 512 bits makes up the proposed security algorithm. A hash signature is used to provide device authentication. An effective clustering method for sensing devices based on the node level, separation from the clusters, remaining energy, and fitness has been presented for long network life. The suggested swarm-based method determines the sensor nodes’ fitness. A deep neural network- (DNN-) based resource scheduling algorithm (DNN-RSM) is meant to reduce the delay and communications overhead for IoT components in the hybrid cloud system. For optimum resource allocation, all queries originating from the cluster head are categorised using DNN based on their storage, processing, and bandwidth needs. The suggested structure delivers better outcomes, particularly regarding energy use, delay, and safety level. The results of the simulation provide credence to the concept that the proposed strategy is superior to the current system. The suggested scheme includes stringent security, decreased energy usage, decreased latency, and efficient resource utilization.

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Supervised anomaly detection via conditional generative adversarial network and ensemble active learning

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arXiv 2021年

作者： Chen, Zhi Duan, Jiang Kang, Li Qiu, Guoping School of Economic Information Engineering Southwestern University of Finance and Economics Chengdu611130 China Guangdong Key Laboratory for Intelligent Information Processing Shenzhen Institute of Artificial Intelligence and Robotics for Society College of Electronics and Information Engineering Shenzhen University Shenzhen518060 China School of Computer Science University of Nottingham NottinghamNG8 1BB United Kingdom

Anomaly detection has wide applications in machine intelligence but is still a difficult unsolved problem. Major challenges include the rarity of labeled anomalies and it is a class highly imbalanced problem. Traditional unsupervised anomaly detectors are suboptimal while supervised models can easily make biased predictions towards normal data. In this paper, we present a new supervised anomaly detector through introducing the novel Ensemble Active Learning Generative Adversarial Network (EAL-GAN). EAL-GAN is a conditional GAN having a unique one generator vs. multiple discriminators architecture where anomaly detection is implemented by an auxiliary classifier of the discriminator. In addition to using the conditional GAN to generate class balanced supplementary training data, an innovative ensemble learning loss function ensuring each discriminator makes up for the deficiencies of the others is designed to overcome the class imbalanced problem, and an active learning algorithm is introduced to significantly reduce the cost of labeling real-world data. We present extensive experimental results to demonstrate that the new anomaly detector consistently outperforms a variety of SOTA methods by significant margins. The codes are available on Github. Copyright © 2021, The Authors. All rights reserved.

关键词： Anomaly detection

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

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SSRN

SSRN 2022年

作者： Jeganathan, Anandpushparaj Dhayabaran, Balaji Jayakody, Dushantha Nalin K. Muthuchidambaranathan, P. Ranchagodage Don, Sanjaya Arunapriya National Institute of Technology Tiruchirappalli India 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 de 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 Padukka10500 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 ina 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： Sensor nodes

Virtual Reality based Telerobotics Framework with Depth Cameras

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Virtual Reality based Telerobotics Framework with Depth Came...

IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Bukeikhan Omarali Brice Denoun Kaspar Althoefer Lorenzo Jamone Maurizio Valle Ildar Farkhatdinov School of Electronic Engineering and Computer Science (Centre for Advanced Robotics @ Queen Mary Centre for Intelligent Sensing) Queen Mary University of London UK Cosmic Lab The Electrical Electronics and Telecommunication Engineering and Naval Architecture University of Genoa Italy

ISBN: (数字)9781728160757

ISBN: (纸本)9781728160764

This work describes a virtual reality (VR) based robot teleoperation framework which relies on scene visualization from depth cameras and implements human-robot and human-scene interaction gestures. We suggest that mounting a camera on a slave robot's end-effector (an in-hand camera) allows the operator to achieve better visualization of the remote scene and improve task performance. We compared experimentally the operator's ability to understand the remote environment in different visualization modes: single external static camera, in-hand camera, in-hand and external static camera, in-hand camera with OctoMap occupancy mapping. The latter option provided the operator with a better understanding of the remote environment whilst requiring relatively small communication bandwidth. Consequently, we propose suitable grasping methods compatible with the VR based teleoperation with the in-hand camera. Video demonstration: https://***/3vZaEykMS_E.

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Subterranean MAV Navigation based on Nonlinear MPC with Collision Avoidance Constraints ⁎

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IFAC-PapersOnLine 2020年第2期53卷 9650-9657页

作者： Sina Sharif Mansouri Christoforos Kanellakis Emil Fresk Björn Lindqvist Dariusz Kominiak Anton Koval Pantelis Sopasakis George Nikolakopoulos Robotics Team Department of Computer Electrical and Space Engineering Luleå University of Technology Luleå SE-97187 Sweden WideFind AB Aurorum 1C Luleå SE-97775 Sweden School of Electronics Electrical Engineering and Computer Science (EEECS) Queen’s University Belfast and Centre for Intelligent Autonomous Manufacturing Systems (i-AMS) United Kingdom

Micro Aerial Vehicles (MAVs) navigation in subterranean environments is gaining attention in the field of aerial robotics, however there are still multiple challenges for collision free navigation in such harsh environments. This article proposes a novel baseline solution for collision free navigation with Nonlinear Model Predictive Control (NMPC). In the proposed method, the MAV is considered as a floating object, where the velocities on the x, y axes and the position on altitude are the references for the NMPC to navigate along the tunnel, while the NMPC avoids the collision by considering kinematics of the obstacles based on measurements from a 2D lidar. Moreover, a novel approach for correcting the heading of the MAV towards the center of the mine tunnel is proposed, while the efficacy of the suggested framework has been evaluated in multiple field trials in an underground mine in Sweden.

关键词： NMPC Collision Avoidance Subterranean MAV Autonomous Tunnel Inspection Mining Aerial robotics

Monetizing edge service in mobile internet ecosystem

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arXiv 2020年

作者： Wang, Zhiyuan Gao, Lin Wang, Tong Luo, Jingjing School of Electronics and Information Engineering Harbin Institute of Technology Shenzhen China Department of Computer Science and Engineering Chinese University of Hong Kong Shatin N.T. Hong Kong Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

In mobile Internet ecosystem, Mobile Users (MUs) purchase wireless data services from Internet Service Provider (ISP) to access to Internet and acquire the interested content services (e.g., online game) from Content Provider (CP). The popularity of intelligent functions (e.g., AI and 3D modeling) increases the computation-intensity of the content services, leading to a growing computation pressure for the MUs’ resource-limited devices. To this end, edge computing service is emerging as a promising approach to alleviate the MUs’ computation pressure while keeping their quality-of-service, via offloading some computation tasks of MUs to edge (computing) servers deployed at the local network edge. Thus, Edge Service Provider (ESP), who deploys the edge servers and offers the edge computing service, becomes an upcoming new stakeholder in the ecosystem. In this work, we study the economic interactions of MUs, ISP, CP, and ESP in the new ecosystem with edge computing service, where MUs can acquire the computation-intensive content services (offered by CP) and offload some computation tasks, together with the necessary raw input data, to edge servers (deployed by ESP) through ISP. We first study the MU’s Joint Content Acquisition and Task Offloading (J-CATO) problem, which aims to maximize his long-term payoff. We derive the off-line solution with crucial insights, based on which we design an online strategy with provable performance. Then, we study the ESP’s edge service monetization problem. We propose a pricing policy that can achieve a constant fraction of the ex post optimal revenue with an extra constant loss for the ESP. Numerical results show that the edge computing service can stimulate the MUs’ content acquisition and improve the payoffs of MUs, ISP, and CP. Copyright © 2020, The Authors. All rights reserved.

关键词： Game theory